Add to ORKGWe exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP model allows evaluation of the value function in closed form. The resulting policy iteration algorithm is demonstrated on a simple problem with a two dimensional state space. Further, we speculate that the intrinsic ability of GP models to characterise distributions of functions would allow the method to capture entire distributions over future values instead of merely their expectation, which has traditionally been the focus of much of reinforcement learning.
We give a basic introduction to Gaussian Process regression models. We focus on understanding the ro...
Abstract. We present a kernel-based approach to reinforcement learning that overcomes the stability ...
Abstract—Autonomous learning has been a promising direction in control and robotics for more than a ...
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 ...
Control of nonlinear systems on continuous domains is a challenging task for various reasons. For ro...
Gaussian process models constitute a class of probabilistic statistical models in which a Gaussian p...
tion and the use of Gaussian Processes. They belong to the class of fitted value iteration algorithm...
Recent approaches to Reinforcement Learning (RL) with function approximation include Neural Fitted Q...
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...
An off-policy Bayesian nonparameteric approximate reinforcement learning framework, termed as GPQ, t...
How can and should an agent actively learn a function? Psychological theories about function learnin...
This paper derives sample complexity results for using Gaussian Processes (GPs) in both model-based ...
The exploration-exploitation trade-off is among the central challenges of reinforcement learning. Th...
We give a basic introduction to Gaussian Process regression models. We focus on understanding the ro...
Abstract. We present a kernel-based approach to reinforcement learning that overcomes the stability ...
Abstract—Autonomous learning has been a promising direction in control and robotics for more than a ...
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 ...
Control of nonlinear systems on continuous domains is a challenging task for various reasons. For ro...
Gaussian process models constitute a class of probabilistic statistical models in which a Gaussian p...
tion and the use of Gaussian Processes. They belong to the class of fitted value iteration algorithm...
Recent approaches to Reinforcement Learning (RL) with function approximation include Neural Fitted Q...
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...
An off-policy Bayesian nonparameteric approximate reinforcement learning framework, termed as GPQ, t...
How can and should an agent actively learn a function? Psychological theories about function learnin...
This paper derives sample complexity results for using Gaussian Processes (GPs) in both model-based ...
The exploration-exploitation trade-off is among the central challenges of reinforcement learning. Th...
We give a basic introduction to Gaussian Process regression models. We focus on understanding the ro...
Abstract. We present a kernel-based approach to reinforcement learning that overcomes the stability ...
Abstract—Autonomous learning has been a promising direction in control and robotics for more than a ...