Unsupervised Basis Function Adaptation for Reinforcement Learning

no code implementations • 23 Mar 2017 • Edward Barker, Charl Ras

In this article we will: (a) informally discuss the potential advantages offered by such methods; (b) introduce a new algorithm based on such methods which adapts a state aggregation approximation architecture on-line and is designed for use in conjunction with SARSA; (c) provide theoretical results, in a policy evaluation setting, regarding this particular algorithm's complexity, convergence properties and potential to reduce VF error; and finally (d) test experimentally the extent to which this algorithm can improve performance given a number of different test problems.

reinforcement-learning Reinforcement Learning (RL)