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


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.

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The authors would like to express our thanks for the support from the following research grants: 2018AAA0102004, NSFC-61625201, NSFC-61527804.

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Correspondence to Diqi Chen.

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Chen, D., Wang, Y. & Gao, W. Combining a gradient-based method and an evolution strategy for multi-objective reinforcement learning. Appl Intell 50, 3301–3317 (2020).

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  • Multi-objective reinforcement learning
  • Multi-policy reinforcement learning
  • Pareto frontier
  • Sampling efficiency