Abstract: Offline reinforcement learning,in which the agent learns from a fixed dataset without interacting with the environment,is a current hot spot in the field of reinforcement learning.Many offline reinforcement learning algorithms try to regularize value function to force the agent choose actions in the given dataset.The conservative Q-learning(CQL) algorithm avoids this problem by assigning a lower value to the OOD(out of distribution) state-action pairs through the value function regularization.However,the algorithm is too conservative to recognize the state-action pairs outside the distribution precisely,and therefore it is difficult to learn the optimal policy.To address this problem,the uncertainty-weighted conservative Q-learning algorithm(UWCQL) is proposed by introducing an uncertainty mechanism during training.The UWCQL adds uncertainty weight to the CQL regularization term,assigns higher conservative weight to actions with high uncertainty to ensure that the algorithm can more effectively train the agent to choose proper state-action pairs in the dataset.The effectiveness of UWCQL is verified by applying it to the D4RL MuJoCo dataset,along with the best offline reinforcement learning algorithms,and the experimental results show that the UWCQL algorithm has better performance.

Key words: Offline reinforcement learning, Deep reinforcement learning, Reinforcement learning, Conservative Q-learning, Uncertainty