Combining a gradient-based method and an evolution strategy for multi-objective reinforcement learning

Abstract

Multi-objective reinforcement learning (MORL) algorithms aim to approximate the Pareto frontier uniformly in multi-objective decision making problems. In the scenario of deep reinforcement learning (RL), gradient-based methods are often adopted to learn deep policies/value functions due to the fast convergence speed, while pure gradient-based methods can not guarantee a uniformly approximated Pareto frontier. On the other side, evolution strategies straightly manipulate in the solution space to achieve a well-distributed Pareto frontier, but applying evolution strategies to optimize deep networks is still a challenging topic. To leverage the advantages of both kinds of methods, we propose a two-stage MORL framework combining a gradient-based method and an evolution strategy. First, an efficient multi-policy soft actor-critic algorithm is proposed to learn multiple policies collaboratively. The lower layers of all policy networks are shared. The first-stage learning can be regarded as representation learning. Secondly, the multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is applied to fine-tune policy-independent parameters to approach a dense and uniform estimation of the Pareto frontier. Experimental results on three benchmarks (Deep Sea Treasure, Adaptive Streaming, and Super Mario Bros) show the superiority of the proposed method.