Fuzzy Q-learning for generalization of reinforcement learning

Fuzzy Q-learning, introduced earlier by the author, is an extension of Q-learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental dynamic programming using a society of intelligent agents which are controlled at the top level by fuzzy Q-learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of fuzzy Q-learning through generalization of input space by using fuzzy rules and bridges the gap between Q-learning and rule based intelligent systems