From 2ed7552b201a8c8487d0a331e597321a8a59db3a Mon Sep 17 00:00:00 2001 From: ce-hao Date: Wed, 1 Sep 2021 13:59:02 +0800 Subject: [PATCH 01/36] 1 --- hw1/cs285/infrastructure/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hw1/cs285/infrastructure/utils.py b/hw1/cs285/infrastructure/utils.py index d894480b..2aee8eba 100644 --- a/hw1/cs285/infrastructure/utils.py +++ b/hw1/cs285/infrastructure/utils.py @@ -39,7 +39,7 @@ def sample_trajectory(env, policy, max_path_length, render=False, render_mode=(' next_obs.append(ob) rewards.append(rew) - # TODO end the rollout if the rollout ended + # TODO end the rollout if the rollout ended, 1 # HINT: rollout can end due to done, or due to max_path_length rollout_done = TODO # HINT: this is either 0 or 1 terminals.append(rollout_done) From 6b0b8106f7259d35bf8689bf855aa954bc45ec07 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Wed, 1 Sep 2021 14:33:37 +0800 Subject: [PATCH 02/36] finish two utils --- hw1/cs285/infrastructure/pytorch_util.py | 21 ++++++++++++++++++++- hw1/cs285/infrastructure/utils.py | 15 +++++++++------ 2 files changed, 29 insertions(+), 7 deletions(-) diff --git a/hw1/cs285/infrastructure/pytorch_util.py b/hw1/cs285/infrastructure/pytorch_util.py index bc7a4081..d02592e3 100644 --- a/hw1/cs285/infrastructure/pytorch_util.py +++ b/hw1/cs285/infrastructure/pytorch_util.py @@ -47,8 +47,27 @@ def build_mlp( # TODO: return a MLP. This should be an instance of nn.Module # Note: nn.Sequential is an instance of nn.Module. - raise NotImplementedError + # raise NotImplementedError + # build the sequential model + model = nn.Sequential() + + # 1. add input layer + model.add_module('input', nn.Linear(input_size, size)) + + # 2. add hidden layers + for n in range(n_layers): + model.add_module('hidden'+str(n), nn.Linear(size, size)) + model.add_module('activation'+str(n), activation) + + # 3. add output layer + model.add_module('output', nn.Linear(size, output_size)) + model.add_module('out activation', output_activation) + + # 4. assert nn.Sequential model is a nn.Module + assert(isinstance(model, nn.Module)) + + return model device = None diff --git a/hw1/cs285/infrastructure/utils.py b/hw1/cs285/infrastructure/utils.py index 2aee8eba..c8bd1811 100644 --- a/hw1/cs285/infrastructure/utils.py +++ b/hw1/cs285/infrastructure/utils.py @@ -27,7 +27,7 @@ def sample_trajectory(env, policy, max_path_length, render=False, render_mode=(' # use the most recent ob to decide what to do obs.append(ob) - ac = TODO # HINT: query the policy's get_action function + ac = policy.get_action(ob) #TODO # HINT: query the policy's get_action function ac = ac[0] acs.append(ac) @@ -39,9 +39,9 @@ def sample_trajectory(env, policy, max_path_length, render=False, render_mode=(' next_obs.append(ob) rewards.append(rew) - # TODO end the rollout if the rollout ended, 1 + # TODO end the rollout if the rollout ended # HINT: rollout can end due to done, or due to max_path_length - rollout_done = TODO # HINT: this is either 0 or 1 + rollout_done = done or (steps >= max_path_length) # HINT: this is either 0 or 1 terminals.append(rollout_done) if rollout_done: @@ -60,8 +60,8 @@ def sample_trajectories(env, policy, min_timesteps_per_batch, max_path_length, r timesteps_this_batch = 0 paths = [] while timesteps_this_batch < min_timesteps_per_batch: - - TODO + path = sample_trajectory(env, policy, max_path_length, render, render_mode) + timesteps_this_batch += get_pathlength(path) return paths, timesteps_this_batch @@ -74,7 +74,10 @@ def sample_n_trajectories(env, policy, ntraj, max_path_length, render=False, ren """ paths = [] - TODO + # TODO + for n in ntraj: + path = sample_trajectory(env, policy, max_path_length, render, render_mode) + paths.append(path) return paths From f1aaee7387d6832a179bea83e5634ce684782a42 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Wed, 1 Sep 2021 14:45:47 +0800 Subject: [PATCH 03/36] finish the replay sample --- hw1/cs285/infrastructure/replay_buffer.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/hw1/cs285/infrastructure/replay_buffer.py b/hw1/cs285/infrastructure/replay_buffer.py index 60148e79..94816797 100644 --- a/hw1/cs285/infrastructure/replay_buffer.py +++ b/hw1/cs285/infrastructure/replay_buffer.py @@ -76,9 +76,14 @@ def sample_random_data(self, batch_size): ## HINT 1: use np.random.permutation to sample random indices ## HINT 2: return corresponding data points from each array (i.e., not different indices from each array) ## HINT 3: look at the sample_recent_data function below - - return TODO, TODO, TODO, TODO, TODO - + shuffled_indices = np.random.permutation(self.obs.shape[0]) + + return sample_recent_data(self.obs[shuffled_indices], batch_size), \ + sample_recent_data(self.acs[shuffled_indices], batch_size), \ + sample_recent_data(self.rews[shuffled_indices], batch_size), \ + sample_recent_data(self.next_obs[shuffled_indices], batch_size), \ + sample_recent_data(self.terminals[shuffled_indices], batch_size) + def sample_recent_data(self, batch_size=1): return ( self.obs[-batch_size:], From deeeef1d83c5fb977de918cd5418110f09123db7 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Wed, 1 Sep 2021 14:48:30 +0800 Subject: [PATCH 04/36] finish the replay sample --- hw1/cs285/infrastructure/replay_buffer.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/hw1/cs285/infrastructure/replay_buffer.py b/hw1/cs285/infrastructure/replay_buffer.py index 94816797..0cfa96d9 100644 --- a/hw1/cs285/infrastructure/replay_buffer.py +++ b/hw1/cs285/infrastructure/replay_buffer.py @@ -76,13 +76,13 @@ def sample_random_data(self, batch_size): ## HINT 1: use np.random.permutation to sample random indices ## HINT 2: return corresponding data points from each array (i.e., not different indices from each array) ## HINT 3: look at the sample_recent_data function below - shuffled_indices = np.random.permutation(self.obs.shape[0]) + random_indices = np.random.permutation(self.obs.shape[0]) - return sample_recent_data(self.obs[shuffled_indices], batch_size), \ - sample_recent_data(self.acs[shuffled_indices], batch_size), \ - sample_recent_data(self.rews[shuffled_indices], batch_size), \ - sample_recent_data(self.next_obs[shuffled_indices], batch_size), \ - sample_recent_data(self.terminals[shuffled_indices], batch_size) + return sample_recent_data(self.obs[random_indices], batch_size), \ + sample_recent_data(self.acs[random_indices], batch_size), \ + sample_recent_data(self.rews[random_indices], batch_size), \ + sample_recent_data(self.next_obs[random_indices], batch_size), \ + sample_recent_data(self.terminals[random_indices], batch_size) def sample_recent_data(self, batch_size=1): return ( From 2834a912d742814a5c0d3f69ef4a97828c7b4957 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Wed, 1 Sep 2021 17:03:56 +0800 Subject: [PATCH 05/36] checked two utils, they are correct; --- hw1/cs285/infrastructure/pytorch_util.py | 6 ++++++ hw1/cs285/infrastructure/rl_trainer.py | 6 ++++-- hw1/cs285/infrastructure/utils.py | 3 ++- hw1/cs285/policies/MLP_policy.py | 7 ++++++- 4 files changed, 18 insertions(+), 4 deletions(-) diff --git a/hw1/cs285/infrastructure/pytorch_util.py b/hw1/cs285/infrastructure/pytorch_util.py index d02592e3..d7cf79fa 100644 --- a/hw1/cs285/infrastructure/pytorch_util.py +++ b/hw1/cs285/infrastructure/pytorch_util.py @@ -69,6 +69,12 @@ def build_mlp( return model + ''' + Another method is to use list = [('name', nn.Linear()), (), ()] , then nn.Sequential(collections.OrderedDict(list)) + Or list = [nn.Linear(), xx ,xx] and nn.Sequential(list) + + ''' + device = None diff --git a/hw1/cs285/infrastructure/rl_trainer.py b/hw1/cs285/infrastructure/rl_trainer.py index bb27972e..5c4f3390 100644 --- a/hw1/cs285/infrastructure/rl_trainer.py +++ b/hw1/cs285/infrastructure/rl_trainer.py @@ -187,12 +187,12 @@ def train_agent(self): # TODO sample some data from the data buffer # HINT1: use the agent's sample function # HINT2: how much data = self.params['train_batch_size'] - ob_batch, ac_batch, re_batch, next_ob_batch, terminal_batch = TODO + ob_batch, ac_batch, re_batch, next_ob_batch, terminal_batch = self.agent.sample(self.params['train_batch_size']) # TODO use the sampled data to train an agent # HINT: use the agent's train function # HINT: keep the agent's training log for debugging - train_log = TODO + train_log = self.agent.train(ob_batch, ac_batch, re_batch, next_ob_batch, terminal_batch) all_logs.append(train_log) return all_logs @@ -202,6 +202,8 @@ def do_relabel_with_expert(self, expert_policy, paths): # TODO relabel collected obsevations (from our policy) with labels from an expert policy # HINT: query the policy (using the get_action function) with paths[i]["observation"] # and replace paths[i]["action"] with these expert labels + for i in paths: + action = self.agent.actor.get_action(paths[i]["observation"]) return paths diff --git a/hw1/cs285/infrastructure/utils.py b/hw1/cs285/infrastructure/utils.py index c8bd1811..ddab958b 100644 --- a/hw1/cs285/infrastructure/utils.py +++ b/hw1/cs285/infrastructure/utils.py @@ -61,6 +61,7 @@ def sample_trajectories(env, policy, min_timesteps_per_batch, max_path_length, r paths = [] while timesteps_this_batch < min_timesteps_per_batch: path = sample_trajectory(env, policy, max_path_length, render, render_mode) + paths.append(path) timesteps_this_batch += get_pathlength(path) return paths, timesteps_this_batch @@ -75,7 +76,7 @@ def sample_n_trajectories(env, policy, ntraj, max_path_length, render=False, ren paths = [] # TODO - for n in ntraj: + for n in range(ntraj): path = sample_trajectory(env, policy, max_path_length, render, render_mode) paths.append(path) diff --git a/hw1/cs285/policies/MLP_policy.py b/hw1/cs285/policies/MLP_policy.py index c8e1fd7d..106ba5da 100644 --- a/hw1/cs285/policies/MLP_policy.py +++ b/hw1/cs285/policies/MLP_policy.py @@ -81,7 +81,12 @@ def get_action(self, obs: np.ndarray) -> np.ndarray: observation = obs[None] # TODO return the action that the policy prescribes - raise NotImplementedError + # raise NotImplementedError + if self.discrete: + action = self.logits_na(obs) + else: + action = self.mean_net(obs) + return action # update/train this policy def update(self, observations, actions, **kwargs): From afaa788e27f32c24f08b45576c837bb318e8cae6 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Wed, 1 Sep 2021 17:08:14 +0800 Subject: [PATCH 06/36] other students use strange ways in replay buffer --- hw1/cs285/infrastructure/replay_buffer.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/hw1/cs285/infrastructure/replay_buffer.py b/hw1/cs285/infrastructure/replay_buffer.py index 0cfa96d9..57241a9e 100644 --- a/hw1/cs285/infrastructure/replay_buffer.py +++ b/hw1/cs285/infrastructure/replay_buffer.py @@ -78,11 +78,17 @@ def sample_random_data(self, batch_size): ## HINT 3: look at the sample_recent_data function below random_indices = np.random.permutation(self.obs.shape[0]) - return sample_recent_data(self.obs[random_indices], batch_size), \ - sample_recent_data(self.acs[random_indices], batch_size), \ - sample_recent_data(self.rews[random_indices], batch_size), \ - sample_recent_data(self.next_obs[random_indices], batch_size), \ - sample_recent_data(self.terminals[random_indices], batch_size) + return(sample_recent_data(self.obs[random_indices], batch_size), + sample_recent_data(self.acs[random_indices], batch_size), + sample_recent_data(self.rews[random_indices], batch_size), + sample_recent_data(self.next_obs[random_indices], batch_size), + sample_recent_data(self.terminals[random_indices], batch_size)) + + ''' + other implementations + idx = np.random.permutation(self.obs.shape[0])[:batch_size] + return self.obs[idx] + ''' def sample_recent_data(self, batch_size=1): return ( From 832f143876af7bff480f1fb339159c587105dbbe Mon Sep 17 00:00:00 2001 From: ce-hao Date: Wed, 1 Sep 2021 17:18:58 +0800 Subject: [PATCH 07/36] ok trainer --- hw1/cs285/infrastructure/rl_trainer.