Explore
Efficient Reinforcement Learning Using Gaussian Processes
0 Ungluers have
Faved this Work
Login to Fave
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.
This book is included in DOAB.
Why read this book? Have your say.
You must be logged in to comment.