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    AAAI
    2010
    199views Intelligent Agents» more  AAAI 2010»
    Bayesian Policy Search for Multi-Agent Role Discovery
    13 years 5 months ago
    Bayesian Policy Search for Multi-Agent Role Discovery
    Download  web.engr.oregonstate.edu
    Bayesian inference is an appealing approach for leveraging prior knowledge in reinforcement learning (RL). In this paper we describe an algorithm for discovering different classes...
    Aaron Wilson, Alan Fern, Prasad Tadepalli
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    AAAI
    1992
    128views Intelligent Agents» more  AAAI 1992»
    Automatic Programming of Robots Using Genetic Programming
    13 years 5 months ago
    Automatic Programming of Robots Using Genetic Programming
    Download  www.genetic-programming.com
    The goal in automatic programming is to get a computer to perform a task by telling it what needs to be done, rather than by explicitly programming it. This paper considers the ta...
    John R. Koza, James Rice
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    NIPS
    1996
    117views Information Technology» more  NIPS 1996»
    Reinforcement Learning for Mixed Open-loop and Closed-loop Control
    13 years 5 months ago
    Reinforcement Learning for Mixed Open-loop and Closed-loop Control
    Download  anytime.cs.umass.edu
    Closed-loop control relies on sensory feedback that is usually assumed to be free. But if sensing incurs a cost, it may be coste ective to take sequences of actions in open-loop m...
    Eric A. Hansen, Andrew G. Barto, Shlomo Zilberstei...
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    NIPS
    1996
    192views Information Technology» more  NIPS 1996»
    Multidimensional Triangulation and Interpolation for Reinforcement Learning
    13 years 5 months ago
    Multidimensional Triangulation and Interpolation for Reinforcement Learning
    Download  www.cs.cmu.edu
    Dynamic Programming, Q-learning and other discrete Markov Decision Process solvers can be applied to continuous d-dimensional state-spaces by quantizing the state space into an arr...
    Scott Davies
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    NIPS
    1994
    152views Information Technology» more  NIPS 1994»
    Finding Structure in Reinforcement Learning
    13 years 5 months ago
    Finding Structure in Reinforcement Learning
    Download  www.ri.cmu.edu
    Reinforcement learning addresses the problem of learning to select actions in order to maximize one's performance inunknownenvironments. Toscale reinforcement learning to com...
    Sebastian Thrun, Anton Schwartz
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    NIPS
    1993
    86views Information Technology» more  NIPS 1993»
    Robust Reinforcement Learning in Motion Planning
    13 years 5 months ago
    Robust Reinforcement Learning in Motion Planning
    Download  www.cs.cmu.edu
    While exploring to nd better solutions, an agent performing online reinforcement learning (RL) can perform worse than is acceptable. In some cases, exploration might have unsafe, ...
    Satinder P. Singh, Andrew G. Barto, Roderic A. Gru...
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    AAAI
    1994
    185views Intelligent Agents» more  AAAI 1994»
    Learning to Coordinate without Sharing Information
    13 years 5 months ago
    Learning to Coordinate without Sharing Information
    Download  www.agent.ai
    Researchers in the eld of Distributed Arti cial Intelligence (DAI) have been developing e cient mechanisms to coordinate the activities of multiple autonomous agents. The need for...
    Sandip Sen, Mahendra Sekaran, John Hale
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    AAAI
    1993
    107views Intelligent Agents» more  AAAI 1993»
    Complexity Analysis of Real-Time Reinforcement Learning
    13 years 5 months ago
    Complexity Analysis of Real-Time Reinforcement Learning
    Download  www.ri.cmu.edu
    This paper analyzes the complexity of on-line reinforcement learning algorithms, namely asynchronous realtime versions of Q-learning and value-iteration, applied to the problem of...
    Sven Koenig, Reid G. Simmons
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    NIPS
    1997
    94views Information Technology» more  NIPS 1997»
    Reinforcement Learning with Hierarchies of Machines
    13 years 5 months ago
    Reinforcement Learning with Hierarchies of Machines
    Download  www.cs.berkeley.edu
    We present a new approach to reinforcement learning in which the policies considered by the learning process are constrained by hierarchies of partially specified machines. This ...
    Ronald Parr, Stuart J. Russell
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    NIPS
    2000
    112views Information Technology» more  NIPS 2000»
    Balancing Multiple Sources of Reward in Reinforcement Learning
    13 years 5 months ago
    Balancing Multiple Sources of Reward in Reinforcement Learning
    Download  www.cc.gatech.edu
    For many problems which would be natural for reinforcement learning, the reward signal is not a single scalar value but has multiple scalar components. Examples of such problems i...
    Christian R. Shelton
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