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1 Journal of Artificial Intelligence and Soft Computing Research

1 2012

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Model-building adaptive critics for semi-Markov control

Gosavi A., Murray S., Hu J., Ghosh S.

Journal of Artificial Intelligence and Soft Computing Research

2012

Vol. 2, No. 1

43--58

Adaptive (or actor) critics are a class of reinforcement learning algorithms. Generally, in adaptive critics, one starts with randomized policies and gradually updates the probability of selecting actions until a deterministic policy is obtained. Classically, these algorithms have been studied for Markov decision processes under model-free updates. Algorithms that build the model are often more stable and require less training in comparison to their model-free counterparts. We propose a new model-building adaptive critic, which builds the model during the learning, for a discounted-reward semi-Markov decision process under some assumptions on the structure of the process. We illustrate the use of our algorithm with numerical results on a system with 10 states and a real-world case-study from management science.