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A promising paradigm for offline reinforcement learning (RL) is to constrainthe learned policy to stay close to the dataset behaviors, known as policyconstraint offline RL. However, existing works heavily rely on the purity ofthe data, exhibiting performance degradation or even catastrophic failure whenlearning from contaminated datasets containing impure trajectories of diverselevels. e.g., expert level, medium level, etc., while offline contaminated datalogs exist commonly in the real world. To mitigate this, we first introducegradient penalty over the learned value function to tackle the explodingQ-functions. We then relax the closeness constraints towards non-optimalactions with critic weighted constraint relaxation. Experimental results showthat the proposed techniques effectively tame the non-optimal trajectories forpolicy constraint offline RL methods, evaluated on a set of contaminated D4RLMujoco and Adroit datasets.

Robust Offline Reinforcement Learning with Gradient Penalty and Constraint Relaxation