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
Reinforcement learning (RL) and optimal control of systems with contin- uous states and actions requ...
The exploration-exploitation trade-off is among the central challenges of reinforcement learning. Th...
Recent approaches to Reinforcement Learning (RL) with function approximation include Neural Fitted Q...
This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference ...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learn...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learn...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learn...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learn...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learn...
Control of nonlinear systems on continuous domains is a challenging task for various reasons. For ro...
Reinforcement learning (RL) and optimal control of systems with contin- uous states and actions requ...
Gaussian process models constitute a class of probabilistic statistical models in which a Gaussian p...
Gaussian process models constitute a class of probabilistic statistical models in which a Gaussian p...
The exploration-exploitation trade-off is among the central challenges of rein-forcement learning. T...
This paper derives sample complexity results for using Gaussian Processes (GPs) in both model-based ...
Reinforcement learning (RL) and optimal control of systems with contin- uous states and actions requ...
The exploration-exploitation trade-off is among the central challenges of reinforcement learning. Th...
Recent approaches to Reinforcement Learning (RL) with function approximation include Neural Fitted Q...
This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference ...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learn...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learn...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learn...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learn...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learn...
Control of nonlinear systems on continuous domains is a challenging task for various reasons. For ro...
Reinforcement learning (RL) and optimal control of systems with contin- uous states and actions requ...
Gaussian process models constitute a class of probabilistic statistical models in which a Gaussian p...
Gaussian process models constitute a class of probabilistic statistical models in which a Gaussian p...
The exploration-exploitation trade-off is among the central challenges of rein-forcement learning. T...
This paper derives sample complexity results for using Gaussian Processes (GPs) in both model-based ...
Reinforcement learning (RL) and optimal control of systems with contin- uous states and actions requ...
The exploration-exploitation trade-off is among the central challenges of reinforcement learning. Th...
Recent approaches to Reinforcement Learning (RL) with function approximation include Neural Fitted Q...
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