Autonomous Reinforcement Learning with Experience Replay for Humanoid Gait Optimization

摘要

This paper demonstrates application of Reinforcement Learning to optimization of control of a complex system in realistic setting that requires efficiency and autonomy of the learning algorithm. Namely, Actor-Critic with experience replay (which addresses efficiency), and the Fixed Point method for step-size estimation (which addresses autonomy) is applied here to approximately optimize humanoid robot gait. With complex dynamics and tens of continuous state and action variables, humanoid gait optimization represents a challenge for analytical synthesis of control. The presented algorithm learns a nimble gait within 80 minutes of training.