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/hw1/cs285/infrastructure/rl_trainer.py b/hw1/cs285/infrastructure/rl_trainer.py index 5c4f3390..b9c87339 100644 --- a/hw1/cs285/infrastructure/rl_trainer.py +++ b/hw1/cs285/infrastructure/rl_trainer.py @@ -162,11 +162,16 @@ def collect_training_trajectories( # (2) collect `self.params['batch_size']` transitions + if itr == 0: + with open(load_initial_expertdata, 'rb') as f: + loaded_paths = pickle.load(f) + return loaded_paths, 0, None + # TODO collect `batch_size` samples to be used for training # HINT1: use sample_trajectories from utils # HINT2: you want each of these collected rollouts to be of length self.params['ep_len'] print("\nCollecting data to be used for training...") - paths, envsteps_this_batch = TODO + paths, envsteps_this_batch = utils.sample_trajectories(self.env, collect_policy, batch_size, self.params['ep_len']) # collect more rollouts with the same policy, to be saved as videos in tensorboard # note: here, we collect MAX_NVIDEO rollouts, each of length MAX_VIDEO_LEN @@ -202,8 +207,8 @@ def do_relabel_with_expert(self, expert_policy, paths): # TODO relabel collected obsevations (from our policy) with labels from an expert policy # HINT: query the policy (using the get_action function) with paths[i]["observation"] # and replace paths[i]["action"] with these expert labels - for i in paths: - action = self.agent.actor.get_action(paths[i]["observation"]) + for path in paths: + path["action"] = expert_policy.get_action(path["observation"]) return paths From 8f21d4724816aa7816707d8818c7f9ac35d9dea2 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Wed, 1 Sep 2021 17:58:34 +0800 Subject: [PATCH 08/36] mlp policy get action has many implementations --- hw1/cs285/policies/MLP_policy.py | 21 +++++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/hw1/cs285/policies/MLP_policy.py b/hw1/cs285/policies/MLP_policy.py index 106ba5da..43f5d12b 100644 --- a/hw1/cs285/policies/MLP_policy.py +++ b/hw1/cs285/policies/MLP_policy.py @@ -82,11 +82,8 @@ def get_action(self, obs: np.ndarray) -> np.ndarray: # TODO return the action that the policy prescribes # raise NotImplementedError - if self.discrete: - action = self.logits_na(obs) - else: - action = self.mean_net(obs) - return action + action = self.forward(ptu.from_numpy(observation)) + return ptu.to_numpy(action) # update/train this policy def update(self, observations, actions, **kwargs): @@ -98,7 +95,19 @@ def update(self, observations, actions, **kwargs): # return more flexible objects, such as a # `torch.distributions.Distribution` object. It's up to you! def forward(self, observation: torch.FloatTensor) -> Any: - raise NotImplementedError + # raise NotImplementedError + if self.discrete: + action = self.logits_na(observation) + else: + action = self.mean_net(observation) + return action + + ''' + if self.discrete: + return distributions.Categorical(logits=self.logits_na(observation)) + else: + return distributions.Normal(self.mean_net(observation), torch.exp(self.logstd)[None]) + ''' ##################################################### From 9b4a41dd16fc364d71bd5fe13059eab4a1f92d88 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Wed, 1 Sep 2021 18:35:51 +0800 Subject: [PATCH 09/36] this seems ok --- hw1/cs285/policies/MLP_policy.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/hw1/cs285/policies/MLP_policy.py b/hw1/cs285/policies/MLP_policy.py index 43f5d12b..5c8f0143 100644 --- a/hw1/cs285/policies/MLP_policy.py +++ b/hw1/cs285/policies/MLP_policy.py @@ -107,6 +107,8 @@ def forward(self, observation: torch.FloatTensor) -> Any: return distributions.Categorical(logits=self.logits_na(observation)) else: return distributions.Normal(self.mean_net(observation), torch.exp(self.logstd)[None]) + + loss = -action_distribution.log_prob(actions).mean() ''' @@ -123,7 +125,20 @@ def update( adv_n=None, acs_labels_na=None, qvals=None ): # TODO: update the policy and return the loss - loss = TODO + # question, why not run many iterations? + + # convert inputs as torch.tensor, and predict actions + actions = ptu.from_numpy(actions) + predicted_actions = self.forward(ptu.from_numpy(observations)) + + # get loss and train + loss = self.loss(actions, predicted_actions) + + # get gradient and backpropagation + self.optimizer.zero_grad() + loss.backward() + self.optimizer.step() + return { # You can add extra logging information here, but keep this line 'Training Loss': ptu.to_numpy(loss), From 94f5b87e453ed2fff3d04b50087680f3afc06977 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 10:12:46 +0800 Subject: [PATCH 10/36] add pickle --- hw1/cs285/infrastructure/rl_trainer.py | 1 + 1 file changed, 1 insertion(+) diff --git a/hw1/cs285/infrastructure/rl_trainer.py b/hw1/cs285/infrastructure/rl_trainer.py index b9c87339..3d43a837 100644 --- a/hw1/cs285/infrastructure/rl_trainer.py +++ b/hw1/cs285/infrastructure/rl_trainer.py @@ -8,6 +8,7 @@ from cs285.infrastructure import pytorch_util as ptu from cs285.infrastructure.logger import Logger from cs285.infrastructure import utils +import pickle # how many rollouts to save as videos to tensorboard MAX_NVIDEO = 2 From a643df7dd2fbe1639d831928561afdfec9726292 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 11:08:57 +0800 Subject: [PATCH 11/36] add self.sample_recent_data --- hw1/cs285/infrastructure/replay_buffer.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/hw1/cs285/infrastructure/replay_buffer.py b/hw1/cs285/infrastructure/replay_buffer.py index 57241a9e..0aa52d7d 100644 --- a/hw1/cs285/infrastructure/replay_buffer.py +++ b/hw1/cs285/infrastructure/replay_buffer.py @@ -78,11 +78,11 @@ def sample_random_data(self, batch_size): ## HINT 3: look at the sample_recent_data function below random_indices = np.random.permutation(self.obs.shape[0]) - return(sample_recent_data(self.obs[random_indices], batch_size), - sample_recent_data(self.acs[random_indices], batch_size), - sample_recent_data(self.rews[random_indices], batch_size), - sample_recent_data(self.next_obs[random_indices], batch_size), - sample_recent_data(self.terminals[random_indices], batch_size)) + return(self.sample_recent_data(self.obs[random_indices], batch_size), + self.sample_recent_data(self.acs[random_indices], batch_size), + self.sample_recent_data(self.rews[random_indices], batch_size), + self.sample_recent_data(self.next_obs[random_indices], batch_size), + self.sample_recent_data(self.terminals[random_indices], batch_size)) ''' other implementations From c14d11833e5b0da33345f3f61e9fb9572e4b4e49 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 11:12:08 +0800 Subject: [PATCH 12/36] change sample random --- hw1/cs285/infrastructure/replay_buffer.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/hw1/cs285/infrastructure/replay_buffer.py b/hw1/cs285/infrastructure/replay_buffer.py index 0aa52d7d..3f5aea52 100644 --- a/hw1/cs285/infrastructure/replay_buffer.py +++ b/hw1/cs285/infrastructure/replay_buffer.py @@ -78,12 +78,14 @@ def sample_random_data(self, batch_size): ## HINT 3: look at the sample_recent_data function below random_indices = np.random.permutation(self.obs.shape[0]) - return(self.sample_recent_data(self.obs[random_indices], batch_size), - self.sample_recent_data(self.acs[random_indices], batch_size), - self.sample_recent_data(self.rews[random_indices], batch_size), - self.sample_recent_data(self.next_obs[random_indices], batch_size), - self.sample_recent_data(self.terminals[random_indices], batch_size)) - + self.obs = self.obs[random_indices] + self.acs = self.acs[random_indices] + self.rews = self.rews[random_indices] + self.next_obs = self.next_obs[random_indices] + self.terminals = self.terminals[random_indices] + + return self.sample_recent_data(batch_size + ''' other implementations idx = np.random.permutation(self.obs.shape[0])[:batch_size] From 12b030232cd3cc21ffbaf6c70a62943479b6251d Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 11:13:01 +0800 Subject: [PATCH 13/36] change sample random --- hw1/cs285/infrastructure/replay_buffer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hw1/cs285/infrastructure/replay_buffer.py b/hw1/cs285/infrastructure/replay_buffer.py index 3f5aea52..d8cd100c 100644 --- a/hw1/cs285/infrastructure/replay_buffer.py +++ b/hw1/cs285/infrastructure/replay_buffer.py @@ -84,7 +84,7 @@ def sample_random_data(self, batch_size): self.next_obs = self.next_obs[random_indices] self.terminals = self.terminals[random_indices] - return self.sample_recent_data(batch_size + return self.sample_recent_data(batch_size) ''' other implementations From 5b4ad5592e5504551bf06d5647ecf3bd1fadd17f Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 11:14:13 +0800 Subject: [PATCH 14/36] add reset --- hw1/cs285/infrastructure/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hw1/cs285/infrastructure/utils.py b/hw1/cs285/infrastructure/utils.py index ddab958b..d26e2889 100644 --- a/hw1/cs285/infrastructure/utils.py +++ b/hw1/cs285/infrastructure/utils.py @@ -7,7 +7,7 @@ def sample_trajectory(env, policy, max_path_length, render=False, render_mode=('rgb_array')): # initialize env for the beginning of a new rollout - ob = TODO # HINT: should be the output of resetting the env + ob = env.reset() # HINT: should be the output of resetting the env # init vars obs, acs, rewards, next_obs, terminals, image_obs = [], [], [], [], [], [] From 523a3d848025cbb8a7e4a83f3ebad0b4021751bb Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 12:26:31 +0800 Subject: [PATCH 15/36] this tried to imitate human --- hw1/cs285/policies/MLP_policy.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/hw1/cs285/policies/MLP_policy.py b/hw1/cs285/policies/MLP_policy.py index 5c8f0143..f9be35cd 100644 --- a/hw1/cs285/policies/MLP_policy.py +++ b/hw1/cs285/policies/MLP_policy.py @@ -129,10 +129,11 @@ def update( # convert inputs as torch.tensor, and predict actions actions = ptu.from_numpy(actions) - predicted_actions = self.forward(ptu.from_numpy(observations)) + # predicted_actions = self.forward(ptu.from_numpy(observations)) + actions_label = self.get_action(observations) # get loss and train - loss = self.loss(actions, predicted_actions) + loss = self.loss(actions, actions_label) # get gradient and backpropagation self.optimizer.zero_grad() From 0ebfc4cb953e5865943c6ff04a5759090befbf94 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 12:29:22 +0800 Subject: [PATCH 16/36] action --- hw1/cs285/policies/MLP_policy.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/hw1/cs285/policies/MLP_policy.py b/hw1/cs285/policies/MLP_policy.py index f9be35cd..5c1147ab 100644 --- a/hw1/cs285/policies/MLP_policy.py +++ b/hw1/cs285/policies/MLP_policy.py @@ -130,10 +130,10 @@ def update( # convert inputs as torch.tensor, and predict actions actions = ptu.from_numpy(actions) # predicted_actions = self.forward(ptu.from_numpy(observations)) - actions_label = self.get_action(observations) + predicted_actions = self.get_action(observations) # get loss and train - loss = self.loss(actions, actions_label) + loss = self.loss(actions, predicted_actions) # get gradient and backpropagation self.optimizer.zero_grad() From 3414ea375fdc17283c27a48438be3bbd94fed78b Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 12:31:28 +0800 Subject: [PATCH 17/36] action --- hw1/cs285/policies/MLP_policy.