The National Science Foundation Workshop on reinforcement learning

AI Magazine

Volumn 17, Issue 4, 1996, Pages 89-97

  Mahadevan, Sridhar ^a   Kaelbling, Leslie Pack ^b  

^a UNIVERSITY OF SOUTH FLORIDA   (United States)

^b BROWN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 17144419347     PISSN: 07384602     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (9)

1. Baird, L., and Klopf, H. 1993. Reinforcement Learning with High-Dimensional Continuous Actions, Technical Report WL-TR-93-1147, Wright Laboratory, Wright-Patterson Air Force Base.
2. Bandera, C.; Francisco, V.; Jose, B.; Harmon, M.; and Baird, L. 1996. Residual Q-Learning Applied to Visual Attention. In Proceedings of the Thirteenth International Conference on Machine Learning, 20-27. San Francisco, Calif.: Morgan Kaufmann.
3. Barto, A.; Bradkte, S.; and Singh, S. 1995. Learning to Act Using Real-Time Dynamic Programming. Artificial Intelligence 72:81-138.
4. Barto, A.; Sutton, R.; and Anderson, C. 1983. Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems. IEEE Transactions on Systems, Man, and Cybernetics 13(5): 834-846.
5. Bertsekas, D. 1995. Dynamic Programming and Optimal Control. New York: Athena Scientific.
6. Bertsekas, D., and Tsitsiklis, J. 1996. Neuro-Dynamic Programming. New York: Athena Scientific.
7. Mahadevan, S. 1996b. Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results. Machine Learning 22:159-196.
8. Mahadevan, S. 1996c. Sensitive-Discount Optimality: Unifying Average-Reward and Discounted Reinforcement Learning. In Proceedings of the Thirteenth International Conference on Machine Learning, 328-336. San Francisco, Calif.: Morgan Kaufmann.
9. Mahadevan, S., and Connell, J. 1992. Automatic Programming of Behavior-Based Robots Using Reinforcement Learning. Artificial Intelligence 55:311-365.