Behavior-regularized actor-critic methods achieve strong offline RL results with simple regularization, rendering many recent technical additions unnecessary.
Striving for simplicity in off-policy deep reinforcement learning.arXiv preprint arXiv:1907.04543
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Many batch RL algorithms underperform both online DQN and the behavioral policy on Atari; an adapted discrete-action BCQ outperforms the others tested.
Offline RL promises to extract high-utility policies from static datasets but faces fundamental challenges that current methods only partially address.
citing papers explorer
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Behavior Regularized Offline Reinforcement Learning
Behavior-regularized actor-critic methods achieve strong offline RL results with simple regularization, rendering many recent technical additions unnecessary.
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Benchmarking Batch Deep Reinforcement Learning Algorithms
Many batch RL algorithms underperform both online DQN and the behavioral policy on Atari; an adapted discrete-action BCQ outperforms the others tested.
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Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Offline RL promises to extract high-utility policies from static datasets but faces fundamental challenges that current methods only partially address.