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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Two Steps Reinforcement Learning in Continuous Reinforcement Learning Tasks
verfasst von : Iván López-Bueno, Javier García, Fernando Fernández
Erschienen in: Bio-Inspired Systems: Computational and Ambient Intelligence
Verlag: Springer Berlin Heidelberg
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Two steps reinforcement learning is a technique that combines an iterative refinement of a Q function estimator that can be used to obtains a state space discretization with classical reinforcement learning algorithms like Q-learning or Sarsa. However, the method requires a discrete reward function that permits learning an approximation of the Q function using classification algorithms. However, many domains have continuous reward functions that could only be tackled by discretizing the rewards. In this paper we propose solutions to this problem using discretization and regression methods. We demonstrate the usefulness of the resulting approach to improve the learning process in the Keepaway domain. We compare the obtained results with other techniques like VQQL and CMAC.