On the Importance of the Policy Structure in Offline Reinforcement Learning

Offline reinforcement learning (RL) has attracted a great deal of attention recently as an approach to utilizing past experience to learn a policy. Recent studies have reported the challenges of offline RL, such as estimating the values of actions that are out of the data distribution. To mitigate the issues of offline RL, we propose an algorithm that leverages a mixture of deterministic policies. With our framework, the state-action space is divided by learning discrete latent variables, and sub-policies corresponding to each region are trained. The proposed algorithm, which we call Value-Weighted Variational Auto-Encoder (V2AE), is derived by considering the variational lower bound of the offline RL objective function. The aim of this work is to shed lights on the importance on the policy structure in offline RL. We show empirically that the use of the proposed mixture policy can reduce the accumulation of the approximation error in offline RL, which was reported in previous studies. Experimental results also indicate that introducing the policy structure improves the performance on tasks with D4RL benchmarking datasets.