Code accompanying "The Importance of Pessimism in Fixed-Dataset Policy Optimization" by Buckman, Gelada, Bellemare. https://arxiv.org/abs/2009.06799

The tabular experiments can be replicated with simply experiment_performance_comparison.py and experiment_various_explorations.py.

Running the deep learning experiments requires quite a few more steps.

1. Use dqn_data_collection.py to train a standard DQN model to convergence, checkpointing the resulting near-optimal policy.
2. Use policy_data_collection.py, pointing at a specific checkpointed policy, and setting a specific epsilon, to collect a dataset with epsilon-greedy, and save it.
3. Use main.py to run an FDPO experiment.
4. Use compile_logs.py to extract the final performance numbers from the logs of many completed experiments, and compile them into a table suitable for plotting.