Part of
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.
Umfang: IX, 205 S.
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Deisenroth, M. 2010. Efficient Reinforcement Learning using Gaussian Processes. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000019799
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Veröffentlicht am 22. November 2010
Englisch
223
| Paperback | 978-3-86644-569-7 |