ERL-Re$^2$: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation

Abstract

Deep Reinforcement Learning (Deep RL) and Evolutionary Algorithm (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 representations. The state representation conveys expressive common features of the environment learned by all the agents collectively; the linear policy representation provides a favorable space for efficient policy optimization, where novel behavior-level crossover and mutation operations can be performed. Moreover, the linear policy representation allows convenient generalization of policy fitness with the help of Policy-extended Value Function Approximator (PeVFA), further improving the sample efficiency of fitness estimation. The experiments on a range of continuous control tasks show that ERL-Re 2 consistently outperforms strong baselines and achieves significant improvement over both its Deep RL and EA components.

Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

“…Recently, the integration of RL with meta-heuristics has been thoroughly explored. This collaboration is gaining traction because RL can also serve as a solver for optimization problems [42]. Therefore, this research focuses on the synergy between RL and meta-heuristic approaches, examining novel ways of cooperation between these two paradigms.…”