Partially Observable Markov Decision Problems Tommi Jaakkola...  Home/Search
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Abstract: Increasing attention has been paid to reinforcement learning algorithms in recent years, partly due to successes in the theoretical analysis of their behavior in Markov environments. If the Markov assumption is removed, however, neither generally the algorithms nor the analyses continue to be usable. We propose and analyze a new learning algorithm to solve a certain class of non-Markov decision problems. Our algorithm applies to problems in which the environment is Markov, but the... (Update)
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BibTeX entry: (Update)
@misc{ markov-reinforcement,
author = "Partially Observable Markov",
title = "Reinforcement Learning Algorithm for",
url = "citeseer.ist.psu.edu/757090.html" }
Citations (may not include all citations):658 Learning from delayed rewards (context) - Watkins - 1989
563 Learning to predict by the methods of temporal differences - Sutton - 1988
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230 Dynamic Programming: Deterministic and Stochastic Models (context) - Bertsekas - 1987
107 the convergence of stochastic iterative Dynamic Programming .. - Jaakkola, Jordan et al. - 1994
106 A survey of partially observable Markov decision processes (context) - Monahan - 1982
71 Asynchronous stochastic approximation and Q-learning (context) - Tsitsiklis - 1994
56 Learning without state estimation in partially observable en.. - Singh, Jaakkola et al. - 1994
52 A reinforcement learning method for maximizing undiscounted .. (context) - Schwartz - 1993
25 The convergence of TD (context) - Dayan - 1992
10 Monte-Carlo matrix inversion and reinforcement learning - Barto, Duff - 1994
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