Add to ORKGFinding an optimal policy in a reinforcement learning (RL) framework with continuous state and action spaces is challenging. Approximate solutions are often inevitable. GPDP is an approximate dynamic programming algorithm based on Gaussian process (GP) models for the value functions. In this paper, we extend GPDP to the case of unknown transition dynamics. After building a GP model for the transition dynamics, we apply GPDP to this model and determine a continuous-valued policy in the entire state space. We apply the resulting controller to the underpowered pendulum swing up. Moreover, we compare our results on this RL task to a nearly optimal discrete DP solution in a fully known environment
This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference ...
We present a data-efficient reinforcement learning method for continuous state-action systems under ...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learn...
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...
Reinforcement learning (RL) and optimal control of systems with contin- uous states and actions requ...
Reinforcement learning (RL) and optimal control of systems with contin- uous states and actions requ...
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...
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...
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...
Control of nonlinear systems on continuous domains is a challenging task for various reasons. For ro...
This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference ...
We present a data-efficient reinforcement learning method for continuous state-action systems under ...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learn...
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...
Reinforcement learning (RL) and optimal control of systems with contin- uous states and actions requ...
Reinforcement learning (RL) and optimal control of systems with contin- uous states and actions requ...
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...
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...
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...
Control of nonlinear systems on continuous domains is a challenging task for various reasons. For ro...
This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference ...
We present a data-efficient reinforcement learning method for continuous state-action systems under ...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learn...