46 search results - page 5 / 10 » Discretization of Continuous Action Spaces in Extensive-Form... |
124
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ICANNGA
2007
Springer
15 years 8 months ago
2007
Springer
Reinforcement Learning (RL) is analyzed here as a tool for control system optimization. State and action spaces are assumed to be continuous. Time is assumed to be discrete, yet th...
126
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AIIDE
2006
15 years 3 months ago
2006
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, ti...
118
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ATAL
2009
Springer
15 years 8 months ago
2009
Springer
Many problems of multiagent planning under uncertainty require distributed reasoning with continuous resources and resource limits. Decentralized Markov Decision Problems (Dec-MDP...
101
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AAMAS
2007
Springer
15 years 8 months ago
2007
Springer
Abstract. Reinforcement learning (RL) is a widely used learning paradigm for adaptive agents. There exist several convergent and consistent RL algorithms which have been intensivel...
108
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CDC
2008
IEEE
15 years 3 months ago
2008
IEEE
Abstract-- The aim of this paper is to address left invertibility for dynamical systems with inputs and outputs in discrete sets. We study systems that evolve in discrete time with...