Erl-re\(^2\): Efficient Evolutionary Reinforcement Learning With Shared State Representation And Individual Policy Representation

Abstract

Deep Reinforcement Learning (Deep RL) and Evolutionary Algorithms (EA) are two major paradigms of policy optimization with distinct learning principles, i.e., gradient-based v.s. gradient-free. An appealing research direction is integrating Deep RL and EA to devise new methods by fusing their complementary advantages. However, existing works on combining Deep RL and EA have two common drawbacks: 1) the RL agent and EA agents learn their policies individually, neglecting efficient sharing of useful common knowledge; 2) parameter-level policy optimization guarantees no semantic level of behavior evolution for the EA side. In this paper, we propose Evolutionary Reinforcement Learning with Two-scale State Representation and Policy Representation (ERL-Re\(^2\)), a novel solution to the aforementioned two drawbacks. The key idea of ERL-Re\(^2\) is two-scale representation: all EA and RL policies share the same nonlinear state representation while maintaining individual\} linear policy repres

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• arxiv keyhao2022erl

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