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Efficient Reinforcement Learning Using Gaussian Processes
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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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Keywords
- autonomous learning
- Bayesian inference
- Control
- Gaussian processes
- Machine learning
Links
DOI: 10.5445/KSP/1000019799Editions