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hw1/cs285/policies/MLP_policy.py b/hw1/cs285/policies/MLP_policy.py index 5c1147ab..33348dbe 100644 --- a/hw1/cs285/policies/MLP_policy.py +++ b/hw1/cs285/policies/MLP_policy.py @@ -130,7 +130,7 @@ def update( # convert inputs as torch.tensor, and predict actions actions = ptu.from_numpy(actions) # predicted_actions = self.forward(ptu.from_numpy(observations)) - predicted_actions = self.get_action(observations) + predicted_actions = self.get_action(ptu.from_numpy(observations)) # get loss and train loss = self.loss(actions, predicted_actions) From 1267a9955da5373cfa578449f79c44490a6052a8 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 12:31:54 +0800 Subject: [PATCH 18/36] action --- hw1/cs285/policies/MLP_policy.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/hw1/cs285/policies/MLP_policy.py b/hw1/cs285/policies/MLP_policy.py index 33348dbe..687b55b7 100644 --- a/hw1/cs285/policies/MLP_policy.py +++ b/hw1/cs285/policies/MLP_policy.py @@ -129,8 +129,8 @@ def update( # convert inputs as torch.tensor, and predict actions actions = ptu.from_numpy(actions) - # predicted_actions = self.forward(ptu.from_numpy(observations)) - predicted_actions = self.get_action(ptu.from_numpy(observations)) + predicted_actions = self.forward(ptu.from_numpy(observations)) + # predicted_actions = self.get_action(ptu.from_numpy(observations)) # get loss and train loss = self.loss(actions, predicted_actions) From 46ee8498bbba35224ddd3e130cf0e4380a6374e4 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 13:03:39 +0800 Subject: [PATCH 19/36] 1 --- hw1/cs285/infrastructure/replay_buffer.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/hw1/cs285/infrastructure/replay_buffer.py b/hw1/cs285/infrastructure/replay_buffer.py index d8cd100c..536422ee 100644 --- a/hw1/cs285/infrastructure/replay_buffer.py +++ b/hw1/cs285/infrastructure/replay_buffer.py @@ -76,15 +76,18 @@ def sample_random_data(self, batch_size): ## HINT 1: use np.random.permutation to sample random indices ## HINT 2: return corresponding data points from each array (i.e., not different indices from each array) ## HINT 3: look at the sample_recent_data function below - random_indices = np.random.permutation(self.obs.shape[0]) - self.obs = self.obs[random_indices] - self.acs = self.acs[random_indices] - self.rews = self.rews[random_indices] - self.next_obs = self.next_obs[random_indices] - self.terminals = self.terminals[random_indices] - return self.sample_recent_data(batch_size) + # random_indices = np.random.permutation(self.obs.shape[0]) + # self.obs = self.obs[random_indices] + # self.acs = self.acs[random_indices] + # self.rews = self.rews[random_indices] + # self.next_obs = self.next_obs[random_indices] + # self.terminals = self.terminals[random_indices] + # return self.sample_recent_data(batch_size) + + indices = np.random.permutation(self.obs.shape[0])[:batch_size] + return self.obs[idx], self.acs[idx], self.rews[idx], self.next_obs[idx], self.terminals[idx] ''' other implementations From e5b9069fcdc47aee6cfffdfcda1d870968daed70 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 13:04:36 +0800 Subject: [PATCH 20/36] 1 --- hw1/cs285/infrastructure/replay_buffer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hw1/cs285/infrastructure/replay_buffer.py b/hw1/cs285/infrastructure/replay_buffer.py index 536422ee..6312701f 100644 --- a/hw1/cs285/infrastructure/replay_buffer.py +++ b/hw1/cs285/infrastructure/replay_buffer.py @@ -86,7 +86,7 @@ def sample_random_data(self, batch_size): # self.terminals = self.terminals[random_indices] # return self.sample_recent_data(batch_size) - indices = np.random.permutation(self.obs.shape[0])[:batch_size] + idx = np.random.permutation(self.obs.shape[0])[:batch_size] return self.obs[idx], self.acs[idx], self.rews[idx], self.next_obs[idx], self.terminals[idx] ''' From 4d3fb5f418d7ebb536345f98b7f5816662c6f6f0 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 13:20:33 +0800 Subject: [PATCH 21/36] 1 --- hw1/cs285/infrastructure/replay_buffer.py | 27 +++++++++-------------- 1 file changed, 11 insertions(+), 16 deletions(-) diff --git a/hw1/cs285/infrastructure/replay_buffer.py b/hw1/cs285/infrastructure/replay_buffer.py index 6312701f..fde1f4eb 100644 --- a/hw1/cs285/infrastructure/replay_buffer.py +++ b/hw1/cs285/infrastructure/replay_buffer.py @@ -77,23 +77,18 @@ def sample_random_data(self, batch_size): ## HINT 2: return corresponding data points from each array (i.e., not different indices from each array) ## HINT 3: look at the sample_recent_data function below - - # random_indices = np.random.permutation(self.obs.shape[0]) - # self.obs = self.obs[random_indices] - # self.acs = self.acs[random_indices] - # self.rews = self.rews[random_indices] - # self.next_obs = self.next_obs[random_indices] - # self.terminals = self.terminals[random_indices] - # return self.sample_recent_data(batch_size) - - idx = np.random.permutation(self.obs.shape[0])[:batch_size] - return self.obs[idx], self.acs[idx], self.rews[idx], self.next_obs[idx], self.terminals[idx] + random_indices = np.random.permutation(self.obs.shape[0]) + self.obs = self.obs[random_indices] + self.acs = self.acs[random_indices] + self.rews = self.rews[random_indices] + self.next_obs = self.next_obs[random_indices] + self.terminals = self.terminals[random_indices] + return self.sample_recent_data(batch_size) + + # idx = np.random.permutation(self.obs.shape[0])[:batch_size] + # return self.obs[idx], self.acs[idx], self.rews[idx], self.next_obs[idx], self.terminals[idx] - ''' - other implementations - idx = np.random.permutation(self.obs.shape[0])[:batch_size] - return self.obs[idx] - ''' + def sample_recent_data(self, batch_size=1): return ( From 46825b298e6a7607e3c7589f9f2d93b303556263 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 14:23:27 +0800 Subject: [PATCH 22/36] start the plot frame --- hw1/report_plots.py | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) create mode 100644 hw1/report_plots.py diff --git a/hw1/report_plots.py b/hw1/report_plots.py new file mode 100644 index 00000000..9d8a5fbc --- /dev/null +++ b/hw1/report_plots.py @@ -0,0 +1,24 @@ +import matplotlib.pyplot as plt +import seaborn as sns + +import numpy as np +import pandas as pd + +# manually add data of Ant and Halfcheeta + + +no_iter = np.arange(10) +mean = [] +std = [] +mean_expert = np.ones(10) * 1 +mean_bc = np.ones(10) * 1 + +sns.set_theme(style="darkgrid") + +# Load an example dataset with long-form data +fmri = sns.load_dataset("fmri") + +# Plot the responses for different events and regions +sns.lineplot(x="timepoint", y="signal", + hue="region", style="event", + data=fmri) \ No newline at end of file From 9960dc904c0e8c60ee1bf62f9a86f40f0f806d1a Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 16:11:31 +0800 Subject: [PATCH 23/36] the plot is ok --- hw1/report_plots.py | 43 +++++++++++++++++++++++++++++++------------ 1 file changed, 31 insertions(+), 12 deletions(-) diff --git a/hw1/report_plots.py b/hw1/report_plots.py index 9d8a5fbc..be5d0b92 100644 --- a/hw1/report_plots.py +++ b/hw1/report_plots.py @@ -3,22 +3,41 @@ import numpy as np import pandas as pd +import os # manually add data of Ant and Halfcheeta -no_iter = np.arange(10) -mean = [] -std = [] -mean_expert = np.ones(10) * 1 -mean_bc = np.ones(10) * 1 +def plot_mean_std(ax, iterations, mean, std, mean_exp, mean_bc): + # style = "darkgrid" + style = "whitegrid" + sns.set_theme(style=style) # background color -sns.set_theme(style="darkgrid") + ax.plot(iterations, mean_expert, 'r', label='expert') + ax.plot(iterations, mean_bc, 'g', label='naive bc') + ax.plot(iterations, mean, '-s', label='mean DAgger') + ax.fill_between(iterations, mean-std, mean+std, alpha=0.2) + -# Load an example dataset with long-form data -fmri = sns.load_dataset("fmri") -# Plot the responses for different events and regions -sns.lineplot(x="timepoint", y="signal", - hue="region", style="event", - data=fmri) \ No newline at end of file +if __name__ == "__main__": + no_iter = np.arange(10) + mean = np.linspace(0.9, 1.1, 10) + std = np.linspace(0, 0.2, 10) + + mean_expert = np.ones(10) * 1.3 + mean_bc = np.ones(10) * 0.9 + + + fig, ax = plt.subplots(1,1) + plot_mean_std(ax, no_iter, mean, std, mean_expert, mean_bc) + + ax.legend() + ax.set_xlabel('Iterations') + ax.set_ylabel('Return') + ax.set_title('return of exp') + + exp_dir = 'plots/' + if not os.path.exists(exp_dir): + os.makedirs(exp_dir) + plt.savefig(fname=exp_dir + 'name' + '.svg', format='svg') \ No newline at end of file From 222ffe9445bc64977a9b39a32682a6418c0ff8f9 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 16:15:15 +0800 Subject: [PATCH 24/36] the plot is ok --- hw1/report_plots.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/hw1/report_plots.py b/hw1/report_plots.py index be5d0b92..4c672d4c 100644 --- a/hw1/report_plots.py +++ b/hw1/report_plots.py @@ -9,14 +9,12 @@ def plot_mean_std(ax, iterations, mean, std, mean_exp, mean_bc): - # style = "darkgrid" - style = "whitegrid" - sns.set_theme(style=style) # background color + ax.plot(iterations, mean_expert, 'r', label='expert') ax.plot(iterations, mean_bc, 'g', label='naive bc') - ax.plot(iterations, mean, '-s', label='mean DAgger') - ax.fill_between(iterations, mean-std, mean+std, alpha=0.2) + ax.plot(iterations, mean, 'b-s', label='DAgger mean') + ax.fill_between(iterations, mean-std, mean+std, alpha=0.2, label='DAgger std') @@ -29,7 +27,11 @@ def plot_mean_std(ax, iterations, mean, std, mean_exp, mean_bc): mean_bc = np.ones(10) * 0.9 - fig, ax = plt.subplots(1,1) + # fig, ax = plt.subplots(1,1) + plt.figure(figsize=(10,5)) + style = "whitegrid" + sns.set_theme(style=style) # background color + ax = plt.gca() plot_mean_std(ax, no_iter, mean, std, mean_expert, mean_bc) ax.legend() @@ -40,4 +42,4 @@ def plot_mean_std(ax, iterations, mean, std, mean_exp, mean_bc): exp_dir = 'plots/' if not os.path.exists(exp_dir): os.makedirs(exp_dir) - plt.savefig(fname=exp_dir + 'name' + '.svg', format='svg') \ No newline at end of file + plt.savefig(fname=exp_dir + 'Ant' + '.svg', format='svg') \ No newline at end of file From 6701e7faa989e09f3696f52cac76397f66917bff Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 16:50:51 +0800 Subject: [PATCH 25/36] 1 --- hw1/cs285/infrastructure/rl_trainer.py | 13 ++++++++++++- hw1/report_plots.py | 26 ++++++++++++++++---------- 2 files changed, 28 insertions(+), 11 deletions(-) diff --git a/hw1/cs285/infrastructure/rl_trainer.py b/hw1/cs285/infrastructure/rl_trainer.py index 3d43a837..87e92b6e 100644 --- a/hw1/cs285/infrastructure/rl_trainer.py +++ b/hw1/cs285/infrastructure/rl_trainer.py @@ -124,17 +124,26 @@ def run_training_loop(self, n_iter, collect_policy, eval_policy, training_logs = self.train_agent() # HW1: implement this function below # log/save + mean_list = [] + std_list = [] if self.log_video or self.log_metrics: # perform logging print('\nBeginning logging procedure...') - self.perform_logging( + mean, std = self.perform_logging( itr, paths, eval_policy, train_video_paths, training_logs) + mean_list.append(mean) + std_list.append(std) + if self.params['save_params']: print('\nSaving agent params') self.agent.save('{}/policy_itr_{}.pt'.format(self.params['logdir'], itr)) + import numpy as np + print('mean : ', np.round(mean_list)) + print('std : ', np.round(std_list)) + #################################### #################################### @@ -275,3 +284,5 @@ def perform_logging(self, itr, paths, eval_policy, train_video_paths, training_l print('Done logging...