Multi-criteria expertness based cooperative method for SARSA and eligibility trace algorithms

摘要

Temporal difference and eligibility traces are of the most common approaches to solve reinforcement learning problems. However, except in the case of Q-learning, there are no studies about using these two approaches in a cooperative multi-agent learning setting. This paper addresses this shortcoming by using temporal difference and eligibility traces as the core learning method in multi-criteria expertness based cooperative learning (MCE). The experiments, performed on a sample maze world, show the results of an empirical study on temporal difference and eligibility trace methods in a MCE based cooperative learning setting.