Application of Evolutionary Reinforcement Learning (ERL) Approach in Control Domain: A Review

摘要

Evolutionary algorithms have come to take a centre stage in diverse areas spanning multiple applications. Reinforcement learning is a novel paradigm that has recently evolved as a major control technique. This paper presents a concise review on implementing reinforcement learning with evolutionary algorithms, e.g. genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), to several benchmark control problems, e.g. inverted pendulum, cart-pole problem, mobile robots. Some techniques have combined Q-Learning with evolutionary approaches to improve their performance. Others have used knowledge acquisition to obtain optimal fuzzy rule set and genetic reinforcement learning (GRL) for designing consequent parts of fuzzy systems. We also propose a Q-value-based GRL for fuzzy controller (QGRF) where evolution is performed after each trial in contrast to GA where many trials are required to be performed before evolution.