\n\n') self.logger.flush() + + return np.mean(eval_returns), np.std(eval_returns) diff --git a/hw1/report_plots.py b/hw1/report_plots.py index 4c672d4c..a23272f3 100644 --- a/hw1/report_plots.py +++ b/hw1/report_plots.py @@ -18,16 +18,8 @@ def plot_mean_std(ax, iterations, mean, std, mean_exp, mean_bc): -if __name__ == "__main__": - no_iter = np.arange(10) - mean = np.linspace(0.9, 1.1, 10) - std = np.linspace(0, 0.2, 10) +def set_plot_env(iterations, mean, std, mean_exp, mean_bc, exp_name): - mean_expert = np.ones(10) * 1.3 - mean_bc = np.ones(10) * 0.9 - - - # fig, ax = plt.subplots(1,1) plt.figure(figsize=(10,5)) style = "whitegrid" sns.set_theme(style=style) # background color @@ -42,4 +34,18 @@ def plot_mean_std(ax, iterations, mean, std, mean_exp, mean_bc): exp_dir = 'plots/' if not os.path.exists(exp_dir): os.makedirs(exp_dir) - plt.savefig(fname=exp_dir + 'Ant' + '.svg', format='svg') \ No newline at end of file + plt.savefig(fname=exp_dir + exp_name + '.svg', format='svg') + +if __name__ == "__main__": + no_iter = np.arange(10) + mean = np.linspace(0.9, 1.1, 10) + std = np.linspace(0, 0.2, 10) + + mean_expert = np.ones(10) * 1.3 + mean_bc = np.ones(10) * 0.9 + + +class Ant_exp: + iterations = np.arange(20) + mean = + std = From 1af92e4720deacc47e65b494bfca2152fdd45c8a Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 16:54:26 +0800 Subject: [PATCH 26/36] 1 --- hw1/cs285/infrastructure/rl_trainer.py | 10 +++++----- hw1/report_plots.py | 14 +++++++++----- 2 files changed, 14 insertions(+), 10 deletions(-) diff --git a/hw1/cs285/infrastructure/rl_trainer.py b/hw1/cs285/infrastructure/rl_trainer.py index 87e92b6e..73b5facf 100644 --- a/hw1/cs285/infrastructure/rl_trainer.py +++ b/hw1/cs285/infrastructure/rl_trainer.py @@ -88,6 +88,8 @@ def run_training_loop(self, n_iter, collect_policy, eval_policy, self.total_envsteps = 0 self.start_time = time.time() + mean_list = [] + std_list = [] for itr in range(n_iter): print("\n\n********** Iteration %i ************"%itr) @@ -124,8 +126,6 @@ def run_training_loop(self, n_iter, collect_policy, eval_policy, training_logs = self.train_agent() # HW1: implement this function below # log/save - mean_list = [] - std_list = [] if self.log_video or self.log_metrics: # perform logging @@ -140,9 +140,9 @@ def run_training_loop(self, n_iter, collect_policy, eval_policy, print('\nSaving agent params') self.agent.save('{}/policy_itr_{}.pt'.format(self.params['logdir'], itr)) - import numpy as np - print('mean : ', np.round(mean_list)) - print('std : ', np.round(std_list)) + import numpy as np + print('mean : ', np.round(mean_list)) + print('std : ', np.round(std_list)) #################################### #################################### diff --git a/hw1/report_plots.py b/hw1/report_plots.py index a23272f3..d46d76ca 100644 --- a/hw1/report_plots.py +++ b/hw1/report_plots.py @@ -36,6 +36,14 @@ def set_plot_env(iterations, mean, std, mean_exp, mean_bc, exp_name): os.makedirs(exp_dir) plt.savefig(fname=exp_dir + exp_name + '.svg', format='svg') + +class Ant_exp: + mean = [1] + std = [] + mean_bc = mean[0] + mean_expert = mean[-1] + iterations = np.arange(len(mean)) + if __name__ == "__main__": no_iter = np.arange(10) mean = np.linspace(0.9, 1.1, 10) @@ -44,8 +52,4 @@ def set_plot_env(iterations, mean, std, mean_exp, mean_bc, exp_name): mean_expert = np.ones(10) * 1.3 mean_bc = np.ones(10) * 0.9 - -class Ant_exp: - iterations = np.arange(20) - mean = - std = + set_plot_env(no_iter, mean, std, mean_expert, mean_bc, 'Ant') \ No newline at end of file From 6241645ca177bdbd4fc2703626d6c5857a4f1996 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 17:45:00 +0800 Subject: [PATCH 27/36] it can save --- hw1/report_plots.py | 43 ++++++++++++++++++++++++++----------------- 1 file changed, 26 insertions(+), 17 deletions(-) diff --git a/hw1/report_plots.py b/hw1/report_plots.py index d46d76ca..b284960a 100644 --- a/hw1/report_plots.py +++ b/hw1/report_plots.py @@ -8,48 +8,57 @@ # manually add data of Ant and Halfcheeta -def plot_mean_std(ax, iterations, mean, std, mean_exp, mean_bc): - +def plot_mean_std(ax, iterations, mean, std, mean_expert, mean_bc): + mean = np.array(mean) + std = np.array(std) ax.plot(iterations, mean_expert, 'r', label='expert') ax.plot(iterations, mean_bc, 'g', label='naive bc') ax.plot(iterations, mean, 'b-s', label='DAgger mean') ax.fill_between(iterations, mean-std, mean+std, alpha=0.2, label='DAgger std') - -def set_plot_env(iterations, mean, std, mean_exp, mean_bc, exp_name): +def set_plot_env(iterations, mean, std, mean_expert, mean_bc, exp_name): plt.figure(figsize=(10,5)) style = "whitegrid" sns.set_theme(style=style) # background color ax = plt.gca() - plot_mean_std(ax, no_iter, mean, std, mean_expert, mean_bc) + plot_mean_std(ax, iterations, mean, std, mean_expert, mean_bc) ax.legend() ax.set_xlabel('Iterations') ax.set_ylabel('Return') - ax.set_title('return of exp') + ax.set_title('return of exp ' + exp_name) exp_dir = 'plots/' if not os.path.exists(exp_dir): os.makedirs(exp_dir) plt.savefig(fname=exp_dir + exp_name + '.svg', format='svg') +def plot_DAgger(mean, std, exp_name): + iterations = np.arange(len(mean)) + I = np.ones(len(mean)) + mean_bc = mean[0] * I + mean_expert = mean[-1] * I + set_plot_env(iterations, mean, std, mean_expert, mean_bc, exp_name) class Ant_exp: - mean = [1] - std = [] - mean_bc = mean[0] - mean_expert = mean[-1] - iterations = np.arange(len(mean)) + mean = [4274., 4648., 4746., 4619., 4447., 4356., 4731., 4739., 4581., 4834., 4648., 4382., 4750., 4809., 4729., 4718., 4492., 4829., 4676., 4682.] + std = [1128., 53., 85., 103., 854., 1030., 124., 135., 336., 109., 499., 1152., 91., 68., 104., 72., 169., 127., 81., 375.] + if __name__ == "__main__": - no_iter = np.arange(10) - mean = np.linspace(0.9, 1.1, 10) - std = np.linspace(0, 0.2, 10) + # no_iter = np.arange(10) + # mean = np.linspace(0.9, 1.1, 10) + # std = np.linspace(0, 0.2, 10) + + # mean_expert = np.ones(10) * 1.3 + # mean_bc = np.ones(10) * 0.9 + + exp = Ant_exp + mean = exp.mean + std = exp.std - mean_expert = np.ones(10) * 1.3 - mean_bc = np.ones(10) * 0.9 - set_plot_env(no_iter, mean, std, mean_expert, mean_bc, 'Ant') \ No newline at end of file + plot_DAgger(mean, std, 'Ant') \ No newline at end of file From f83e62215e1fcd71d2017a0ef28f48c17fcb7f79 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 17:54:22 +0800 Subject: [PATCH 28/36] 1 --- hw1/cs285/infrastructure/rl_trainer.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/hw1/cs285/infrastructure/rl_trainer.py b/hw1/cs285/infrastructure/rl_trainer.py index 73b5facf..83438e28 100644 --- a/hw1/cs285/infrastructure/rl_trainer.py +++ b/hw1/cs285/infrastructure/rl_trainer.py @@ -130,19 +130,19 @@ def run_training_loop(self, n_iter, collect_policy, eval_policy, # perform logging print('\nBeginning logging procedure...') - mean, std = self.perform_logging( + self.perform_logging( itr, paths, eval_policy, train_video_paths, training_logs) - mean_list.append(mean) - std_list.append(std) + # mean_list.append(mean) + # std_list.append(std) if self.params['save_params']: print('\nSaving agent params') self.agent.save('{}/policy_itr_{}.pt'.format(self.params['logdir'], itr)) - import numpy as np - print('mean : ', np.round(mean_list)) - print('std : ', np.round(std_list)) + # import numpy as np + # print('mean : ', np.round(mean_list)) + # print('std : ', np.round(std_list)) #################################### #################################### @@ -285,4 +285,4 @@ def perform_logging(self, itr, paths, eval_policy, train_video_paths, training_l self.logger.flush() - return np.mean(eval_returns), np.std(eval_returns) + # return np.mean(eval_returns), np.std(eval_returns) From d658381d2e2622b5e934188f9f2cd5b3578ff060 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 18:46:49 +0800 Subject: [PATCH 29/36] copy from colab --- .../repot_plots-checkpoint.ipynb | 35 + hw1/cs285/infrastructure/rl_trainer.py | 14 +- hw1/plots/Ant.svg | 1208 +++++++++++++++++ hw1/repot_plots.ipynb | 48 + 4 files changed, 1298 insertions(+), 7 deletions(-) create mode 100644 hw1/.ipynb_checkpoints/repot_plots-checkpoint.ipynb create mode 100644 hw1/plots/Ant.svg create mode 100644 hw1/repot_plots.ipynb diff --git a/hw1/.ipynb_checkpoints/repot_plots-checkpoint.ipynb b/hw1/.ipynb_checkpoints/repot_plots-checkpoint.ipynb new file mode 100644 index 00000000..99d57121 --- /dev/null +++ b/hw1/.ipynb_checkpoints/repot_plots-checkpoint.ipynb @@ -0,0 +1,35 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "unnecessary-orchestra", + "metadata": {}, + "outputs": [], + "source": [ + "run report_plots.py" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hw1/cs285/infrastructure/rl_trainer.py b/hw1/cs285/infrastructure/rl_trainer.py index 83438e28..73b5facf 100644 --- a/hw1/cs285/infrastructure/rl_trainer.py +++ b/hw1/cs285/infrastructure/rl_trainer.py @@ -130,19 +130,19 @@ def run_training_loop(self, n_iter, collect_policy, eval_policy, # perform logging print('\nBeginning logging procedure...') - self.perform_logging( + mean, std = self.perform_logging( itr, paths, eval_policy, train_video_paths, training_logs) - # mean_list.append(mean) - # std_list.append(std) + mean_list.append(mean) + std_list.append(std) if self.params['save_params']: print('\nSaving agent params') self.agent.save('{}/policy_itr_{}.pt'.format(self.params['logdir'], itr)) - # import numpy as np - # print('mean : ', np.round(mean_list)) - # print('std : ', np.round(std_list)) + import numpy as np + print('mean : ', np.round(mean_list)) + print('std : ', np.round(std_list)) #################################### #################################### @@ -285,4 +285,4 @@ def perform_logging(self, itr, paths, eval_policy, train_video_paths, training_l self.logger.flush() - # return np.mean(eval_returns), np.std(eval_returns) + return np.mean(eval_returns), np.std(eval_returns) diff --git a/hw1/plots/Ant.svg b/hw1/plots/Ant.svg new file mode 100644 index 00000000..2a051489 --- /dev/null +++ b/hw1/plots/Ant.svg @@ -0,0 +1,1208 @@ + + + + + + + + + 2021-09-03T09:44:45.657490 + image/svg+xml + + + Matplotlib v3.3.3, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run report_plots.py" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From be8b18e893cae297dda9e7f979cf66ee03719e65 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 18:52:07 +0800 Subject: [PATCH 30/36] start adding more ep_len --- hw1/report_plots.