Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms
暂无分享,去创建一个
[1] L. Shapley,et al. Stochastic Games* , 1953, Proceedings of the National Academy of Sciences.
[2] Ronald A. Howard,et al. Dynamic Programming and Markov Processes , 1960 .
[3] R. Karp,et al. On Nonterminating Stochastic Games , 1966 .
[4] Cyrus Derman,et al. Finite State Markovian Decision Processes , 1970 .
[5] D. Bertsekas. Monotone Mappings with Application in Dynamic Programming , 1977 .
[6] Robert M Thrall,et al. Mathematics of Operations Research. , 1978 .
[7] Stef Tijs,et al. Fictitious play applied to sequences of games and discounted stochastic games , 1982 .
[8] Patchigolla Kiran Kumar,et al. A Survey of Some Results in Stochastic Adaptive Control , 1985 .
[9] Karl-Heinz Waldmann,et al. On Bounds for Dynamic Programs , 1985, Math. Oper. Res..
[10] E. Zeidler,et al. Fixed-point theorems , 1986 .
[11] S. Verdú,et al. Abstract dynamic programming models under commutativity conditions , 1987 .
[12] A. Barto,et al. Learning and Sequential Decision Making , 1989 .
[13] Anne Condon,et al. On Algorithms for Simple Stochastic Games , 1990, Advances In Computational Complexity Theory.
[14] Richard E. Korf,et al. Real-Time Heuristic Search , 1990, Artif. Intell..
[15] Pierre Priouret,et al. Adaptive Algorithms and Stochastic Approximations , 1990, Applications of Mathematics.
[16] Richard S. Sutton,et al. Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming , 1990, ML.
[17] Anne Condon,et al. The Complexity of Stochastic Games , 1992, Inf. Comput..
[18] C. Atkeson,et al. Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time , 1993, Machine Learning.
[19] Terrence J. Sejnowski,et al. Temporal Difference Learning of Position Evaluation in the Game of Go , 1993, NIPS.
[20] Ronald J. Williams,et al. Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions , 1993 .
[21] Anton Schwartz,et al. A Reinforcement Learning Method for Maximizing Undiscounted Rewards , 1993, ICML.
[22] Andrew G. Barto,et al. Convergence of Indirect Adaptive Asynchronous Value Iteration Algorithms , 1993, NIPS.
[23] Ronald J. Williams,et al. Analysis of Some Incremental Variants of Policy Iteration: First Steps Toward Understanding Actor-Cr , 1993 .
[24] Satinder Singh. Asynchronous Modified Policy Iteration with Single-sided Updates , 1993 .
[25] Michael I. Jordan,et al. MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES , 1996 .
[26] M.A.F. Mcdonald,et al. Approximate Discounted Dynamic Programming Is Unreliable , 1994 .
[27] George H. John. When the Best Move Isn't Optimal: Q-learning with Exploration , 1994, AAAI.
[28] Satinder Singh,et al. An Upper Bound on the Loss from Approximate Optimal-Value Functions , 2004, Machine-mediated learning.
[29] Sebastian Thrun,et al. Learning to Play the Game of Chess , 1994, NIPS.
[30] Mahesan Niranjan,et al. On-line Q-learning using connectionist systems , 1994 .
[31] Michael L. Littman,et al. Markov Games as a Framework for Multi-Agent Reinforcement Learning , 1994, ICML.
[32] Martin L. Puterman,et al. Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .
[33] John N. Tsitsiklis,et al. Asynchronous stochastic approximation and Q-learning , 1994, Mach. Learn..
[34] Michael I. Jordan,et al. Reinforcement Learning with Soft State Aggregation , 1994, NIPS.
[35] Matthias Heger,et al. Consideration of Risk in Reinforcement Learning , 1994, ICML.
[36] Gerald Tesauro,et al. Temporal Difference Learning and TD-Gammon , 1995, J. Int. Comput. Games Assoc..
[37] Carlos H. C. Ribeiro. Attentional Mechanisms as a Strategy for Generalization in the Q-Learning Algorithm , 1995 .
[38] Geoffrey J. Gordon. Stable Function Approximation in Dynamic Programming , 1995, ICML.
[39] Ben J. A. Kröse,et al. Learning from delayed rewards , 1995, Robotics Auton. Syst..
[40] Steven J. Bradtke,et al. Incremental dynamic programming for on-line adaptive optimal control , 1995 .
[41] Gavin Adrian Rummery. Problem solving with reinforcement learning , 1995 .
[42] Csaba Szepesvári,et al. General Framework for Reinforcement Learning , 1995 .
[43] Richard S. Sutton,et al. Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding , 1995, NIPS.
[44] Andrew G. Barto,et al. Learning to Act Using Real-Time Dynamic Programming , 1995, Artif. Intell..
[45] Richard S. Sutton,et al. Reinforcement Learning with Replacing Eligibility Traces , 2005, Machine Learning.
[46] Matthias Heger. Risk-averse reinforcement learning , 1996 .
[47] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[48] Michael L. Littman,et al. Algorithms for Sequential Decision Making , 1996 .
[49] Matthias Heger. The Loss from Imperfect Value Functions in Expectation-Based and Minimax-Based Tasks , 1996, Machine Learning.
[50] John N. Tsitsiklis,et al. Analysis of temporal-difference learning with function approximation , 1996, NIPS 1996.
[51] Peter Dayan,et al. Q-learning , 1992, Machine Learning.
[52] SRIDHAR MAHADEVAN,et al. Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results , 2005, Machine Learning.