A Bayesian reinforcement learning approach in markov games for computing near-optimal policies

Author:

Clempner Julio B.ORCID

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Artificial Intelligence

Reference38 articles.

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2. Asiain, E., Clempner, J.B., Poznyak, A.S.: Controller exploitation-exploration: A reinforcement learning architecture. Soft Computing 23(11), 3591–3604 (2019)

3. Asmuth J., Li L., Littman M., Nouri A., Wingate D.: A bayesian sampling approach to exploration in reinforcement learning. In: UAI ’09: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, AUAI Press, Montreal, Quebec, Canada, pp 19–26 (2009)

4. Bellman R.: (1961) Adaptive Control Processes: A Guided Tour. Princeton University Press

5. Besson, R., Le Pennec, E.: Allassonnière S,: Learning from both experts and data. Entropy 21(12), 1208 (2019). https://doi.org/10.3390/e21121208

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1. Joint Observer and Mechanism Design;Optimization and Games for Controllable Markov Chains;2023-12-14

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