Add to ORKGReinforcement learning (RL) and optimal control of systems with contin- uous states and actions require approximation techniques in most interesting cases. In this article, we introduce Gaussian process dynamic programming (GPDP), an approximate value-function based RL algorithm. We consider both a classic optimal control problem, where problem-specific prior knowl- edge is available, and a classic RL problem, where only very general priors can be used. For the classic optimal control problem, GPDP models the unknown value functions with Gaussian processes and generalizes dynamic programming to continuous-valued states and actions. For the RL problem, GPDP starts from a given initial state and explores the state space using Bayesian active ...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learn...
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...
Reinforcement learning (RL) and optimal control of systems with contin- uous states and actions requ...
Finding an optimal policy in a reinforcement learning (RL) framework with continuous state and actio...
Finding an optimal policy in a reinforcement learning (RL) framework with continuous state and actio...
In general, it is difficult to determine an optimal closed-loop policy in nonlinear control problems...
In general, it is difficult to determine an optimal closed-loop policy in nonlinear control problems...
Finding an optimal policy in a reinforcement learning (RL) framework with continuous state and actio...
In general, it is difficult to determine an optimal closed-loop policy in nonlinear control problems...
The framework of dynamic programming (DP) and reinforcement learning (RL) can be used to express imp...
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...
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...
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...
Reinforcement learning (RL) and optimal control of systems with contin- uous states and actions requ...
Finding an optimal policy in a reinforcement learning (RL) framework with continuous state and actio...
Finding an optimal policy in a reinforcement learning (RL) framework with continuous state and actio...
In general, it is difficult to determine an optimal closed-loop policy in nonlinear control problems...
In general, it is difficult to determine an optimal closed-loop policy in nonlinear control problems...
Finding an optimal policy in a reinforcement learning (RL) framework with continuous state and actio...
In general, it is difficult to determine an optimal closed-loop policy in nonlinear control problems...
The framework of dynamic programming (DP) and reinforcement learning (RL) can be used to express imp...
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...
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...
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...