Policy Optimization by Looking Ahead for Model-based Offline Reinforcement Learning

Offline reinforcement learning (RL) aims to optimize a policy, based on pre-collected data, to maximize the cumulative rewards after performing a sequence of actions. Existing approaches learn a value function from historical data and then guide the updating of the policy parameters by maximizing the value function at a single time. Driven by the gap between maximizing the cumulative rewards of RL and the greedy strategy of existing methods, we propose an approach of policy optimization by looking ahead (POLA) to mitigate the gap. Concretely, we optimize the policy on both current and future states where the future states are predicted by a transition model. A trajectory contains numerous actions before the task is done. Performing the best action at each time does not mean an optimal trajectory in the end. We need to allow sub-optimal or negative actions occasionally. But existing methods focus on generating the optimal action at each time according to the maximizing Q-value principle. This motivates our looking ahead approach. Besides, hidden confounding factors may affect the decision making process. To that end, we incorporate the correlations among dimensions of the state into the policy, providing more information about the environment for the policy to make decisions. Empirical results on the Mujoco dataset show the effectiveness of the proposed approach.