Global Policy Construction in Modular Reinforcement Learning

Ruohan Zhang, Zhao Song, Dana H. Ballard

Abstract: We propose a modular reinforcement learning algorithm which decomposes a Markov decision process into independent modules. Each module is trained using Sarsa(λ). We introduce three algorithms for forming global policy from modules policies, and demonstrate our results using a 2D grid world.