Optmiziations to run faster #76
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| for t_idx in self.t_idxes: | ||
| tm = self.get_teammate_from_idx(t_idx) | ||
| if type(tm) != CustomAgent: | ||
| # if not isinstance(tm, CustomAgent): |
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I think we are good to not have this comment.
| HOW_LONG=20 | ||
| NUM_OF_CKPOINTS=40 | ||
| LAYOUT_NAMES="c4_v2" | ||
| EXP_DIR=${LAYOUT_NAMES} |
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Should EXP_DIR be ${LAYOUT_NAMES}/${NUM_PLAYER}, instead?
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This is just an experiment that I decided to run this way. For the final experiments we can set them be layout_names/num_player
| HOW_LONG=20 | ||
| NUM_OF_CKPOINTS=40 | ||
| LAYOUT_NAMES="counter_circuit,coordination_ring,cramped_room,asymmetric_advantages,forced_coordination" | ||
| EXP_DIR="Classic/$NUM_PLAYERS" # When quick_test=True this will be overwritten to "Test/$EXP_DIR" |
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I suppose we want to save agents under a directory, mentioning $NUM_PLAYERS, like this one. Although we have it in this scripts, many scripts do not have EXP_DIR mentioning $NUM_PLAYERS.
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The directory of best_baseline_experiment is already executed so I am not going to change their scripts. Whenever we add a new script we can follow this template
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| def best_EGO(args, add_adv=False) -> None: | ||
| '''for a very specifric experimetn: only for 2 players:: ignore this''' |
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a little typo here. experimetn
Jmiceli17
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Jmiceli17
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Awesome! Just a few small questions
| self.agent = agent | ||
| self.p_idx = p_idx | ||
| self.env.set_teammates(teammates) | ||
| self.env.set_teammates(teamtype='run_type') |
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set_teammates just checks if teamtype is set right? I think it'd be better to have teamtype=True here then so we don't think there's something special about the 'run_type' string
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what's the difference between this and sandbox/profile_analyze.py? do we need both?
| print("Training agent: " + self.name + ", for experiment: " + experiment_name) | ||
| self.print_tc_helper(self.teammates_collection, "Train TC") | ||
| self.print_tc_helper(self.eval_teammates_collection, "Eval TC") | ||
| self.print_tc_helper(self.eval_envs[0].teammates_collection[TeammatesCollection.EVAL], "Eval TC") |
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Are the teammate collections are always going to be uniform?
This PR sets the default computation device to CPU and enables compatibility with SubProcEnv, which allows the code to leverage multiple processes for parallel execution. To support this, the logic for teammate_collection has to be moved to base_overcooked_env.
Currently, the default is configured not to use multiple processes because of memory issues (the higher the n_envs the more memory). Until that is resolved, we continue to use DummyVecEnv by default.