• Corpus ID: 252780451

A Behavior Regularized Implicit Policy for Offline Reinforcement Learning

@inproceedings{Yang2022ABR,
  title={A Behavior Regularized Implicit Policy for Offline Reinforcement Learning},
  author={Shentao Yang and Zhendong Wang and Huangjie Zheng and Yihao Feng and Mingyuan Zhou},
  year={2022}
}
Offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment. The lack of environmental interactions makes the policy training vulnerable to state-action pairs far from the training dataset and prone to missing rewarding actions. For training more effective agents, we propose a framework that supports learning a flexible yet well-regularized fully-implicit policy. We further propose a simple modification to the classical policy-matching… 

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References

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