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/hw1/report_plots.py b/hw1/report_plots.py index b284960a..b02cec6a 100644 --- a/hw1/report_plots.py +++ b/hw1/report_plots.py @@ -47,6 +47,9 @@ class Ant_exp: mean = [4274., 4648., 4746., 4619., 4447., 4356., 4731., 4739., 4581., 4834., 4648., 4382., 4750., 4809., 4729., 4718., 4492., 4829., 4676., 4682.] std = [1128., 53., 85., 103., 854., 1030., 124., 135., 336., 109., 499., 1152., 91., 68., 104., 72., 169., 127., 81., 375.] + ep_len = [100, 300, 500, 800, 1000, 1500, 2000] + mean_len = [384, ] + std_len = [66, ] if __name__ == "__main__": # no_iter = np.arange(10) From 9736f80a18acbe9e8b3bcad6ca5c09c7ee115990 Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 19:24:42 +0800 Subject: [PATCH 31/36] to test the expert policy --- hw1/cs285/infrastructure/rl_trainer.py | 13 ++++++++++++- hw1/report_plots.py | 4 ++-- 2 files changed, 14 insertions(+), 3 deletions(-) diff --git a/hw1/cs285/infrastructure/rl_trainer.py b/hw1/cs285/infrastructure/rl_trainer.py index 73b5facf..ab64f6cb 100644 --- a/hw1/cs285/infrastructure/rl_trainer.py +++ b/hw1/cs285/infrastructure/rl_trainer.py @@ -140,10 +140,11 @@ def run_training_loop(self, n_iter, collect_policy, eval_policy, print('\nSaving agent params') self.agent.save('{}/policy_itr_{}.pt'.format(self.params['logdir'], itr)) - import numpy as np print('mean : ', np.round(mean_list)) print('std : ', np.round(std_list)) + self.perform_expert(expert_policy) + #################################### #################################### @@ -286,3 +287,13 @@ def perform_logging(self, itr, paths, eval_policy, train_video_paths, training_l self.logger.flush() return np.mean(eval_returns), np.std(eval_returns) + + def perform_expert(self, expert_policy): + print("\nCollecting data for EXPERT !! eval...") + eval_paths, eval_envsteps_this_batch = utils.sample_trajectories(self.env, expert_policy, self.params['eval_batch_size'], self.params['ep_len']) + mean_exp = np.mean(eval_returns) + std_exp = np.std(eval_returns) + mean_exp_max = np.max(eval_returns) + mean_exp_min = np.min(eval_returns) + + print('mean exp: {}, std exp: {}, mean max: {}, mean min: {}'.format(np.round(mean_exp), np.round(std_exp), np.round(mean_exp_max), np.round(mean_exp_min) )) diff --git a/hw1/report_plots.py b/hw1/report_plots.py index b02cec6a..d627312c 100644 --- a/hw1/report_plots.py +++ b/hw1/report_plots.py @@ -48,8 +48,8 @@ class Ant_exp: std = [1128., 53., 85., 103., 854., 1030., 124., 135., 336., 109., 499., 1152., 91., 68., 104., 72., 169., 127., 81., 375.] ep_len = [100, 300, 500, 800, 1000, 1500, 2000] - mean_len = [384, ] - std_len = [66, ] + mean_len = [] + std_len = [] if __name__ == "__main__": # no_iter = np.arange(10) From 0f2bf5eeaf327cf03b47b53d05cc6d21b7cd77cf Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 19:26:20 +0800 Subject: [PATCH 32/36] last forgot eval reward --- hw1/cs285/infrastructure/rl_trainer.py | 1 + 1 file changed, 1 insertion(+) diff --git a/hw1/cs285/infrastructure/rl_trainer.py b/hw1/cs285/infrastructure/rl_trainer.py index ab64f6cb..57de0369 100644 --- a/hw1/cs285/infrastructure/rl_trainer.py +++ b/hw1/cs285/infrastructure/rl_trainer.py @@ -291,6 +291,7 @@ def perform_logging(self, itr, paths, eval_policy, train_video_paths, training_l def perform_expert(self, expert_policy): print("\nCollecting data for EXPERT !! eval...") eval_paths, eval_envsteps_this_batch = utils.sample_trajectories(self.env, expert_policy, self.params['eval_batch_size'], self.params['ep_len']) + eval_returns = [eval_path["reward"].sum() for eval_path in eval_paths] mean_exp = np.mean(eval_returns) std_exp = np.std(eval_returns) mean_exp_max = np.max(eval_returns) From e3ebe558d16528744406c1a602edd9dfc0ec481d Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 20:05:22 +0800 Subject: [PATCH 33/36] this one is good for ploting --- hw1/cs285/infrastructure/rl_trainer.py | 2 +- hw1/plots/Ant.png | Bin 0 -> 25510 bytes hw1/plots/Ant.svg | 237 +++++++++++++------------ hw1/plots/Hooper.png | Bin 0 -> 26445 bytes hw1/report_plots.py | 32 +++- hw1/repot_plots.ipynb | 2 +- 6 files changed, 147 insertions(+), 126 deletions(-) create mode 100644 hw1/plots/Ant.png 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mean_bc) - ax.legend() + ax.legend(loc='center right') ax.set_xlabel('Iterations') ax.set_ylabel('Return') - ax.set_title('return of exp ' + exp_name) + ax.set_title('return of ' + exp_name +' experiment') + ax.set_xlim([-0.5,10]) exp_dir = 'plots/' if not os.path.exists(exp_dir): os.makedirs(exp_dir) - plt.savefig(fname=exp_dir + exp_name + '.svg', format='svg') + plt.savefig(fname=exp_dir + exp_name + '.png', format='png') -def plot_DAgger(mean, std, exp_name): +def plot_DAgger(mean, std, mean_expert, exp_name): iterations = np.arange(len(mean)) I = np.ones(len(mean)) mean_bc = mean[0] * I - mean_expert = mean[-1] * I + mean_expert = mean_expert * I set_plot_env(iterations, mean, std, mean_expert, mean_bc, exp_name) class Ant_exp: - mean = [4274., 4648., 4746., 4619., 4447., 4356., 4731., 4739., 4581., 4834., 4648., 4382., 4750., 4809., 4729., 4718., 4492., 4829., 4676., 4682.] - std = [1128., 53., 85., 103., 854., 1030., 124., 135., 336., 109., 499., 1152., 91., 68., 104., 72., 169., 127., 81., 375.] + # mean = [4274., 4648., 4746., 4619., 4447., 4356., 4731., 4739., 4581., 4834., 4648., 4382., 4750., 4809., 4729., 4718., 4492., 4829., 4676., 4682.] + # std = [1128., 53., 85., 103., 854., 1030., 124., 135., 336., 109., 499., 1152., 91., 68., 104., 72., 169., 127., 81., 375.] + mean = [4274., 4648., 4746., 4619., 4447., 4356., 4731., 4739., 4581., 4834.] + std = [1128., 53., 85., 103., 854., 1030., 124., 135., 336., 109.] ep_len = [100, 300, 500, 800, 1000, 1500, 2000] + mean_expert = 4710 mean_len = [] std_len = [] +class Hooper_exp: + + mean = [ 523., 1700., 2453., 3763., 3778., 3790., 3388., 3771., 3537., 3788.] + std = [ 75., 607., 725., 4., 3., 4., 671., 3., 160., 3.] + mean_expert = 3779 + + if __name__ == "__main__": # no_iter = np.arange(10) # mean = np.linspace(0.9, 1.1, 10) @@ -59,9 +70,12 @@ class Ant_exp: # mean_expert = np.ones(10) * 1.3 # mean_bc = np.ones(10) * 0.9 - exp = Ant_exp + # exp = Ant_exp + exp = Hooper_exp + mean = exp.mean std = exp.std + mean_expert = exp.mean_expert - plot_DAgger(mean, std, 'Ant') \ No newline at end of file + plot_DAgger(mean, std, mean_expert, 'Hooper') \ No newline at end of file diff --git a/hw1/repot_plots.ipynb b/hw1/repot_plots.ipynb index 4a99c0bd..6474e144 100644 --- a/hw1/repot_plots.ipynb +++ b/hw1/repot_plots.ipynb @@ -8,7 +8,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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zwSCqa`FH3@m_pAsDE=ut&u*GwzB$R}y))F{)v==a_^zkXUSCR|g9@0p6KDy2yKdkS$PbL3ffjhDE$^n4up zabV4G%{#NmTDPwDbs`1y5RoVcM~>G2@UgJI{~u@jHzhj^YA{}I%VpkmhQW;lH`Q*d J7Asqa{5JuuLyiCd literal 0 HcmV?d00001 diff --git a/hw1/report_plots.py b/hw1/report_plots.py index 73f85b18..b20b5f7e 100644 --- a/hw1/report_plots.py +++ b/hw1/report_plots.py @@ -44,16 +44,41 @@ def plot_DAgger(mean, std, mean_expert, exp_name): mean_expert = mean_expert * I set_plot_env(iterations, mean, std, mean_expert, mean_bc, exp_name) +def plot_changing_ep(ep_len, mean_len, std_len): + plt.figure(figsize=(10,5)) + style = "whitegrid" + sns.set_theme(style=style) # background color + ax = plt.gca() + + mean_len = np.array(mean_len) + std_len = np.array(std_len) + I = np.ones(len(mean_len)) + mean_expert = Ant_exp.mean_expert * I + + ax.plot(ep_len, mean_expert, 'r', label='expert') + plt.plot(ep_len, mean_len, 'b-s', label='bc mean') + plt.fill_between(ep_len, mean_len-std_len, mean_len+std_len, alpha=0.2, label='bc std') + ax.legend(loc='upper left') + ax.set_xlabel('num of traning steps') + ax.set_ylabel('Return') + ax.set_title('return of ' + 'Ant experiments with varying traning steps') + + exp_dir = 'plots/' + if not os.path.exists(exp_dir): + os.makedirs(exp_dir) + plt.savefig(fname=exp_dir + 'varying' + '.png', format='png') + + class Ant_exp: - # mean = [4274., 4648., 4746., 4619., 4447., 4356., 4731., 4739., 4581., 4834., 4648., 4382., 4750., 4809., 4729., 4718., 4492., 4829., 4676., 4682.] - # std = [1128., 53., 85., 103., 854., 1030., 124., 135., 336., 109., 499., 1152., 91., 68., 104., 72., 169., 127., 81., 375.] mean = [4274., 4648., 4746., 4619., 4447., 4356., 4731., 4739., 4581., 4834.] std = [1128., 53., 85., 103., 854., 1030., 124., 135., 336., 109.] - - ep_len = [100, 300, 500, 800, 1000, 1500, 2000] mean_expert = 4710 - mean_len = [] - std_len = [] + + ep_len = [100, 300, 500, 700, 990, 1100, 1400, 1500, 1700] + mean_len = [567, 1505, 3849, 3296, 3774, 3570, 2227, 4236, 4249] + std_len = [7, 1250, 1299, 1604, 1363, 1392, 1740, 1151, 901] + mean_exp_len = [] + std_exp_len = [] class Hooper_exp: @@ -66,16 +91,18 @@ class Hooper_exp: # no_iter = np.arange(10) # mean = np.linspace(0.9, 1.1, 10) # std = np.linspace(0, 0.2, 10) - # mean_expert = np.ones(10) * 1.3 # mean_bc = np.ones(10) * 0.9 - # exp = Ant_exp - exp = Hooper_exp - - mean = exp.mean - std = exp.std - mean_expert = exp.mean_expert + exp = Ant_exp + # exp = Hooper_exp + # mean = exp.mean + # std = exp.std + # mean_expert = exp.mean_expert + # plot_DAgger(mean, std, mean_expert, 'Hooper') - plot_DAgger(mean, std, mean_expert, 'Hooper') \ No newline at end of file + mean_len = exp.mean_len + std_len = exp.std_len + ep_len = exp.ep_len + plot_changing_ep(ep_len, mean_len, std_len) \ No newline at end of file diff --git a/hw1/repot_plots.ipynb b/hw1/repot_plots.ipynb index 6474e144..26d85e55 100644 --- a/hw1/repot_plots.ipynb +++ b/hw1/repot_plots.ipynb @@ -8,7 +8,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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I4nKJYRjU1dUVTEunEEIIkSskkOtBgriRIc+rEEIIkX0SyAkhhBBC5CkJ5ESvNm7cyMMPP+x2MYQQQgjRDwnkxBFSqRQbN27k0UcfdbsoQgghhOiHZK3muHXr1nHDDTcQDocB+PznP8+bb77Jxo0bufHGG4lGo3zwgx/kq1/9KqeffjpnnnkmF1xwAatXr+add97h4x//OFdffTUA27Zt44c//CEtLS0kk0muvfZaLrvsMgCUUnz1q1/l2WefZc6cOTz55JOEQiEuueQSjj/+eL71rW+59hwIIYTIbR3hBBYWPq+Jz+vB65F2otEigVw/Dj71DAeefGpEzj3+vWcy7swz+n1Me3s73/3ud7nlllsYN24cBw8e5PLLL+fBBx/kS1/6ErfffjtvvfUWp512GqeffnrXcYcOHeLOO+9k165dXHXVVRx33HHMmjWLr3zlK/zkJz9h5syZhEIhLrvsMo499lhmzpwJQCaT4fbbbwdg/vz5PPPMM/ziF78YkfoLIYQoDMlUhkNtUdJpC8Owk9u8XoOAz4Pf58Hn8eDzGvh9Hkl8GwESyOWwNWvWsGfPHj71qU91bTMMg927d/OTn/yESy65hPr6eu66667Djrv88ssBqKur44wzzuDVV1/F6/WydetWvvSlL3U9LplMsm3btq5A7v3vf/8o1EoIIUQh6YgkSKctACzLnjs0kbBIJDJAEgDTMDBM8PtMO8DzevA6rXc+r7TeDYcEcv0Yd+YZR201G0mWZaGU4s477zxi37p16zBNk7a2NmKxGOXl5X2ewzAMLMuipqaGv/zlL31er7S0NGtlF0IIUfjS6QztofhRH5exLEhDNJ0mGrPXfe9svfN4DAI+E7/Pi89r4ncCPNOU1ruBkDA4hy1ZsoSdO3fy8ssvd21bv349bW1tfOUrX+GnP/0pF1xwAd/+9rcPO+7+++8HoKWlheeee44TTjiB6dOnEwwGeeCBB7oet3XrVkKhUK/XLi8vp6OjI/uVEkIIUTBC0SQppzVusCwLMhmLZDJDKJKiuS3GgaYIew+G2Lm/nb0HO2hsidAWihOOJUmm0lkufWGQFrkcVlVVxa9+9St+8pOf8MMf/pBkMsmUKVPw+XxcdtllHHfccSxZsoSPfexj3H333Vx55ZUATJw4kauuuooDBw7wmc98BqUUADfffDM//OEP+d3vfkcmk6Guro6f//znvV775JNP5tZbb+Xiiy/mhBNOkGQHIYQQh8lkLNrCR2+NG/R5LcCyiMbTROM9Wu9Mw+6e9Xu6umV9Xg8ejyfr5cgXEsjluEWLFnUlIPTG4/Ecsf/ss8/mn//5n49Ya7WhoYFbbrml1/NorQ/7u6Kigj/84Q/DKLkQQohCFoomnXFwI69z7F0mY5FMZQhHU4Ad4JmGQUvY4mBLGL/33fF3fl9xBHcSyAkhhBBiUCzLon0EWuMGXw5IWxahSIz2UBJIdrXemQYE/HbmrB3cGfi9HjwFNjWKBHIF5qmnRma6FCGEEKJTJJYinsjNMWtdrXdAKpo6ovXusKlROlvvvGbeTo0igZwQQgghBqUtFMcaWo6Dazpb79IJi7gzNYqB3XpnmDiZs50BXv5MbCyBnBBCCCEGLBpPEo2n3C5GVth5FfbUKJF0mkiPqVHyYWJjCeSEGAHpjIVH5kASQhSg9nAi71rjBmswExv7fCYlAZ9rZZVATogsS2cs9jWGsLAO/yXny+9xGCI3pNMZUukM6bRFKpMhlbbweUz8fg9er9zSxciKJ94dc1aMepvYuCToZdJYCeSEKBgd4XjXIOCev+RM8/AsKp/PmQNJWu+Eww7SnEAtnbGDtVSGZDpDMpUhkwEsy+kSevc40zBobE9xsCVM0Oftep/JDweRTR2RBJlMgTfHDZq7z4cEcsP00esepbXjyBTs6ooAt1933rDOrZRi9erVh80FJ3JbKp3p9f0A9i+5TLr3LKqeE1z6ffkxyFYMXmeglkpbpNN2gNb5bzKZ6ZoM1bIG9/WQsSyisQTtoSTtJDENMD0GJQEvQb/X6QryyrJHYsiSqTTt4aTbxRA9jFogp5TaAcSc/wC+rrV+TCk1B7gNqAOagGu01pudY4a0bzT19aXd13ZR2NpC8UEtV9OZRdXbDOZer0HQ58HX2XpXRBNc5rNU967Prv+3W9MSqUzX2BsGGagNVsaCTMqiI5WkI2zPrWWaBiUBjxPYeQj4vdIaLAasIyytcblotFvkLtdab+ix7WbgJq31HUqpq4FfA2cOc19WPPX6Lh5/ddeQj//Gr1b2ue/sE6Zy5nFTj3qOW2+9lRdeeIGWlha+9KUvce655wKwZs0arr/+esLhMABf+9rXWLFixWHHPvjggzz++ONUVFSgtWb8+PF8+9vf5vrrr2fnzp0sWLCAG264AcMwCIVC/OhHP0JrTTwe58QTT+Qb3/gGHo+HW2+9lb/97W+k02kCgQDXXXcd8+bNA+xWwy9+8Ys8/vjjtLa28rWvfa2rjMUmkUzTFkoM+zy9DbI1AMN8d4LLQFeAJ4tLj7ZkKkM6kyGVsux/nWAtNcqB2mBZFqTTFqFIilAk1RXYBf12QBd03leFNlmqyI5UOkNbePj3N5F9rnatKqXGAUuBs51NdwM3KqXGAsZQ9mmtG0er/KPBMAz+8Ic/sG3bNq688kqOO+44PB4Pn/vc5/jlL3/J0qVLSafThEKhXo9/4403eOihh5gwYQKf+cxn+PKXv8wdd9xBSUkJ73//+3nppZdYvnw5P/rRjzj++OP5wQ9+QCaT4Stf+Qr33XcfH/rQh7j00kv5xCc+AcCLL77Id7/7Xe69996ua5SXl3PfffexatUqvvCFLxRtINfaER+xX6sWYGWOnODSdFrv/P7DM6g8XvcG3uYzy7JIpS1S6TTpNP0GavmetdcZ2IWd91NXN7/fpCTgJeDzds2nJUQokiA9iN4GMXpGO5C7UyllACuBbwJTgL1a6zSA1jqtlNrnbDeGuG/AgdyGDYc3Dnq93q4WLoAT59Vx4ry6fs9xxXee6HPft65d0u+x3a/VlwsuuIBwOMz48eNRSvHKK69gmiYNDQ0opbrO0bPsnRYtWkRFRQXhcJhZs2Yxbtw4TNMkHo8za9YsNm/ezOLFi3nyySdZu3Ytv/vd7wCIxWLU1tYSDod5/fXXufXWW2lvb8cwDHbt2nXYtc444wzC4TCzZ8/m4MGDNDc3EwgEjihLIpFg1apVR61ztozmtQyvnwPNUVIp92c693hMPKbJUytXOVmzXjymhWFYmFhkMhkymdFZH9Et3V97wzAO+y9jGVgYWEAmY3QlE6TSFsl0Gitjt7RZGcvOUMszGzdtzMp5TNPA67HfPyUBDz6PYb+PsEin03arYw4azc99rhmpuns8Xg51pIjGcrtFLlvv/cGqKA3SuNcgnXbn/j+agdypWuvdSqkA8HPgRuBno3j9IyxYsOCwgGPjxo1ZTSzIxrlKS0u7zmOaJsFgEMMw8Hg8Azp/WVlZ1+OCwSCZTKbr70AgcNh5br75ZqZMmXLY8YlEgq9//evccccdzJ8/nwMHDnDaaacddu2amprD/g4EAr2Wze/3s3jx4kE+A0OzatUqli1bNirXAtjXGKKyNndS8jdu2sjMmfO6/u5ae9CEgM/TlVhRaF2zlmWxbt0bqHnzupIJ0hmLZCptB2oF1KLWm42bNjJv7ryjP3CITNPA57UTKAKd4+xyaNzmaH/uc8lI1r09HKe8OToi586WkX7v96ck6GHS2IoRvUY8Hj+i8anTqLWZa613O//GgV8BpwC7gUlKKQ+A82+9s32o+0ZVdcWRLU/9bR+s++67D4AdO3awceNGFi9ezJIlS9i6dStr1qwBIJ1O09bWNqzrnHnmmdxyyy1dvyiam5vZvXs3iUSCVCrFxIkTAbjrrruGdZ1CFIrk/iznlgWZjEUqZXelNbfFOdAUYe/BEDvfaWdvYweHWiO0h+NE40nS6dxssbMsOyiLxpN0RBK0dsQ41BrhnaYQew52sGNfO++0xtnXGOZAU4RDrTFa2uOEIili8TTptEUmU5hB3GjIZOyljVo7Evb750CIXQfaOdgSpj0cJ57I7c+BGDzLsmgLSfJeLhuVFjmlVBng1Vq3OV2rVwBrtdYHlVJrgSuBO5x/13SOcxvqvtE03ClGjsbv93PFFVfQ0tLC97//ferq7K7eX/7yl/z4xz8mEolgmiZf//rXWb58+ZCv881vfpOf/OQnXHLJJRiGgc/n45vf/CZTpkzh85//PJdffjkTJ07ktNNOy1bVCkImY9EaiuVtYJCxgLR12OSWhy0s7bfH3XWf825Ey5OxMz3TmXen5+gco5ZM2QkGnUvq9PWcJ5OpvH098k2mW1JOO0lM06CsxEtVeYCgX2a3KgThaIpEMjd/2AmbMRrjHJRSM4D7AI/z31vA57XW+5VSc7GnEakBWrCnEdHOcUPadzSrVq1qALb31rXamY1ZCMLhcE7NQTeaz+9odbG0h+IcbMm9LodsdzN0ds16PIazsLTXng7FmRJloJPO9hWoJVOdCQVHD9SOxs0uFrflSt1Nw6C81A7oAqMY0EnXavbrvrexo+tHXi4roq7V6cuWLdvRfd+ofMK01tuAXkf+a603ASdmc58QoyGdztBSJPMFdo4ry2QskskM0Jk1a687GPCZBPx2cOfzmnZGZGfQ5gRqyXSGdBYCNZH7MpZFezhJKJKivNRHVbl/VAM6kR2RWJJYPPeDuGInnywhhqgtnCCZKu4uh851ByPpNJFuXbOABGrCCegShKJJO6Ark4Aun7SHE/I5zgPyiRJiCJKptAwA7oPc+EVPmYxFeyhBKCIBXb6IJVJEopK8kg/kkyTEELSF4jI5phCDJAFd/mgPJ/JyHsViJJ8gIQYplkjJwtFCDEP3gK6i1EelBHQ5JZFME4rIPS5fyCdHiEFqaR+5pbiEKCaZjEVbKEGHE9BVlQfw59AEw8WqI5KQe1wekUDuKKLxJPFE9rN2An4PJYH+18NUSrF69eqsTyHyxBNPMG7cOBYtWtTr/ldeeYX/+q//4s9//nNWr1sIItEkkZj8UhUimySgyx3JVIb2cG4vxSUOJ4HcUcQTaQ61xrJ+3jHVwaMGciPliSeeYMGCBX0GcqJ3lmXR0pG/k/8Kkeu6B3SVZT4qyySgG22hSELG/+YZCeRGSCocJhU5chH7TvF0GZH2o5/n1z//OS+9/jpt7e187pOf5CxnZYV1b77Jz3/9a8KRCABf/MxnOPn44w87du36N7j+xl+SsSxSqRSfvPpqqiorefKJJ3jh+ee55667uPqDH+Sic87hpt/9jseefpqxY8awYO5cMskkkb17h/4E9CLR2sob//6drJ6zL/GODt7481+yes50OpM3M5xXRMI0l+bOZNCjSepeGHU/hD2Vjcdj4DVNjAGsBzwSn/t8kY26W5ZFPJHOyx+rbr73q047DS4Z2VWe+iOBXI4zTJPbbryRHbt28bHPf56lCxfi8Xj48ne+ww3f+x7HLlhAOp3uCui6u+2eP/CRyy/nwnPOwbIsQuEwFeXlnL58OcfMmcMV738/AM+++CLPvvQSf/jNbwj4/Xzp298e7WrmPMuyJ7cVQowey4JUyiJtpAcV0ImhScs6xHlJArkR4i0rw9vP2LZAdZDSiuBRz3PVJz5B6fjxHDNpEvMXLEAfOIDH42HWnDksP/fcrsf1tjjIccuX839//COHolFOOeUUFi9ebJettBR/dTWlkyYBsHbLFi64+GLGzJoFwIc/+lF+9atfde3PFn97O/N+8P2snrMvq1atYmEWl6uxF2fPfhf7SDmwaSOTc2CpJjdI3Qu37qZp9Nvlmu3PfT4Zbt0zGYu9jR3EE/n5g9XN935J0N3uf9PVq4tBsSwLwzAY6Pq4H/nIR7j55pupra3lP/7jP/jZz37W53lF35KpDK1FshSXELksk7Fo7UiwpzHEodYIiaQsH5Ut4ViSRJ4GccVOArkcd9999wGwY8cONm7cyOLFi1myZAlbt25lzZo1AKTTadra2o44dufOnUydOpUrrriCa665hjfeeAOA8vJyOjo6uh538skn88gjjxCJREin013XFLb2cJyUDP4VImdk0nZAt7cxRFNblGRKArrhsCyL9lAcucvlJ+laPYqA38OY6qN3gQ7lvAPh9/u54ooraGlp4fvf/z51dXUA/PKXv+THP/4xkUgE0zT5+te/zvLlyw879u6772b16tX4fD78fj/f+ta3ALj44ov5xje+waOPPsrHP/5xLr30UtauXcull17KuHHjOPHEEzlw4EB2K5ynEsk0bSFJxRciF6XTFi3tcdrDCSrL/Hi8freLlJeisRSxEZhmS4wOCeSOoiTgc22aEK01AJ/85CeP2Ld06VLuueeefo//t3/7t17noFu0aBF/+9vfDtv2xS9+kS9+8Ytdf//Lv/zLUIpccFo7ZPJfIXJdZ0B3oDVOU1uUyjI/Pq9MWzJQbeG4JDnkMQnkhOhDNJ4kFJHWOCHyRSKRPKyFrrIsgM8rI4j6E40nicZSbhdDDIMEckL0oaU9jjTGCZF/ena5SkDXt45wQu5zeU4COSF6EYokicblV6oQ+ax7QFdV7qeiVAK67uKJFKGo3OfynQRyPXRO8SGyK5+mOMlkLFo6ojJmRIgCkU5bNLfFaQtJQNddRyQhY4ALgLyTuwkGgzQ1NeVV0JEPLMuiqamJYDD72b8joSOSyNtJMYUQfesM6PYc7KC5PVrUq7UkU2k6Ikm3iyGyQFrkupk8eTJ79uyhsbHR7aJkRSKRwO/PjXT8YDDI5MmT3S7GUaXTMvmvEIXu8Ba6AJVlfrye4mrX6IgkSMv8mAVBArlufD4f06dPd7sYWbNq1aquZbnEwLSF4yRTxfsrXYhiYgd0MdpDcSqLKKBLpzO0y/yYBUMCOSEcMvmvEMUpVWQBXUckIavVFBAJ5IRwtIXi0tUgRBHrHtB1drl6Ciygy2Qs2mV+zIIigZwQQCyRoiMsA3+FEHZA19QWo60AA7pQNElCkrkKigRyQtA5+a+0xgkh3tU9oKuuCFBRmt8BnWVZtIclmavQSCAnil4kmiQSk9Y4IUTvUmmLQ60xWjvyO6ALR1PEE2m3iyGyTAI5UdQsy6KlIyaT/wohjqozoHt3YuH8Cujaw3G51xUgCeREUeuIJInF5ReqEGLgkqnM4QFdWQCPmdsrAkVisuxgoZJAThStdMaiNRRDfqAKIYYinwK69nAi661x37nlxV5Xh6go9fH9Ty/P7sVyTF91r64IcPt1541qWSSQE0WrIxyX7C0hxLB1BXThBFVluRfQxRMpItHst8b1tcRXISz9ZVkWlmX/m+n618LKQAarzzq6sTKQBHKiKCVTshSXECK7ksl3A7rq8gDlpf6cCOjaw4lRz8r/2wvbyGTAwiKTeTcYsizsgKjz/zMWFs6/VvfHdQugMt2P6xZUdQu0ItEogddfP/L8Pa7ZWSYr0yNA635+p0z5QgI5UZTaw3GZ2VwIMSKSyQyNLVFaQ3HXA7pEMu1KC9nTq/ZgGgaGAYZpdP3/4f86+w0D0+x7f9c2095mGuA1za7/NwwDIxOnsiLonAsM3j1n98f1PFf38x+2v/vjMDDMd8tkGnD/s1tH/TntiwRyoujEEylZiksIMeI6A7q2cJzqMjugM0c5oOuIJMhkRv9H6w2fP21Ur7dx00bmzZ03ateTQE4IF7WG4q7c2IQQxSmRyHAwEaV1lAO6VDpDe1h+tBa6/JkAR4gsiMSShAtgIK4QIv8kEhkOtkTZ29hB+yj8oOwIJ0Z0/eiKUt+gtheSvupYXREY5ZJIi5woIpZl0doRRxrjhBBuijstdG1hey3X8pLst9ClM9aIt8Zdc/4x3HTfOq694BiOnT12RK+Va7pPr1IS9DBpbIVrZZFAThSNUFQmxBRC5I54IsPB5iht/uwHdOFIgmRqZKdXWrelEZ/XZF5D7YheR/RPAjlRFDIZi1ZZiksIkYOyHdBZlkVbeGSnV8pYFus2H2JeQy0Bn2dEryX6J4GcKAodkQRxmfxXCJHDDg/ogpSX+IYU0IWiyRGf7HzHvnY6IgkWF1mXai4a9UBOKfVd4DpgodZ6g1JqDnAbUAc0AddorTc7jx3SPiG6S6dl8l8hRP6wA7oIbX6TqvIgFaU+DGPgAV17OD7iE9qu29yI12NwzHTpVnXbqGatKqWWAicBu7ptvhm4SWs9B7gJ+HUW9gnRpS0cH/GxIkIIkW3xRIbG5gh7DnbQEUlgDWBsSCSWJBZPj2i5MpbF+i2HmDutlqBfOvbcNmqBnFIqgB1w/RPYPxaUUuOApcDdzsPuBpYqpcYOdd+oVEbkjUQyLZP/CiHyloXTQtcUYW9j6KgBXVsoPuJjgXe/00FrKM4i6VbNCaPZIvd94A6t9fZu26YAe7XWaQDn333O9qHuE6JLWyg+ovMoCSHEaLCAWDzdb0AXjSeJxka2NQ7sbFWPabBget2IX0sc3ai0iSqlTgaOB/5tNK43UBs2bHC7CCNu1apVbhfBNWvWvsGBlijJ1Mjf2HLRxk0b3S6Ca6TuxatY6m+aJqVBP5WlXjxGGo/Hw+p1G2luC4/odS3L4vW3mqiv87FjR24NS3frta8oDdK41yCddue7ZrQ6t08H5gLblVIAk4HHgC8Ck5RSHq11WinlAeqB3YAxxH0DtmDBAgKB0Z+FebSsWrWKZcuWuV0MV6xdu5bxk2dQUVecqziM9rqDuUTqXpx1h+Ksv2FAwO/hnb07mThpCuMnjmwPxO4DHXREG7lgxXTmzZ0wotcaDDdf+9GYEDgej/fZ+DQqXata6x9rreu11g1a6wZgD3Cu1vpeYC1wpfPQK4E1WutGrfXBoewbjfqI3JcxvERixRnECSGKh2XZXa7vNIVGZQ3pdZsbMU2DBTOlWzVX5EK6yWeB25RS3wFagGuysE8UMcuyaA8nqZSseCFEkRhIRms2rrFuyyFmT66mLFj466nmC1cCOadVrvP/NwEn9vG4Ie0Txa0jkiQcHf15475zy4t0RI5sBawo9R22Lp8QQuSjfYfCHGqNcuYyySvMJaM6j5wQIy2dsWgNxUali6Gn3oK4/rYLIUQ+Wbe5EcOAhdKtmlMkkBMFpT0UH/GlaYQQothYlsW6zY3MmlxNeanf7eKIbiSQEwUjmcrQGsrNpbgSyeKcAkUIURjeaY5wsCXK4lkyCXCuyYVkByGyoi0Uy9nJf6/77cucOH8CyxfVM7a6xO3iiDzX13jMEr/BD4ts+g0xOtZvbsQAFs4a43ZRRA8SyImCEE+kaA/n7li0udNqeG7tXp5dvYe5DbWsWFzP3IZazEEshC1Ep77GXUYTuflDRuS/dVsOMX1SFZVl0q2aaySQEwWhNRR3JcGhU7ifOesqSn1c875jaAvFeWnDfl56Yz+/+csG6qqCnLKonhPmT5BUfjEgoUiCrXvb+n1MOJqkrETeTyJ7DjZH2H8ozPtPn+l2UUQvJJATeS8SSxJyOTP07y/vxDDgqx85joljynp9TFV5gPNOauDs46eyfushVq7bx4PPb+ORF3ewdO44ViyuZ/K4kZ0dXOSXzsBty55Wtu5pY3/T0Zdf+vavX2TqhArmTqtlbkMtU8dXYJrS8iuGbt0We679RTI+LidJICfymmVZtHTEGYW5MPt0sCXCyvX7OGn+xD6DuO48HpMlc8axZM449jWGWLluH6s2HeCVN9+hYWIlKxbXs3j2WLweyUUqNqFokm17W9m8+/DAze81mV5fxVI1jlmTq/ife9f2eY5zTprGph3N/P2VnTz2yk5KA17UtBrmTqtFNdRQVVa4yxKKkbFu8yEaJlZSXSHvnVwkgZzIa6Foklg85WoZHnp+Gz6PyfknNwz62Pqx5XzorDlctGIGr771Di+s38cdj27iL89t5aQFE1m+sF5ungWsM3Dbssduddt/qPfAbfL4igEH9ued1MB5JzUQjiZ5e1cLG3c0s2lnM2vetltV6seUMa/Bbq1rmFgpPxhEvw61RtnbGOKS02a4XRTRBwnkRN7KZCxaO2KutsZt2d3Khm1NXLB8OhXDGARcEvRy+tLJnLpkEm/vbGHl+n088eounnxtFwtmjmHF4npmTa7GkOSIvNZf4NZQX8WS5WOZNbmaKUcJ3CpKfX1mrXYqK/GxRI1jiRpHxrLY3xhm485mNu1o5unVe3jy9d0E/B7mTKnu6oatrQxmv9Iir0m3au6TQE7krY5IgriLk/9mLIsHnttKTUWA05ZOyso5TcNgrtNa0tQW5cX1+3n5zf2s33KI8bWlrFhcz3HzxhP0y0c3H2QrcOupryXfNm7a2Ot20zCYNK6cSePKOev4qcTiKTbvbu0K7N7Y2gTAuJpS5jbUMG9aLTMnV+PzSmtdsVu/+RBTx1dIkJ/D5NtA5KVUOkNLe8zVMry+8QB7G0N89Lx5+L2erJ+/rqqEi06dwbknT2OtbuT5dXu57+kt/PWF7Rw/bzwrFk9ifG1p1q8rhi4cTXZLTmhlX5YCt2wLBrwsnDWGhbPGYFkWB1uibNrRzMYdzby4fh/PrdmLz2sya3I1c6fVMLehlrHVJdIiXGSa22PsOtDBhSumu10U0Q8J5EReag/HSbk4+W88mebhF7YzbUIFS9TIdjn4vR5OmD+B448Zz653Onh+3V5e2rCflev2MXtKNacunsQxM+rwSGbiqOsrcPN5TabXV/K+5Q05Ebj1xzAMxteWMr62lNOXTiaRTLN1Tysbd7awaWcz9z/bDM9upbYyaI+tm1bD7Ck1BPz2j5e+JieuKPX12XIo8sN6p1tVVnPIbRLIibyTSKZpCyVcLcPTq3bTFk5w7QXHjForhWEYTJtYybSJlVxy2kxe3rCfF9/Yz61/fZPqigCnLKznpAUTZB3EETSQwG3m5Gqm5nDgdjR+n4d50+uYN91eGP1QWxS9ww7qXttoJ+R4TIPp9VXMa6jtc3LivraL/LFu8yEmjS1njKxGk9MkkBN5pzUUd3UprtZQnKdf383i2WOZXl/lShkqSv2cfcI0zjxuKm9ua2Llur387cXtPPrKDpbMGcfkqiSyUNPwhWNJtjnj2zrHuFkUVuB2NGOqShizuIRTFteTSmfYvq+NTU5g99DKbW4XT4yQ1lCcHfvbed/yBreLIo5CAjmRV6LxJCGXl+J65MXtpC0rJ8aNeEyDRbPGsGjWGA40h1m5bh+vvXWA15NpVm1fzYpF9Ryrxo7IGL5CdLTA7fwiCNz64/WYzJ5id61edOoMWkNxvvfbl90ulhgB67ccAqRbNR9IICfySmtHgoyL843sOdjBa28d4IxlkxlTlVvdDeNry7jsPbO54JTp/PWZdWx5J83dj2sefH4rJ86fyCmL6qmtksyz7voM3DwmDfWVnHeyPcZt6vgKvJLBeYTqcpnjsFCt39zIxLoyxklCVc6TQE7kjXA0SaSfNU1HmmVZ/OW5bZSW+Dj7+GmuleNogn4v86eWctnZc9myp5WV6/bxzOrdPL1qN8fMqGPF4nrmTK3BLMIMxHAsybZuS17tawxJ4DaC/vTUZi5aMaMrMULkh/Zwgm172zjnpNy9z4l3SSAn8oJluT/575vbmtiyp5XL3jOLkmDuf3QMw+jqBmvpiPHSG/t5acN+3tzWxNhqe8zTCfMm5EVdhkoCt5HX1+TEPq/Ji+v3oXc2c+U5c5kxyZ3xpGLw3thyCAtYPFu6VfNB4d7BRUHpiCSIxtOuXT+VzvDg89sYX1vKyQvrXSvHUNVUBHnf8umcc8I01m1pZOW6fTzw7FYefmE7y+aNZ8XieurHlLtdzGGLxJLsOBBn04EtfQZuMydXMW18pQRuWdLfFCNb97Ry1981N/5xLacvncz7lk+XSYbzwLotjYyrKWWCdKvmBQnkRM5LZyxaOuKuluHF9ftobI3yyUsW5PV8bV6vybK541k2dzy7D3bwwrp9vP7WAV56Yz8zJ1VxyuJ6Fs0cgydPBvJHurW4bTkscOuQwC0HzJxczdeuPo4Hn9/KM6v3sHFHM1edO5ep4yvcLproQyiSYMueVs46fqpMAJ0nJJATOa89FCeZdG8prkgsyWOv7GTO1GqOaah1rRzZNmVcBVecrbhoxQxefesdVq7bx+8f3khlmZ+TF07k5IUTqSrLrcHsfQdu77a4edOtnHbCQgncckTA7+GD753Dwplj+MMTmv/5w2rOOmEaZ58wtSgzf3PdG1ubsCzpVs0nEsiJnJZMZWgNudsa9/iru4jGUlxy6syC/IVaVuLjPcumcPqSyWzc2czKtXt57OWdPP7qLhbPGsOKxZOYXl/pSt37Cty8HoOGiVWce9I0e4zbhMquLruNmyISxOWguQ21fP3q4/nzs1v4+ys7eXNbE1edqwqiS7+QrNvcyJjqEurHlLldFDFAEsiJnNYWirk6+W9ja5Tn1+7lxAUTqB9b2F84pmkwf3od86fX0dgS4YX1+3j1rQOsebuR+jFlnLK4nmVzxxPwjVwGYjSWYus+OzFhy55W9h48euAm8kdJ0MtHzp3LopljuPept/np3as5/6QG3rNsCmYeD1koFOFoks27W3jPsikF+aO1UEkgJ3JWPJGi3eXJf/+6chsej8H5J7s/+e9oGltTyqWnz+L85dNZvekgK9ft5Y9Pbuah57dxwvwJrFhUz9ia4Q+ElsCtOC2cNYbp9ZX88anN/PWF7WzY1sSV5yjGZeE9JYZuw7YmMtKtmnckkBM5qzUUJ5NxrzVu655W1m85xPuWN1BZVpzrlwZ8Hk5eOJGTFkxg+752Vq7by8p1+3huzV7mTqthxeJJzGuoHXBrSv+BWyXnnDSN2RK4FYXyUj8fu+AY1uhG7nt6MzfcuYoLV8xgxeL6opzjMBes39JIbWWQyeMKu/eh0EggJ3JSJJYk5OKi2xnL4oHntlJdHuD0JZNdK0euMAyDGZOqmDGpirZwnJff2M+Lb+zntw9uoLYySDiaJJ48cnqY8hIfV5ytulZO6C1wmzW5mmkSuBUlwzBYOnccMydXcc8Tb3P/M1t4Y8shrjxHUVspq5CMpmg8hd7ZwqnHTpJu1QEwDQPDhKDfQ0nA3VBKAjmRcyzLnm7Ezcl/V206yJ6DIT5y7lz8IzgmLB9VlQU496QGzjp+Km9sbWLlur00t8d6fWwomuS3D26QwE30q6o8wKcuWcArb77DA89u5fo7XufS02Zy4vwJElSMkje3NZHOWNKt2ovuQVvA78Hn9eD3mvh9npx4f0ogJ3JOKJokFk+5dv1EMs3DL2xnyvgKls4d51o5cp3HY3LsnLEcO2csX/z5s30+7p8vXyyBmzgqwzA4acFE5kyp4e7HN3HPE2+zfsshPnzWHKpkTdcRt25zI9XlAaZOKO45/nI9aOuNBHIip2Qy7i/F9czqPbSG4lx9/lwZq5MFsyZXu10EkUdqq4L842WLWbl2L399YTvX3/46l71nNkvU2Jz9Is13sUSKTTubWb6wuMYnmoaBadpzHeZL0NYbCeRETmkPx4kn3Jv8ty0c58nXd7Fo1hhmTqp2rRxCFDPTMDhtyWTmNtRy12ObuP3Rjazf2sjl75lNeWlxJh6NpLe2N5NKF3a3aq9Bm88O3PIpaOuNBHIiZ6TSGVpdXorrkRd3kE5bXLRihqvlEELAuJpSPv+hJTy9ajePvLyDrXvb+JCzSoTInnWbG6ks89NQX+l2UbKikIO23kggJ3JGWyhOysXJf/c2hnj1zXc4felkxlSXuFaOfFRR6qOjlyzjilKfC6URhcQ0Dd57/FTmTa/lrsc0tz70JsfNG88HTp9FSVC+woYrnkyzcUczJ86fkJfdqp1BW1V5CbVVAfxeLz6fWbBBW2/kUyByQiKZpi2ccO36lmXxl+e2UhL0cvYJU10rR776/qeXu10EUeDqx5TzhSuW8PirO3ni1V1s2d3Kh8+ew9xphbP+sRs27mgmmcqweFbud6v2bGnrHrS1HLCorSzOH+ASyImc0BqKk3GxNe6tHc1s3t3K+8+YRWlQWpGEyEVej8n5J09n/vQ67vq75tf3v8HyhRO5+NSZBPwyTdBQrN/cSHmJjxmTqtwuymH6C9p6a2nLZNwbW+02CeSE66LxJCEXl+JKpzM8+Nw2xtaUcMrCia6VQwgxMFMnVPKlq5byyIs7eHb1HvSuFq48R7ldrLyTSKV5c3sTy+aOd3Wt28ODNi9+r6foukeHY8CBnFJqOvAD4FjgsPU7tNbSFyWGrLUjTsbF+UZe2rCfgy0R/uHi+Xg8MteZEPnA7/VwyWkzWTCzjrv/rrnpj+tY2FDCzFlp/F5pnRsIvbOFRDIzqtmqErRl32Ba5O4CtgJfBiIjUxxRbMLRJJGYe5P/RmMpHn1pB7OnVDN/ep1r5RBCDM3MSdV89SPH8dDKbbywfh8H7lrNVecopk4ojAzMkbRucyOlQS+zRqhb1TQMTI89ua7fJ0HbSBlMIDcfOEVrXbwd0SKr7KW43J389/HXdhKJpbjk1JlyYxEiTwX8Hi4/czZV/jAvborxP/es4awTpnL2CdPwSit7r1KpDG9ua2Lx7LFZ6YkwDTA9hgRtLhhMIPccsARYNZQLKaUeAKYDGSAE/IvWeq1Sag5wG1AHNAHXaK03O8cMaZ/IDx2RBLH4kQutj5ZDbVGeW7uX44+ZwKRx5Uc/QAiR0yaPCfC1qxdw/7Nb+Psru3hzWzNXnauoHyOf75707hZiiTSLZw1/Tj7DgLG1pZSX+CRoc8FgwvAdwGNKqVuUUt/v/t8Aj79Wa71Ya70EuAG41dl+M3CT1noOcBPw627HDHWfyHHpdIYWlyf//evK7XgMg/ctb3C1HEKI7CkJernq3Ll84qL5tIXj/PSu1Tzx2i7SGReb/nPQ+s2NBP0eZk+tGfa5TMOgJOCVIM4lg2mRKwMeAnzAlG7bB/Tp0Fq3dfuzCsgopcYBS4Gzne13AzcqpcYCxlD2aa0bB1En4ZL2cIJk0r1e+m372li3uZHzTpomC3ILUYAWzhzD9Poq/vTU2/zthe1s2HqIq86dy7iaUreL5rpUOsMbW5tYMHNMVrqe/X5TurBdNKBATinlAXYDP9BaD7kZRSn1W+Ac7EDsPOyAcK/WOg2gtU4rpfY5240h7pNALsclU2laQ+61xmUsi788u5Wqcj9nLJty9AOEEHmpvMTHte87hjVvN3LfU5u54c5VXHjKdFYcOykvVzHIli27W4nGUyyenZ2lzkoCMpOZmwb07DuB0j8B1w3nYlrrTwIopT4K/AT49nDON1wbNmxw8/KjYtWqIQ1pHDGmaRJLezjUEh7xa23ctLHX7Vv2xdh1oIMzFlawbevbI14Ot/RV/2IgdS9evdW/BHj/yVU8/2YH9z+7lZff2MUZCyqpKC2saUoG+to/t6Edn8fAih5k46bhtX14TJMJdWVsT7s7VAZy7/tutAwmjP498FngV8O9qNb6dqXULcAeYJJSyuMEix6gHrv1zxjivgFbsGABgUDhdqutWrWKZcuWuV2Mw8QTKfY2hhk7fmTHq2zctJF5c+cdsT2RSnPvC68xeVw5F525pGB/lfdV/2IgdS/OusPR679sscWrb73D/c9u5c8vtXLp6TM5cf6EghjbNdDXPp2xuPOZF1k4aywL5w//veIxDaZOqHB9Ds5c/L7Lpng83mfj02ACuROAf1FKfQ07YOr6JtZan9bfgUqpcqBGa73b+fsioBk4CKwFrgTucP5d0znOTSk1pH0id7V0xMm4OOj42dV7aO2I85Fz5hZsECeE6J1hGJw4fyKzp9Rw99819zzxNuu3HOLDZ80pmrGyW/e0Eo6lsjYJcMBvuh7EFbvBBHK/cf4bijLgj0qpMiCNHcRdpLW2lFKfBW5TSn0HaAGu6XbcUPeJHBSJJQlH3VuKqyOc4MnXdrNwZh2zplS7Vg4hhLtqK4P842WLWLluH39duY3rb3+dD7xnFkvVuIJonevPus2N+H0mcxuGn60KEAzI2tRuG3Agp7W+bagX0VofAE7qY98m4MRs7hO5x578N+7q5L+PvLSDZDrDRStmuFcIIUROMA2D046dxNxpNdz12CbueHQT67cc4oNnzqa81O928UZEJmPxxtZDHNNQl5VlzAzDXrVBuGswa61+oq99Wutb+9onBEAomiQWd28prn2HQrz85n5OPXYSY2X6ASGEY1xNKZ//0BKeXrWbR17ewbbb2/jQe+ewMAsT5eaa7fva6Igks9atahoGAZ8Ecm4bTNfqR3v8PQGYCbzAu5P7CnGETMai1cWluCzL4i/PbaPE7+WcE6e5UwghRM4yTYP3Hj+VedNruesxza1/fZPj5o3nA6fPoiRYOFNrrNtyCJ/HZF5DbVbOFwx4ZHxcDhhM1+p7em5zWumKN0VKDEh7OE484d7kv5t2NPP2rhYuPW0mZUEZzyGE6F39mHK+cMUSHn91F0+8upPNu1u44izF3CwFPm7KWBbrNzcyt6GWQJa6Q4Myf1xOGG4o/X/AP2ShHKJApdIZWl1ciiudsfjL89sYU13CKYvrXSuHECI/eD0m55/cwL9esZSg38uvH3iDe598m3jCvXWhs2Hn/nbawomsTQJsGEi3ao4YzBi5nkFfKXA10JrNAonC0haKk0q7l+Hw8ob9HGiO8IkL58sSMkKIAZs6voIvX7WMR17azjOr9qB3tnDVOYqZk6vdLtqQrNvciMdjMH96XVbOZ5oGAb+0yOWCwXyzpYBkt//agG8C/zQC5RIFIJFM0xZKuHb9aDzFoy/tYOakKhbMzM7NSwhRPHxek4tPncnnPngshgE3/WkdDzy7hUQqv1rnLMti3ZZDzJ1Wm7Xu0KDfg8cs7Kla8sVgXtHpPf4Oa60PZbMworC0htyd/PeJ13YRjia55PSZBT83lBBi5MyYVMVXP3IcD63cxrNr9rJxRwtXnauYNqHS7aINyK4DHbR2xHnfyQ1ZO6eMj8sdg2mR+7LWeme3/w4BKKV+PjJFE/ksGk8SCrvXGtceSfPsmj0cN288U8ZVuFYOIURhCPg9XH7mbD77/oUkkml+cc8aHn5xO6m0e4lcA7VucyMe02D+jOz0TMj4uNwymEDuY31s7zktiShylmXR3BbDxcY4Xn07hGkYvO+Ung3JQggxdGpaLV+7+jiOmzeex1/dxc/uXs2+xpDbxeqTZVms23yIOVNrKM1S1r6Mj8stR30luk0E7O1lUuAZgHSvisO0hxNE4+6NIdmxr41t78Q558RpVBfJ+olCiNFTEvRy5TlzWThzDPc++TY/vXs1553UwHuOm5Jz48b2NIZobo9xzolTs3ZOGR+XWwYSUne2uPk5vPXNAg4A12a7UCJ/JVNpWtpjrl3fsiweeG4rpQGTM5dNca0cQojCt2DmGBrqq/jTU5v524vb2bDtEFedM5dxtbmzesz6zYcwDVgwI3srVZTI+LicctRXo3MiYKXUf2qtvzXyRRL5rKXD3elG1r7dyM53Ojh9QUXWJr0UQoi+lJf4+NgFx7BGH+RPT2/mhjtXccGK6Zx67CRMl5Os7G7VRmZNqaGsJDvdqoaB3FtzzGBWdviWUqoOeB8wQWv9E6VUPWBqrfeMWAlF3ojEknS4mOCQTGV4aOU2Jo0tZ/akoGvlEEIUnyVqHDMmV3HvE2/zwLNbeWPLIa48R1FXVeJamfY3hWlsjXLG0slZO6dpGgR80iKXSwac7KCUOh3QwEeA7zibZwP/bwTKJfJMJmPR3O7eeqoAz63ZQ0tHnEtOm+H6L2EhRPGpKgvwyYsXcMXZij2NIX5yxypeemM/lks3xnWbD2EYsHBmNrtVPZgyPi6nDCZr9efAh7XW52FPDgzwCnBCtgsl8k97OE7MxQSHjkiCx1/bxfwZdcyeUuNaOYQQxc0wDE6cP4GvXX0cUydUcO+Tb3PLA2/QGhr9pQrXbW5k5qQqKsr8WTtnULJVc85gArkGrfWTzv93/rxIMLhJhUUBSqbStLi4nirAoy/tIJnKcPGKGa6WQwghAGorg3z2A4v4wBmz2Lq3jetvf51Vmw6MWuvcO01hDjRHWDx7bNbOKePjctNgArm3lFLn9th2FvBGFssj8lBLe5y0iwkO+5vCvLRhP6csqs+ZbDFJzRdCmIbBqcdO4qsfWcb42lLueHQT//e3twhFRn4s8fothzDIbreqjI/LTYNa2QG4Uyl1G1CilPo18H/AV0eiYCI/RGJJOkbhptSfh57fRtDv5dwTp7lajk5ej0H9uDKqyv3IUD0hxNiaUv7lg8dy4YrpvLm9if+6/XXWbxnZKVjXbW6kob6SqizOpSnj43LTgAM5rfXLwCLgTeBWYBvwIeBrI1M0ketyIcFh045mNu5o5pwTp2YtvX64aiqDBHxextaUMrGujIB/ML+XhBCFyDQN3nvcVL585TKqygP871/f5I5HNxKJJbN+rYMtEfYdCme1WxVkfFyuGsjKDqXAN4Bjgc3AdcBY4AbgW8DvR654Ipe5neCQzlj85fmtjKkKsmLRJNfK0V0w4KGi9N2BxaUlPgJ+D62hOG0dCTJuRr1CCNdNHFPGF69YwuOv7uLxV3eyZU8rV5ylmNtQm7VrrN9st/YtmpW9blUZH5e7BtJUcBNwEfAW9pi4+4BngA3AdK31P49Y6UTOSiTdT3B45c39vNMU4cIVM/B63W/1MgyorQge0fXg8ZjUVZUwYUwpwYDcCIUodh6PyXknN/CFK5YS9Hv59QNvcO8TbxNLpI5+8ACs29LItAkV1FRkbz5Nj2lIi1yOGsirci5wrNb6oFLql8Au4Ayt9XMjWzSRy1o6Yq4mOMTiKR55aQfT6yuz+qtzOMpKfJT2071bGvQR8HtpC8Vo7UiQyUjrnBDFbMr4Cr581TIeeWk7z6zag97VwpXnKGZNrh7yOZvaouw5GOLiU7ObwV8S9GLIoN+cNJBmjHKt9UEAZwWHkARxxS0STRKKZH9cx2A8+fpuQpEkl542MyduLqZpUFNx9EHFHtOgtrKE+rFllEjrnBBFz+c1ufjUmXzuQ8diGvCrP63j/me3kEgNbdhKZxLF4lkyPq5YDOSV8Sql3gN0fVv2/Ftr/dQIlE3koEzGoqk96mqCQ3N7jGdW72bZ3HFMnVDpXkG6qSr3ExjEjS7o9zJxTDltoTgtoTgZF1s3Rf7ymAalJV6SqQyJRMbt4ohhmFFfxVeuPo6/rtzGc2v2smlHM1edO5dpg7zHrdvcyJTxFdRWZa9bVcbH5baBfPMcxM5S7dTU428LkFlYi0RbKE7c5S+Mv72wHQODC06Z7mo5Onk9xpBS/E3ToKYySGnQS1NbjEgsO+NjRGEzDYOA36Si1E9J0IfPGR8aT6RoOlhBScBDPJmRrvs8FPB5uOw9s1kwcwx/eFzzP/es4azjp3LOidPweo7egdbSHmPnOx1Zvzd6TIOATwK5XHXUQE5r3TAK5RB5IJFMu7LMTHc797ezWh/k7BOmZnUg73DUVgYHdJPtS8DvZeKYMtrDCZrb3R17KHKTYYDXY1Je6qOsxNdrN1fA78W04kwaV0E8mSYWTxGOJoglJKjLN2pqDV+7+jgeeHYrj7+6ize3NXHVuXOZNLa83+Pe7VbN7rjhUhkfl9Ok01sMmNtBhmVZPPDcVipK/bz3uKmulaO7koAnK+sYGobdqlcS6GydS7rafS1yQ2fXaXmJn5KAd8CTsQZ8HgI+D1XlAZKpNNF4inA0SSyeJi1BXV4oCXi58hzFwll13PvE2/zs7tWce9I0zjxuap8rx6zb0kj9mDLG1mR3hRsZH5fb5NURAxKOJglH3U1wWLf5EDv2t/Phs+bkxHgNw4CaimBWf6n6fR6ndS5Oc1uMlLTOFR3TGY9UXuqntFvX6VD5vB58Xg+VZQFS6QyRWIpoPEkklpLW3zywYMYYpn+0ij89tZmHX9zBhq1269z4HssRhmNptu9r5/yTG7J6fVPGx+U8CeTEUaUzFs0uJzikUhkeWrmNiWPKOOGYCe4VpJvy0v6nGxmOyjK7da65PUYoIq1zhc4APF6DilJ/n12n2eD1mFSW+aks85NOZ4jE7aAuHE2RyVjyPstRZSU+rr3gGBbpg/zp6c38952ruOCU6Zy6ZBKm80Ny+wF72Eu2V3MwPQZ+GR+X0ySQE0eVCwkOz6/bS3N7jM9+YFFOrPVnmgbVWVzDsDc+r4fxtWWUBhM0t8VIpiQrsdCYpkFZiZeyoJ/S4MC7TrPB47ETJipK/aSrLGLxFJGY3fKelqAuJy1R45g5uZp7ntA88NxWHlq57Yiu8h///jUqSn18/9PLs3JNGR+X+ySQE/2KJ1KuJziEIgn+/spOjplei5pa42pZOg12upHhqCj1E/R7aemI0RFOyBdsnuucyqG8xE5c8Hndb+3wmAZlTnnqqixiCSeoi6VIpTLynsshlWV+PnnxAl576wB3P657fUxHFuf5lPFxuU9eIdGvlg735zh77JWdJJLprM9UPlQ+rznirXG9XXNcTSllQR9N7VGZMyzPGIDHY1BRZre8lQRGpks+G0zToDToozToo86yiMZTdgZsLEkiKUFdLjAMgxPmT+gzkMsW05BpR/KBBHKiT6GI+wkOB5rDvLh+H8sX1TO+tszVsnSqqQzgGcZ0I8Nhj5/y0NIRoz2UQBIQc5sdFHnt5duCvj6zDXOVYbwb1NVUBoklUkTjaSKxJIlEhoxEdQXN45XxcflAAjnRq3TGoqXD3QQHgAef34bf5+HcE6e5WxBHScBDRenwpxsZDo/HZEx1KaVBH01tUdfHL4rD5WLXaTYYhkFJwEdJwEdtZZB4ItU1rYlMQFyYSgIeGR+XBySQE71qC8VcDxD0rhbe2t7MRStmUO5y8ATOdCOV2Z1uZDhKgz4Cfi+tHTHaOhLSOuIyr8fomrA3l7tOsyXg9xLwe6muCBJPponGkoRjSaKxoa0RKnKPjI/LD/IqiSPEEylaOxKuliGTsXjwua3UVgY59dhJrpalU3mp3cWUSzymQV1VSdcyX7G4fImOJtM0KAm8O+dbvnWdZkv3CYib22O0dsRdb80vBhWlvl4TGypKh3+fkvFx+UMCOXGE5va4690kr771DvsOhbn2fccMe0LUbDA9Rs4sCdabkoCP+jFeWkNxWjvczTIudIYBfp89dUdp0CdjiLoxDPuHhc9jcqg1Jq3EI6z7FCMbN21k3tx5WTu3x2uMWma+GB55lcRhOiIJIjF3ExxiiRQPv7iDhomVLJ6d3TUDh6q63J/zX9imaVBbGaQ06GV3WYnbxSk43btOg36ZW6s/leUBvB6Tgy0RWZ0kT5UGJDzIF/JKiS7pdIaWjpjrXSJPvb6bjkiCf7hofk58Wfp8JlVlozvdyHAE/V6qSmFMdZCWjrgswzQM0nU6dKUlPiZ6yjjYEnF9vK0YPBkflz/klRJdWkNx1+cna+mI8cyqPSxV45g2sdLVsoA9/1dNRdC16UaGKp1OU10R7FrmKxJNIeHcwHR2nZaX+imTrtNhCfi9TKgr42BLlGgs5XZxxACZhoFf1lfNG6MSyCml6oDbgZlAHNgCfEZr3aiUmgPcBtQBTcA1WuvNznFD2icGL55I0RZyN8EB4OEXd2BhccEp090uCgDBoDcrA4fd0vlF2hFO0NQek9a5fng8BhVOQktJQLpOs8Xn9TChroxDrVE6wu7fY8TReX2S6JBPRquZwQKu11orrfUiYCvwY2ffzcBNWus5wE3Ar7sdN9R9YpCa22OuJzjsOtDB6xsPcMbSydRWup9YYBpQUxHI+y90wzCoLA8waWw5ZSU+8rw6WdW51um42lKmjq/omp8v31/zXOMxDcbVlFBTGZD3Xx6Q8XH5ZVReLa11M/BMt00vA/+olBoHLAXOdrbfDdyolBqL3as16H1a68aRrEshshMc3O32sCyLvzy3lfJSH+89bqqrZenUOS6qUPh9HiaOKaM9nKC5LVq0g9C7d52WBn3S8jBKJKM1f0i2an4xrFH+MCmlTODvwIPAC8Dvtdbzu+1/C7gaO1gb9D6t9eqjlWHVqlUNwPasVCjPebw+DrUnicbc7fLY/k6Mx9e2c+r8CuZNcT/j0u/zMr4mSDpVmF1BHq+fcCxDWyhGOlP4A9ENw8Dv81Je6ifoMzBJk07LnHtuMAwDPH6aWmPEEu5myIsj+bxextcGyRTova8ATF+2bNmO7hvcCLt/CYSAG4ElLly/y4IFCwgE8icbcbBWrVrFsmXL+n1MU1uU8nZ35x1LpTL8+aXXmFhXxiXvXZKVzMDhzqlUWxXMie7doRrIaw92a2xze4xksnCCue6vfVfWaYm9WH2+Ja0M1kBf91wQT6SyntGa7bnU8km26h7wm0wZ736i2WDl03t/KOLxOBs2bOh136je1ZRSNwCzgQ9rrTPAbmCSUsrj7PcA9c72oe4TAxTLkQSHlev3cqgtxsWnzsiJ6R38fpOqMveXBBsNFaV+Jo0tp6rcXzBjlzymScBvUlsVZNLYMiaOKaeizF/wQVy+sRNxyikJSjdeLimR8XF5Z9TubEqpHwDLgEu11nEArfVBYC1wpfOwK4E1WuvGoe4bhaoUBMuyaG5zP8EhHE3y91d2MXdaDXMbal0tC9jjp6rL82+6keHwekzG1pQysa6MgD9/6+3xGFSV+5lQV8bkcRXUVgZlrE+O83lNJtSVUVkkP5zygXxm8s9oTT8yH/gm8DbwolIKYLvW+v3AZ4HblFLfAVqAa7odOtR94ig6IkmicffndXrslZ3EEikuPm2m20UBIBjI7+lGhqO0xEfA76GlI0Z7KJkXg9FNwyAY8NgrLgR9eDwmu7bFJes0j3hMg7E1JXg8hqzR6jLTlGlH8tFoZa2+iZ2g0Nu+TcCJ2dwn+pdKZ2hpd38Fh4PNEV5Yv4+TF0xkYl2Zu4XBDgpqK/N/upHh8HhMZwqOJM3tMWLx3EsIMAx7tY3yEj9lQa+0IBSAroxWr5PR6nJPQbHyeQ2ZADsPyR2wCLV2xEim3B/c/tDKbfi8Jued3OB2UQAoL/NREijO1rieSoM+An4vrR0x2kKJnPhi9XjsOd/Kg35KgjJhbyGqLHPWaG2WNVrdIOPj8pO8akUmGk/SHnI/5X/z7hY2bGviwlOmU1Hq/vgYj8egurxwM5iHwmParSSlQS/NbTGiLrTOmYZBIGBS4cz55i2isYvFqjToY+IYWaPVDdK6nZ/kVSsilmXR3B53fexTJmPxl+e2UVMR4LQlk10tS6fq8oB0KfShJOBj4hgvbaE4LR3xEW+dMwzwek0qnHFv8uVSfDozWhtbIq5PVl4sTNMgKOur5iW5QxaRjkiCWA7cFF/feIC9jSE+ev48fF73W1gCfpNKaY3rl2ka1FQGKQ16OdQWG5EF0D3OclnlJdJ1KuyM1vF1ZTS1RmmXNVpHnN9n4vNKIJePJJArEql0hua2GG6POokn0/ztxe1Mm1DBkjljXS6NM91IRTAn5q/LBwG/l/oxZV2tc+lhjmPq6jot8VNaIl2n4nBdGa1eg9Z2yWgdSTI+Ln/JK1ckWjtiOTF4+OnXd9MeTvDxC47JiRaXkoCX8hJJcBgMwzCorghSGvTR1BYjEk0O6gdCV9dpiY+yEuk6Ff0zDIO6ym5rtOZA4k0hCvjkc5iv5JUrArmS4NAaivPUqt0smTOWhvoqt4uDaRjUFPl0I8Ph93mYUFdKR9he5utoPxQ6u07LSvyUBLyY0goqBkEyWkeOaRoEZHxc3pJArsDZCQ4x1xMcAB5+YTuWZXHhKTPcLgoAFTLdyLAZhkFleYBgwEtze4xwNHlY95dpQMDvodzJOs2FMZEif0lG68gI+Ez5bOYxCeQKmGEYdIQTRGPuT+q6+2AHr208wJnLplBb5f5i9B6PQXWFJDhki906V0ZHJEFTWxQDw15tocRHULpORRZJRmv2BWV8XF6TV6+AmR67lcRtlmXx4HNbKSvxcdYJU90uDgA1FQHJ0BoBFaVOt6lhSNepGDGS0Zo9BjI+Lt9JW2oBiyTIibEkG7Y1sWVPG+ef1JATmVEBv0lFmbTGjRSvx5QgToy4zozWmqoAMsx16AwZH5f3JJArUJFYkraQ+61xqXSGh57fxvjaUk5aONHt4sh0I0IUkM6M1rE1JfLjYYhkfFz+k1evAGUyFi3tMVIp98fGvbB+H42tUS45dUZOBE+lQW9OLAkmhMieyrIAE+pKCUry0qDlQi+JGB4J5ApQRyThyrqYPYVjSf7+8k7U1BrmNtS6XRxnuhH3Ey2EENlXGvQxpipAwC9fawNlGLK+aiGQd3yBSabStORAggPA31/ZSTSR4uLTZuTEXG2V5ZJBKUQhy6QSTKgrpzQon/OBMAwZH1cI5N1eYFo64q4lOHznlhfpiBw58fDNf17P9z+93IUSvcvrMagql9Y4IQpdV0ZrW5SOUML1ZQlzWcBvyrJ4BUBewQISiSXpcDEVv7cgrr/to6m6IiADeoUoEh7TYGy1ZLQejYyPKwzyzVYgMhl7BYccWMAh5wT8JpUy3YgQRcUwDGorSxhbUyoZrb0wDAjK/HEFQQK5AtEejhPLgQSHXGMYUFMhUxMIUawqy/xMqCvF65F7QHemjI8rGBLIFYBkKk1LR9zVMqx5+6Cr1+9LadBHealMSSBEMetco1UyWt8V8Jt4ZHxcQZB21QLQ0h4n7VKCQyyR4v5ntvDqWwdcuX5/TNOgplK6VIUQskZrTzLnXuGQcDzPRaJJOiLuJDjs3N/ODXeu4rWNBzjnxKl9tnxVuNQiVlkm040IId7l85pMqCujstxPMXe0GgYEpVu1YMi3XB7LZCyaO0Y/wSGTsXjitV089vIOqsoDfO7yY5kxqYrzT54+ugXpRzDgk+lGhBBHMJ2MVq/HpKVIE8RMwyDgk0CuUEggl8fcSHBobotxx2Mb2b6vnaVqHJefOTsnU9irymW6ESFE7+yM1iBej8mh1iiZTHFFczI+rrDk3jewGJBEcvQTHFZvOsgfn3obC7j6vLksmzt+VK8/UMGAB78n43YxhBA5rrLMj9djcLA54tpE6m6Q8XGFRQK5PNXSERu1BIdoPMV9T29m1aaDNEys5Orz5lJXVTIq1x4se7qRIAd2y2BmIcTRdWa0HmyJEE8U/g9Ae31V6VYtJBLI5aFwNElolFZL2L6vjTse3URrR4zzTprGWSdMw5PDc7KVBn2UlcivTSHEwAX8XiaOKaexJUo46v5KNCPJNAxJAisw8mrmmXTGork9OuIDdNMZi8df2cnfX91JTUWQf/ngsTTUV43sRYfJNA1qZboRIcQQeD0m42tLOVTga7QGA56c/jEuBk8CuTzTHoqPePP/obYodz66iR372zlu3nguO2MWwRxMaOipqtxPQH5pCiGGyDQNxtWUFnRGaz7cy8XgyCuaRxLJNK2hkUtwsCyL1zcd5L6nN2MAHz1/HkvVuBG7XjZ5PQZV5dIaJ4QYvtrKID6vSWNrlEwBJUEYBjLtSAGSQC6PNLePXIJDNJbij0+9zZq3G5kxqYqPnDuX2sr8mYetxplKQAghsqGi1I/HNGhsiZJMFUYShGka0mtRgOQVzROhSHLEBuFu3dvKnY9uoi0