Multi-Objective Reinforcement Learning Method for Acquiring All Pareto Optimal Policies Simultaneously

This paper studies multi-objective reinforcement learning problems in which an agent gains multiple rewards. In ordinary multi-objective reinforcement learning methods, only a single Pareto optimal policy is acquired by the scalarizing method which uses the weighted sum of the reward vector, and therefore different Pareto optimal policies are acquired by changing the weight vector and by performing the methods again. On the other hand, a method in which all Pareto optimal policies are acquired simultaneously is proposed for problems whose environment model is known. By using the idea of the method, we propose a method that acquires all Pareto optimal policies simultaneously for the multi-objective reinforcement learning problems whose environment model is unknown. Furthermore, we show theoretically and experimentally that the proposed method can find the Pareto optimal policies.