Heuristically Accelerated Q-Learning: A New Approach to Speed Up Reinforcement Learning

Reinaldo A. C. BianchiCarlos H. C. RibeiroAnna H. Reali Costa

This work presents a new algorithm, called Heuristically Accelerated Q?Learning (HAQL), that allows the use of heuristics to speed up the well-known Reinforcement Learning algorithm Q?learning. A heuristic function H that influences the choice of the actions characterizes the HAQL algorithm. The heuristic function is strongly associated with the policy: it indicates that an action must be taken instead of another. This work also proposes an automatic method for the extraction of the heuristic function H from the learning process, called Heuristic from Exploration. Finally, experimental results shows that even a very simple heuristic results in a significant enhancement of performance of the reinforcement learning algorithm.

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