Leveraging Reinforcement Learning Techniques for Effective Policy Adoption and Validation

Author:

Kuang Nikki Lijing,Leung Clement H. C.

Publisher

Springer International Publishing

Reference24 articles.

1. Ziebart, B.D., Maas, A.L., A. Bagnell, J., Dey, A.K.: Maximum entropy inverse reinforcement learning. In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI 08), vol. 8, pp. 1433–1438 (2008)

2. Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)

3. Kearns, M., Singh, S.: Near-optimal reinforcement learning in polynomial time. In: International Conference on Machine Learning (1998)

4. Santana, H., Ramalho, G., Corruble, V., Ratitch, B.: Multi-agent patrolling with reinforcement learning. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 3, pp. 1122–1129. IEEE Computer Society (2004)

5. Brafman, R.I., Tennenholtz, M.: R-max-a general polynomial time algorithm for near-optimal reinforcement learning. J. Mach. Learn. Res. 3, 213–231 (2002)

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