Reinforcement learning: a survey

Reinforcement learning: a survey (1996)

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by Leslie Pack Kaelbling , Michael L. Littman , Andrew W. Moore

Venue:	Journal of Artificial Intelligence Research
Citations:	1134 - 21 self

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@ARTICLE{Kaelbling96reinforcementlearning:,
    author = {Leslie Pack Kaelbling and Michael L. Littman and Andrew W. Moore},
    title = {Reinforcement learning: a survey},
    journal = {Journal of Artificial Intelligence Research},
    year = {1996},
    volume = {4},
    pages = {237--285}
}

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Abstract

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word "reinforcement." The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.

Citations

1964	Dynamic Programming - Bellman - 1957
1515	A Theory of the Learnable - Valiant - 1984
1433	Genetic algorithms in search, optimization, and machine learning - Goldberg - 1989
1232	Theory of Linear and Integer Programming - SCHRIJVER - 1986
1135	Learning from delayed rewards - Watkins - 1989
1060	Learning to predict by the methods of temporal differences - Sutton - 1988
545	Some Studies in Machine Learning using the Game of Checkers - Samuel - 2000
477	Tsitsiklis. Parallel and Distributed Computation: Numerical Methods - Bertsekas, N - 1989
472	Learning to act using real-time dynamic programming - Barto, Bradtke, et al. - 1995
434	Dynamic programming and Markov processes - Howard - 1960
427	Integrated architectures for learning, planning, and reacting based on approximating integrated architectures for learning, planning, and reacting based on approximating dynamic programming - Sutton - 1990
423	Neuronlike adaptive elements that can solve dicult learning problems - Barto, Sutton, et al. - 1983
417	Markov games as a framework for multi-agent reinforcement learning - Littman - 1994
377	Parallel distributed processing: Explorations in the microstructure of cognition - Rumelhart, McClelland - 1986
342	Dynamic Programming and Optimal Control, Athena Scientific - Bertsekas - 2000
334	Practical issues in temporal difference learning - Tesauro - 1992
331	Dynamic Programming: Deterministic and Stochastic Models - Bertsekas - 1987
330	Temporal Difference Learning and TDGammon - Tesauro - 1995
316	Automatic programming of behavior-based robots using reinforcement learning - Mahadevan, Connell - 1992
300	Generalization in reinforcement learning: Successful examples using sparse coarse coding - Sutton - 1996
295	Empirical Model-Building and Response Surfaces - Box, Draper - 1987
280	Learning in embedded systems - Kaelbling - 1993
275	Prioritized sweeping: Reinforcement learning with less data and less time - Moore, Atkeson - 1993
262	Simple statistical gradient-following algorithms for connectionist reinforcement learning - Williams - 1992
250	Improving elevator performance using reinforcement learning - Crites, Barto - 1996
243	Acting optimally in partially observable stochastic domains - Cassandra, Kaelbling, et al. - 1994
243	Reinforcement Learning with Selective Perception and Hidden State - McCallum - 1996
239	Classifier fitness based on accuracy - Wilson - 1995
232	A New Approach to Manipulator control: the Cerebellar Model Articulation Controller - Albus - 1975
228	Neural Network Perception for Mobile Robot Guidance - Pomerleau - 1992
224	On-line Q-learning using connectionist systems - Rummery, Niranjan - 1994
223	Generalization in reinforcement learning: safely approximating the value function - Boyan, Moore - 1995
222	Motivated reinforcement learning - Dayan - 2002
219	Temporal Credit Assignment in Reinforcement Learning - Sutton - 1984
207	Residual Algorithms: Reinforcement Learning with Function Approximation - Baird - 1995
203	The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces - Moore, Atkeson - 1995
202	Learning policies for partially observable environments: Scaling up - Littman, Cassandra, et al.
190	Td-gammon, a self-teaching backgammon program, achieves master-level play - Tesauro - 1994
190	Learning to coordinate behaviors - Maes, Brooks - 1990
186	On the convergence of stochastic iterative dynamic programming algorithms - Jaakkola, Jordan, et al. - 1994
179	Locally weighted regression – an approach to regression-analysis by local fitting - Cleveland, Devlin - 1988
178	Reinforcement Learning for Robots Using Neural Networks - Lin - 1993
173	Reinforcement learning with perceptual aliasing: The perceptual distinctions approach - Chrisman - 1992
172	Stable function approximation in dynamic programming - Gordon - 1995
168	Bandit problems: Sequential allocation of experiments - Berry, Fristedt - 1985
166	Reinforcement learning with replacing eligibility traces - Singh, Sutton - 1996
165	Survey of partially observable markov decision processes: Theory, models, and algorithms - Monahan - 1982
154	Adaptation in Natural and Arti cial Systems - Holland - 1975
151	A survey of algorithmic methods for partially observed Markov decision processes - Lovejoy - 1991
151	Reward functions for accelerated learning - Mataric - 1994