Add to ORKGWe describe the Fourier Basis, a linear value function approximation scheme based on the Fourier Series. We empirically evaluate its properties, and demonstrate that it performs well compared to Radial Basis Functions and the Polynomial Basis, the two most popular fixed bases for linear value function approximation, and is competitive with learned Proto-Value Functions even though no extra experience or computation is required
A key element in the solution of reinforcement learning problems is the value function. The purpose ...
There are several reinforcement learning algorithms that yield ap-proximate solutions for the proble...
Reinforcement learning is a general computational framework for learning sequential decision strate...
We describe the Fourier basis, a linear value function approx-imation scheme based on the Fourier se...
We describe the Fourier basis, a linear value function approximation scheme based on the Fourier ser...
We analyze a simple, Bellman-error-based approach to generating features, or basis functions, for va...
Reinforcement learning (RL) is a computational framework for learning sequential decision strategies...
A thesis presented for the degree of Doctor of Philosophy, School of Computer Science and Applied Ma...
The application of reinforcement learning to problems with continuous domains requires representing ...
AbstractThis work presents the restricted gradient-descent (RGD) algorithm, a training method for lo...
We address the problem of automatically constructing basis functions for linear approxim...
Reinforcement learning with function approximation has recently achieved tremendous results in appli...
In reinforcement learning (RL), an important sub-problem is learning the value function, which is ch...
Reinforcement learning is often done using parameterized function approximators to store value funct...
Graduation date: 2007The thesis focuses on model-based approximation methods for reinforcement\ud le...
A key element in the solution of reinforcement learning problems is the value function. The purpose ...
There are several reinforcement learning algorithms that yield ap-proximate solutions for the proble...
Reinforcement learning is a general computational framework for learning sequential decision strate...
We describe the Fourier basis, a linear value function approx-imation scheme based on the Fourier se...
We describe the Fourier basis, a linear value function approximation scheme based on the Fourier ser...
We analyze a simple, Bellman-error-based approach to generating features, or basis functions, for va...
Reinforcement learning (RL) is a computational framework for learning sequential decision strategies...
A thesis presented for the degree of Doctor of Philosophy, School of Computer Science and Applied Ma...
The application of reinforcement learning to problems with continuous domains requires representing ...
AbstractThis work presents the restricted gradient-descent (RGD) algorithm, a training method for lo...
We address the problem of automatically constructing basis functions for linear approxim...
Reinforcement learning with function approximation has recently achieved tremendous results in appli...
In reinforcement learning (RL), an important sub-problem is learning the value function, which is ch...
Reinforcement learning is often done using parameterized function approximators to store value funct...
Graduation date: 2007The thesis focuses on model-based approximation methods for reinforcement\ud le...
A key element in the solution of reinforcement learning problems is the value function. The purpose ...
There are several reinforcement learning algorithms that yield ap-proximate solutions for the proble...
Reinforcement learning is a general computational framework for learning sequential decision strate...