Active Exploration Planning in Reinforcement Learning for Inverted Pendulum System Control

Abstract

Reinforcement learning method usually require that all actions be tried in all state infinitely often for convergence. Such algorithms are impractical to be applied to sophisticated systems due to its low learning efficiency. This paper analyses the problem of limit cycles exist in reinforcement learning for inverted pendulum system control and proposed Active exploration planning policy. The algorithm sufficiently makes use of characteristics, active detects limit cycles and plan exploration instead by random exploration. The algorithm action improved the learning efficiency by selecting sub-optimal control action and limiting the exploration to the controllable areas, which can make the number of trials not grow exponentially with the state space. Simulation results for the control of single and double inverted pendulum are presented to show effectiveness of the proposed algorithm.