Efficient Reinforcement Learning using Gaussian Processes
This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model...
- Autores:
- Tipo de recurso:
- Book
- Fecha de publicación:
- 2010
- Institución:
- Universidad de Bogotá Jorge Tadeo Lozano
- Repositorio:
- Expeditio: repositorio UTadeo
- Idioma:
- eng
- OAI Identifier:
- oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/17578
- Acceso en línea:
- https://directory.doabooks.org/handle/20.500.12854/45907
http://hdl.handle.net/20.500.12010/17578
- Palabra clave:
- Autonomous learning
Gaussian processes
Machine learning
Aprendizaje
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Aptitud de aprendizaje
- Rights
- License
- Abierto (Texto Completo)
| Summary: | This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems. |
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