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SBIA
2004
Springer

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

14 years 4 months ago
Heuristically Accelerated Q-Learning: A New Approach to Speed Up Reinforcement Learning
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.
Reinaldo A. C. Bianchi, Carlos H. C. Ribeiro, Anna
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where SBIA
Authors Reinaldo A. C. Bianchi, Carlos H. C. Ribeiro, Anna Helena Reali Costa
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