Abstract:

This work presents a new approach that allows the use of cases in
a case base as heuristics to speed up Reinforcement Learning algorithms, combining
Case Based Reasoning (CBR) and Reinforcement Learning (RL) techniques.
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. CB-HARL is a subset of RL that
makes use of a heuristic function derived from a case base, in a Case Based Reasoning
manner. An algorithm that incorporates CBR techniques into the Heuristically
Accelerated Q–Learning is also proposed. Empirical evaluations were conducted
in a simulator for the RoboCup Four-Legged Soccer Competition, and
results obtained shows that using CB-HARL, the agents learn faster than using
either RL or HARL methods.

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