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Using Case Based Heuristics to Speed up Reinforcement Learning

The aim of this work is to combine three successful AI techniques –Reinforcement Learning (RL), Heuristics Search and Case Based Reasoning (CBR)– creating a new algorithm that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging technique, the Heuristic Accelerated Reinforcement Learning (HARL), in which RL methods are accelerated by making use of heuristic information. Empirical evaluations were conducted in a simulator for the RoboCup Four-Legged Soccer Competition, and the results obtained show that using CB-HARL, the agents learn faster than using either RL or HARL methods.