U533LG3jvcVPzapH5YMBDRanf7WIIIQqMndFqcrAlMupzdo6EoF/GxxUiCeTyQDpj0dKR/QSHdDrDY6/s5InXdlFXGeTzH17CtAmV2b3ICDMMqK0I5lXgKYTIH36fhwl1ZQWR0ZqLk7eL4ZNXNQ+0jUCCQ2NrlDse2ciuAx2cOH8Cl54+My9T0stKfJTKdCNCiBHUmdHa1BalPU8zWmX+uMKVf9/cRSaeSGU1wcGyLF596wB/fmYzHtPk2vcdw7Fzxmbt/KPJNA1qKiTBQQgx8kzTYKyT0dqchxmtpmkQ8MlXfiGSVzXHNbfHs5Y1FYkluffJzazb3MisyVVcde5cairyJ6GhJ5luRAgx2moqg3jzMKM16PfIEJQCJd+COawjkiASy86YjC27W7nzsU20RxJceMp03rNsSl5/qH1eU6YbEUK4oqK0c43W/MlolfFxhUte2RyVTmdo7Rh+830qneGRl3bw9Ou7GVNTwhc+vIQp4yuyU0gX1VQEZLoRIYRrSgL5k9Eq4+MKmwRyOaotPPwEh9Zwiv+5Zw17DoY4acFELj19ZkFMBlkS8FBRJtONCCHclS8ZrTI+rrCNyiurlLoBuAxoABZqrTc42+cAtwF1QBNwjdZ683D2FYJ4IkVrR2LIx1uWxStvvsOfX2zG7/Py8Qvns2jWmCyW0D2GYY9RMYz87RYWQhSOwzJaw4mcTIIoCcj4uEI2Wn1TDwCnATt7bL8ZuElrPQe4Cfh1Fvblveb2OJnM0O4G4WiS//3rW9zzxNuMr/bx1auPK5ggDqC81EdpUKYbEULkjs6M1trKILn4GzMfp5YSAzcqr67WeiWAUqprm1JqHLAUONvZdDdwo1JqLGAMZZ/WunGEqzLihpPg8PauFu56bBOhaJKLT53B+NIOqgsoIcCebiR/s2yFEIUtFzNaZXxc4TOsUWwHVkrtAC7UWm9QSi0Dfq+1nt9t/1vA1djB2qD3aa1XD6Qcq1atagC2D79G2eXx+mhqTxKJDa5bNZ2xeO3tMOt3RKgu83Dm4krGVBZeq9W42jJ8RorRfM8KIcRgGIaB4fHT1B4nOsh7+UgIBnyMq/KRSqXcLorIjunLli3b0X1DUbe3LliwgEAgd1qsmtqilLcPbvLfA81hbn9kE3sbI5yyqJ6LT52B30lo2LhpI/PmzhuJoo46n9dk8rhyPAPMVF21ahXLli0b4VLlrmKuv9S9OOsOuVX/RDI9qhmtfd3vK8p8jK8tG5UyuCmXXvuREI/H2bBhQ6/73Jy/YTcwSSnlAXD+rXe2D3Vf3oonUrSFBv7rzbIsXli/j/++azWtoTj/cNF8Lj9zdlcQV0gMoKYyMOAgTggh3NaZ0Vrm8hKCMj6u8Ln2zai1PgisBa50Nl0JrNFaNw513ygVPessy6K5PTbgBIdQJMGtD73Jn57azIz6Kr569TIWzCychIaeggEPFaUy3YgQIr90ZrRWlftdSYKQ8XHFYbSmH/kF8AFgAvCEUqrJGeP2WeA2pdR3gBbgmm6HDXVf3umIJInEBjZ+YdPOZu56TBOJJ7n0tJmcumQSZi6mSWWJTDcihMhnbq7R6jGNgpg7VPRvtLJWPw98vpftm4AT+zhmSPvyTTqdoWUAKzgkUxn++sI2nluzl/G1pXz2/QupH1s+OoV0UUWpX6YbEULkPTcyWkuDXvkRXASk89xlraE4yWT/KzjsbwpzxyMb2XcozIrF9Vx06gz83sL/leXxGFRX5E4yihBCDMdor9Eq4+OKg7zKLoodJcHBsixWrtvHQ89vIxDw8KlLFnDM9LpRLKG7qsr9BZm8IYQoXqO1Rqsp4+OKhgRyLrEsi+a2vhMcOsIJ7n5cs3FHM/MaarnybFVU64v6fCZVZdIaJ4QoPKOxRqvpMeSHcJGQQM4lHZEE0T4SHN7a3sTdj2ti8RQfOGMWKxbXF9U4BwOoqQjKdCNCiILVmdHa3B6lLZT9NVpLAzI+rlhIIOeCVDpDS3ucnp/bRCrNX1du5/m1e5k4pox/+sBiJo4p/IkcewoGvVSUSoKDEKKwmabBmGono7UtxhCX2O6VjI8rHvJKu6C1I3bEQNd9h0Lc8cgm9jeFOW3JJC48ZQY+b/G1SJkG1FQE5JekEKJoVDs9ENnKaDUNQ8bHFREJ5EZZNJ6kPfTumIiMZfH82r38deU2SgJePn3pQuY11LpYQneVl8l0I0KI4tOV0doSPepMBkfj8cr4uGIigdwosldwiJNxBkO0heP84e+aTTtbOGa6ndBQXsQrGHg8BtXlkuAghChOJQEfE+tMGlsiRIeR0VoS8EivRhGRQG4UdYQTxJwEhw3bDvGHx98mkUhz+Xtms3zRxKL/4FWVB+RXpBCiqHVmtB4cRkarjI8rLvJqj5JkKkNze4x4Ms2Dz2/jhfX7qB9TxjUfnMf42uJLaOjJ7zepktY4IYTAM4yMVtOQZbmKjQRyo6QtFGPn/g5uf3QjB5ojnLF0Mhcsn463CBMaejIMZ7oRs7hbJIUQotNQM1o9XoOAtMgVFXm1R0E4muCBZ7fx15XbKCvx8dkPLEJNrXG7WDmjJOClvEQSHIQQoqfqiiBej4fG1gjpAWS0lgbka73YyCueZR+97lFaO+K97lswo44Pn60kaOnGNAxqKmW6ESGE6Et5qQ+Pp3RAGa0yPq74SL9elvUVxAF84qL5EsT1UFHmoyQgz4kQQvTHzmgtoyTQ9/g30zDwy/xxRUcCuVEkrU6H83gMqiskwUEIIQaiM6O1vI+Vb7w+SXQoRhLICddUlwfweeWmI4QQA+XxmIyrKaW6wk/PtoES6VYtShLIjaLqCj8Bv4lpGhR721zAb1Ip040IIcSgdWa01lUF6Uz2NwxDxscVKXnVR9GY6lIAkqk08USGRDJFLJEinsiQsaxBzRWUzwzDWVtQphsRQogh657R6vV4ZHxckZJALsuqKwK9Jjx0Hwvm83qcLkV7nEM6YxFPpEgk08QSKWLxNJmMNeB5g/JNScBLRREvRSaEENnSmdG6tzQg4+OKlARyWXb7decN+hiPaVAa9HUtFm9ZFolkmngyTTxhB3fJlEWmACK7zulGhBBCZEdJwEdF0O1SCLdIIJeDDMOemTvg94Kzeldnd2zc6Y5N5Gl3rEw3IoQQ2ZdKDW1dVpH/JJDLE53dseWd3bHpDPFkuqs7NhpPY+V4d6xMNyKEEEJklwRyecrjMSn1mId1x8aTaRIJu0s2mkjh8+bWy1tTIdONCCGEENmUW9/0Ysg6U8+7p583vRNgfF1pV6udm92xAb9JZZm0xgkhhBDZJIFcAcukkodlh6bTGWKd3bHxFLGEnR070oGdYUBNRQmmTDcihBBCZJUEckXE4zEp85iUBX1QAZmMRSJld8fGkvY4u3Q6+9mxpUFvn0vKCCGEEGLoJJArYqb5bndsJXa3Z+e0J52tdonk8LpjTdOgplLy4oUQQoiRIIGcOIzf58HfbVLJVDpjJ1CkhtYdW1nmk2VjhBBCiBEi37CiX16PibfEpJR3u2PjSbulLt7ZHZuyyPQS2Xk9BlXl0honhBBCjBQJ5MSgmKZBScBHSQBwumM7u2LjCbvFLpHMYFkW1RUBfF7T1fIKIYQQhUwCOTFsAZ+HgM/TlSHb2R0bDMjbSwghhBhJ8k0rsq6zO1YIIYQQI0u+bYUQQggh8pQEckIIIYQQeUoCOSGEEEKIPCWBnBBCCCFEnpJATgghhBAiT0kgJ4QQQgiRpySQE0IIIYTIU3k9j5xSag5wG1AHNAHXaK03u1sqIYQQQojRke8tcjcDN2mt5wA3Ab92uTxCCCGEEKMmb1vklFLjgKXA2c6mu4EblVJjtdaNRzncA5BIJEawhLkhHo+7XQTXFHPdobjrL3UvXsVc/2KuOxR2/bvFK56e+wzLska3NFmilFoG/F5rPb/btreAq7XWq/s7dtWqVSuA50e4iEIIIYQQ2XTqsmXLVnbfkLctcsP0GnAqsB9Iu1wWIYQQQoj+eICJ2PHLYfK5RW4c8DZQp7VOK6U82AkPswfQtSqEEEIIkffyNtlBa30QWAtc6Wy6ElgjQZwQQgghikXetsgBKKXmYk8/UgO0YE8/ot0tlRBCCCHE6MjrQE4IIYQQopjlbdeqEEIIIUSxk0BOCCGEECJPSSAnhBBCCJGnJJATQgghhMhTxTohcF5TStUBtwMzgTiwBfiM1rpRKTUHO5O3DntevWu01pud4/rcl4+UUt8FrgMWaq03FEvdlVJB4GfAWUAMeElr/eliqL9S6kLgPwAD+4fodVrrPxdq3ZVSNwCXAQ0473Nn+5Dqm0/PRW917+/e5xxTsHXvsf+we5+zrSDqDv2+73u99zn7Cqb+gyUtcvnJAq7XWiut9SJgK/BjZ9/NwE1a6znATcCvux3X3768opRaCpwE7Oq2uSjqDlyPfRObo7VeCHzb2V7Q9VdKGdhf4h/VWh8LXA3cppQyKdy6PwCcBuzssX2o9c2n5+IBjqx7f/c+KOy6A33e+6Bw6g5917+vex8UVv0HRaYfKQBKqcuAfwSuoo/VLrBbMApiJQylVAB4Bru+TwMXAgcpjrqXA3uAyVrrULftfa50QoHU3wnkDgEXa61fUEqdBvwWWEHh130HcKHTKjWk17q/fbn8XHSvey/7LgP+UWt9ViF+BnrWvbd733DeE7lcdzjifd/rvc95XEHWf6CkRS7POa0R/wg8CEwB9mqt0wDOv/uc7f3tyzffB+7QWm/vtq1Y6j4T+yb0XaXU60qpZ5RSKyiC+mutLeBDwF+UUjuxf7VfSxHUvYeh1regnose9z4ojrr3du+D4qh7X/c+KI7690kCufz3SyAE3Oh2QUaDUupk4HjgV26XxSVeYAb2cnTHAV8H/gyUu1qqUaCU8gLfAC7RWk8DLgLuoQjqLnol977i0uu9TylV6W6x3CeBXB5zBoTOBj6stc4Au4FJTtMxzr/1zvb+9uWT04G5wHan2X0y8Bj2r7VCrzvYY0ZSwN0AWutXsLsboxR+/Y8F6rXWLwA4/4axx8wUet27G+rnvGCei17ufVD4de/13qeUOofCrzv0fe+bQ3HUv08SyOUppdQPgGXApVrrOIDW+iCwFrjSediV2L9eGvvbN5rlHi6t9Y+11vVa6watdQP2mIlztdb3UuB1B9BaH8IeG3M2dGVjdY4PWUth138PMFkppQCUUvOACcBmCr/uXYb6OS+U56K3ex8U/v2vn3vf3wu97tDvvW9LMdS/P5LskIeUUvOBDdhf3lFn83at9fuVUnOx06xrgBbsNGvtHNfnvnzVYzBsUdRdKTUDuBU7lT4J/LvW+pFiqL9S6iPAvwGdrTDf1Vo/UKh1V0r9AvgAdsB6CGjSWs8fan3z6bnore7YYyR7vfc5xxRs3bXW83s8ZgeHJ0IURN2h3/d9r/c+55iCqf9gSSAnhBBCCJGnpGtVCCGEECJPSSAnhBBCCJGnJJATQgghhMhTEsgJIYQQQuQpCeSEEEIIIfKUBHJCiKKilBqvlHpOKdWhlPpvl8rwiFLqWjeuLYQoLF63CyCEEKPs09hzU1U667ceRin1f8AerfW3RqoAWuvzR+rcfXHmHfuk1vqJ0b62EGLkSIucEKLYTAPe6i2IGwhnzVchhMgJMiGwEGJEOC1ANwLXYAdPjwLXaq1jSqmPYbcOrej2eAuYrbXe4rSKRYDpwKnAOuAy7FUdrgUOAFdqrdf0ce3lwP9gr8P4NvCvWusXnfN+BLCABPYyT090O+7TwE3d9j+ttb7Iqcv/c45VQBnwFeBT2MsE7caeZf5+5zwfAz4JvAz8A9AK/FO3WeifAe7QWv92AI+djj0r/RLgFUADVVrrq3up9xjg/4AV2KtfvIm9RudtTtnjQBr4vtb6eqXUScBPgWOw17L8V631M93K+BLwXqfOzwAf11o3K6WCwG+B8wEP9jJpF2qtD/T2egghRo60yAkhRtKHgPOwA7JFwMcGeey3gDHYAchLwGrn7z9hByBHUErVAn8DfoG9lM9Pgb8ppeq01h8D7gSu11qX9+xm1Frf0mP/Rd12XwlcAFRrrVPAVuwgswr4HnCHUmpit8efiB10jQGuB36nlDL6qGt/j70LeNWpy3XAR/s4B8CXsdfgHAuMB74JWFrrjwK7gIucel2vlJrkPE//CdRiB6b3KaXGdjvfNcAnsBcZT2E/p2AH01XAFKdcn+XdJbOEEKNIugiEECPpF1rrfQBKqYeAYwdx7P1a61XOsfdjt1L93vn7HuBzfRx3AbBZa3278/fdSqnPAxdht1YN1S+01rs7/9Ba/7HbvnuUUt8ATgD+4mzbqbX+jVPe24BfYQdX7/Ry7l4fq5TyA8cD79VaJ4CVSqkH+yljEpgITNNabwGe7+exVwMPa60fdv5+XCn1OvA+7BY8gNu7reX5bWCtk6SRxA7gZmmt1wOr+rmOEGIESSAnhBhJ3YOWCHbLzkB176aL9vJ3eR/H1WN3E3a3E5g0iGv3Znf3P5RS1wBfAhqcTeXYLWqduuqutY4opTof05u+HjsGaNZaR3qUY0of5/kJdqvd351z3KK1/nEfj50GfFAp1b3V0Qc83eNanXY6+8cAtztl+INSqhq4A7trOdnHtYQQI0QCOSGEG8JAaecfSqkJWTz3Puwgpbup2GP0BqKvgcNd25VS04DfYI8fe0lrnVZKrQX66jodqv1ArVKqtFsw11cQh9a6A7t79ctKqfnA00qp17TWT3JkvXZjt7h9qp/rd7/WVOyWuENa6zR2d/L3lFINwMPYXcO/G3jVhBDZIIGcEMIN64D5SqljgU3YrUjZ8jDwS6XUVcC92EkSxwB/HeDxB4AZR3lMGXZg1AiglPo4sGBIpe2H1nqn0915nVLqW8Ay7C7ih3p7vFLqQuzncyvQjp3YkHZ296zXHcBrSqlzgSewW9tOArZorfc4j7laKfV7YAfwfeBPTtD6HuwpXN5yrpPsdh0hxCiSZAchxKjTWr+NHRg8gZ3xuDKL524CLsRumWoCvoadUXlogKf4HXCMUqpVKfVAH9d4C/hv7ASMA8BC4IVhFr0vHwFOxq7LfwL3YCd/9GY29nMacsr2q84sVOBHwLecen3FGe93CXZCRCN2C91XOfx74XbscYXvAEHg8872CdgJJ+3ARuBZ7MBQCDHKZPoRIYTII06ixyat9XdH+DrP4EyRMpLXEUIMj3StCiFEDlNKHQ80A9uBc7Bb0fpKYBBCFBkJ5IQQIrdNAP6MPd3HHuAf+5oIWQhRfKRrVQghhBAiT0mygxBCCCFEnpJATgghhBAiT0kgJ4QQQgiRpySQE0IIIYTIUxLICSGEEELkKQnkhBBCCCHy1P8HQzE1knA+MHkAAAAASUVORK5CYII=\n", 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" ] From 1130307122f00bc762ab148f15b3b1cf278ca3dc Mon Sep 17 00:00:00 2001 From: ce-hao Date: Fri, 3 Sep 2021 21:29:25 +0800 Subject: [PATCH 35/36] yes --- hw1/{ => cs285}/report_plots.py | 38 +++++++++++++++----------------- hw1/plots/Ant.png | Bin 25510 -> 27349 bytes hw1/plots/Hooper.png | Bin 26445 -> 28730 bytes hw1/plots/figure-1_varying.png | Bin 0 -> 29227 bytes hw1/plots/figure-2_Ant.png | Bin 0 -> 27349 bytes hw1/plots/figure-2_Hooper.png | Bin 0 -> 28730 bytes hw1/repot_plots.ipynb | 20 +++++++++++++++++ 7 files changed, 38 insertions(+), 20 deletions(-) rename hw1/{ => cs285}/report_plots.py (82%) create mode 100644 hw1/plots/figure-1_varying.png create mode 100644 hw1/plots/figure-2_Ant.png create mode 100644 hw1/plots/figure-2_Hooper.png diff --git a/hw1/report_plots.py b/hw1/cs285/report_plots.py similarity index 82% rename from hw1/report_plots.py rename to hw1/cs285/report_plots.py index b20b5f7e..acee3126 100644 --- a/hw1/report_plots.py +++ b/hw1/cs285/report_plots.py @@ -5,8 +5,6 @@ import pandas as pd import os -# manually add data of Ant and Halfcheeta - def plot_mean_std(ax, iterations, mean, std, mean_expert, mean_bc): mean = np.array(mean) @@ -35,7 +33,7 @@ def set_plot_env(iterations, mean, std, mean_expert, mean_bc, exp_name): exp_dir = 'plots/' if not os.path.exists(exp_dir): os.makedirs(exp_dir) - plt.savefig(fname=exp_dir + exp_name + '.png', format='png') + plt.savefig(fname=exp_dir + 'figure-2_' + exp_name + '.png', format='png') def plot_DAgger(mean, std, mean_expert, exp_name): iterations = np.arange(len(mean)) @@ -66,7 +64,7 @@ def plot_changing_ep(ep_len, mean_len, std_len): exp_dir = 'plots/' if not os.path.exists(exp_dir): os.makedirs(exp_dir) - plt.savefig(fname=exp_dir + 'varying' + '.png', format='png') + plt.savefig(fname=exp_dir + 'figure-1_varying_train_step' + '.png', format='png') class Ant_exp: @@ -77,32 +75,32 @@ class Ant_exp: ep_len = [100, 300, 500, 700, 990, 1100, 1400, 1500, 1700] mean_len = [567, 1505, 3849, 3296, 3774, 3570, 2227, 4236, 4249] std_len = [7, 1250, 1299, 1604, 1363, 1392, 1740, 1151, 901] - mean_exp_len = [] - std_exp_len = [] class Hooper_exp: - mean = [ 523., 1700., 2453., 3763., 3778., 3790., 3388., 3771., 3537., 3788.] std = [ 75., 607., 725., 4., 3., 4., 671., 3., 160., 3.] mean_expert = 3779 if __name__ == "__main__": - # no_iter = np.arange(10) - # mean = np.linspace(0.9, 1.1, 10) - # std = np.linspace(0, 0.2, 10) - # mean_expert = np.ones(10) * 1.3 - # mean_bc = np.ones(10) * 0.9 + # figure 1 exp = Ant_exp - # exp = Hooper_exp - - # mean = exp.mean - # std = exp.std - # mean_expert = exp.mean_expert - # plot_DAgger(mean, std, mean_expert, 'Hooper') - mean_len = exp.mean_len std_len = exp.std_len ep_len = exp.ep_len - plot_changing_ep(ep_len, mean_len, std_len) \ No newline at end of file + plot_changing_ep(ep_len, mean_len, std_len) + + # figure 2-1 + exp = Ant_exp + mean = exp.mean + std = exp.std + mean_expert = exp.mean_expert + plot_DAgger(mean, std, mean_expert, 'Ant') + + # figure 2-2 + exp = Hooper_exp + mean = exp.mean + std = exp.std + mean_expert = exp.mean_expert + plot_DAgger(mean, std, mean_expert, 'Hooper') \ No newline at end of file diff --git a/hw1/plots/Ant.png b/hw1/plots/Ant.png index 9ab1542d75b58741f8612c5e816347cda5c35fd7..6632f322b15b3de134dcad286ae54cb1b100ab1f 100644 GIT binary patch literal 27349 zcmd43by$_%*Dku~kdSVWl9ZP2E@>&Hk&S5yi0rJMg)zQ|$)z;edh5H9*7i$N5ZZ_~A>kBJaS4S5?c6Ph} zbpe}$vn6|9N<#*C6EsJ8Jr@Y%86NBp93(A+7y_}8RFIa?^2|6~^6=66FfV$t|0mUO zTqxM|#dC4EUBXFYA~g4unR+oD_qn=Kw&v{ePUuehl)-*eLR?PnbD5_ZKI0s;^0 zf_SR`|Gfo1A__L;?~C0DPOl3qR=sMv$u6`XW4Ur0J7c*yl*l;h?cnazwHhy{-<=K% z9S3_6i$2}W{NnKY=V`mtM9{G0{;qi@UC4ti6q78px|-|Pk(ihm6&auX!7hh)_jK92 z_{_|u{Wvm(U$iultYBmCuY@U8e~ZW}D4-!@Q!cK#+47Q!-tExpvJ#f+*D+_uQDJ#) ze4?d6M*`c6u5^2Qt6OV@Q{HxG6HWRWoiYw(q28`*h;Jpj%Bti2h^<;N{B2^v(}v@ArpNlt z=|-EKR47Krc)pU;Zhp%8xDxxNf&0yg31pe%`u2Q#L+95WfovRQ3~WCL2nci=9jSX_ 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+17,26 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", 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