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Model-Based Reinfor...
Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization
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- Andersson, Olov, 1979- (författare)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska högskolan,KPLAB - Knowledge Processing Lab
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- Heintz, Fredrik, 1975- (författare)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska högskolan,KPLAB - Knowledge Processing Lab
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- Doherty, Patrick, 1957- (författare)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska högskolan,KPLAB - Knowledge Processing Lab
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(creator_code:org_t)
- AAAI Press, 2015
- 2015
- Engelska.
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Ingår i: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI). - : AAAI Press. - 9781577356981 ; , s. 2497-2503
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Abstract
Ämnesord
Stäng
- Reinforcement learning for robot control tasks in continuous environments is a challenging problem due to the dimensionality of the state and action spaces, time and resource costs for learning with a real robot as well as constraints imposed for its safe operation. In this paper we propose a model-based reinforcement learning approach for continuous environments with constraints. The approach combines model-based reinforcement learning with recent advances in approximate optimal control. This results in a bounded-rationality agent that makes decisions in real-time by efficiently solving a sequence of constrained optimization problems on learned sparse Gaussian process models. Such a combination has several advantages. No high-dimensional policy needs to be computed or stored while the learning problem often reduces to a set of lower-dimensional models of the dynamics. In addition, hard constraints can easily be included and objectives can also be changed in real-time to allow for multiple or dynamic tasks. The efficacy of the approach is demonstrated on both an extended cart pole domain and a challenging quadcopter navigation task using real data.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Reinforcement Learning
- Gaussian Processes
- Optimization
- Robotics
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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