Decision Boundary Partitioning: Variable Resolution Model-Free Reinforcement Learning

Reynolds, Stuart
Decision Boundary Partitioning: Variable Resolution Model-Free Reinforcement Learning
ICML-2k ( gzipped Postscript - 241 KB )

Abstract: This paper presents a method to refine the resolution of a continuous state Q-function. Q-functions serve as an estimate of return for model-free reinforcement learning agents and are modified as a result of an agent's interaction with the environment. Traditional (non-adaptive) methods of approximating this function are bound by the parameters and resources with which they are initially provided. To overcome these limitations, the method presented here starts with a coarse discrete representation of the Q-function and refines those areas which are most important for the purposes of decision making.