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Baird, L (1995) Residual algorithms: Reinforcement learning with function approximation Proceedings o the Twelfrh International Conference on Machine Learning @p 30-37) San Francisco: f Morgan Kaufmann
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Barto, A (1992) Reinforcement learning and adaptive critic methods In D White & S Sofge (Eds), Handbook of intelligent control: Neural, fuzzy, and adaptive approaches (pp 469-491) New York: Van Nostrand Reinhold Barto, A, Bradtke, S, & Singh, S (1995) Learning to act using real-time dynamic programming ArtiJicial Intelligence, Special volume: Computational research on interaction and agency, 72(1), 81-138 Barto, A, Sutton, R, & Anderson, C (1983) Neuronlike adaptive elements that can solve difficult learning control problems IEEE Transactions on Systems, Man, and Cybernetics, 13(5), 834846 Bellman, R E (1957) Dynamic Programming Princeton, NJ: Princeton University Press Bellrnan, R (1958) On a routing problem Quarterly of Applied Mathematics, 16(1), 87-90 Bellman, R (1961) Adaptive control processes Princeton, NJ: Princeton University Press Berenji, R (1992) Learning and tuning fuzzy controllers through reinforcements IEEE Transactions on Neural Networks, 3(5), 724-740 Bertsekas, D (1987) Dynamicprogramming: Deterministic and stochastic models Englewood Cliffs, NJ: Prentice Hall Blackwell, D (1965) Discounted dynamic programming Annals of Mathematical Statistics, 36,226235 Boyan, J, & Moore, A (1995) Generalization in reinforcement learning: Safely approximating the value function In G Tesauro, D Touretzky, & T Leen (Eds), Advances in Neural Information Processing Systems 7 Cambridge, M A : MIT Press Crites, R, & Barto, A (1996) Improving elevator performance using reinforcement learning In D S Touretzky, M C Mozer, & M C Hasselmo (Eds), Advances in Neural Information Processing Systems, 8 Dayan, P (1992) The convergence of TD(A) for general A Machine Learning, 8, 341-362 Dean, T, Basye, K, & Shewchuk, J (1993) Reinforcement learning for planning and control In S Minton (Ed), Machine Learning Methods for Planning @p 67-92) San Francisco: Morgan Kaufmann Dietterich, T G, & Flann, N S (1995) Explanation-based learning and reinforcement learning: A unified view Proceedings of the 12th International Conference on Machine Learning @p 176-184) San Francisco: Morgan Kaufmann Ford, L, & Fulkerson, D (1962) Flows in networks Princeton, NJ: Princeton University Press Gordon, G (1995) Stable function approximation in dynamic programming Proceedings of the TwelfthInternational Conference on Machine Learning (pp 261-268) San Francisco: Morgan Kaufmann Kaelbling, L P, Littman, M L, & Moore, A W (1996) Reinforcement learning: A survey Journal of AI Research, 4, 237-285 Online journal at http://wwwcswashingtonedu/research/jair/homehtm1 Holland, J H (1986) Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems In Michalski, Carbonell, & Mitchell (Eds), Machine learning: An artijicial intelligence approach (Vol 2, pp 593423) San Francisco: Morgan Kaufmann Laird, J E, & Rosenbloom, P S (1990) Integrating execution, planning, and learning in SOAR for external environments Proceedings of the Eighth National Conference on Artificial Intelligence (pp 1022-1029) Menlo Park, CA: AAAI Press Lin, L J (1992) Self-improving reactive agents based on reinforcement learning, planning, and teaching Machine Learning, 8, 293-321 Lin, L J (1993) Hierarchical learning of robot skills by reinforcement Proceedings of the International Conference on Neural Networks Littman, M (1996) Algorithms for sequential decision making (PhD dissertation and Technical Report CS-96-09) Brown University, Department of Computer Science, Providence, RI Maclin, R, & Shavlik, J W (1996) Creating advice-taking reinforcement learners Machine Learning, 22, 251-281
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Mahadevan, S (1996) Average reward reinforcement learning: Foundations, algorithms, and empirical results Machine Learning, 22(1), 159-195 Mahadevan, S, & Connell, J (1991) Automatic programming of behavior-based robots using reinforcement learning In Proceedings of the Ninth National Conference on ArtGcial Intelligence San Francisco: Morgan Kaufmann McCallum, A (1995) Reinforcement learning with selective perception and hidden state (PhD dissertation) Department of Computer Science, University of Rochester, Rochester, NY Mitchell, T M, & Thrun, S B (1993) Explanation-based neural network learning for robot control In C Giles, S Hanson, & J Cowan (Eds), Advances in Neural Information Processing System 5 (pp 287-294) San Francisco: Morgan-Kaufmann Moore, A, & Atkeson C (1993) Prioritized sweeping: Reinforcement learning with less data and less real time Machine Learning, 13, 103 Peng, J, & Williams, R (1994) Incremental multi-step Q-learning Proceedings of the Eleventh international Conference on Machine Learning (pp 226-232) San Francisco: Morgan Kaufmann Ring, M (1994) Continual learning in reinforcement environments (PhD dissertation) Computer Science Department, University of Texas at Austin, Austin, TX Samuel, A L (1959) Some studies in machine learning using the game of checkers IBM Journal of Research and Development, 3, 21 1-229 Singh, S (1992) Reinforcement learning with a hierarchy of abstract models Proceedings of the Tenth National Conference on Art@cial Intelligence (pp 202-207) San Jose, CA: AAAI Press Singh, S (1993) Learning to solve markovian decision processes (PhD dissertation) Also CMPSCI Technical Report 93-77, Department of Computer Science, University of Massachusetts at Amherst Singh, S, & Sutton, R (1996) Reinforcement learning with replacing eligibility traces Machine Learning, 22, 123 Sutton, R (1988) Learning to predict by the methods of temporal differences Machine learning, 3, 9-44 Sutton R (1991) Planning by incremental dynamic programming Proceedings of the Eighth Znternational Conference on Machine Learning (pp 353-357) San Francisco: Morgan Kaufmann Communications of the ACM, Tesauro, G (1995) Temporal difference learning and TD-GAMMON 38(3), 58-68 Thrun, S (1992) The role of exploration in learning control In D White & D Sofge (Eds), Handbook of intelligent control: Neural, fizzy, and adaptive approaches (pp 527-559) New York: Van Nostrand Reinhold Thrun, S (1996) Explanation-based neural network learning: A lifelong learning approach Boston: Kluwer Academic Publishers Tsitsiklis, J (1994) Asynchronous stochastic approximation and Q-learning Machine Learning, 16(3), 185-202 Watkins, C (1989) Learning from delayed rewards (PhD dissertation) King's College, Cambridge, England Watkins, C, & Dayan, P (1992) Q-learning Machine Learning, 8, 279-292 Zhang, W, & Dietterich, T G (1996) High-performance job-shop scheduling with a time-delay TD(A) network In D S Touretzky, M C Mozer, & M E Hasselmo (Eds), Advances in neural information processing systems, 8, 1024-1030
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