Add to ORKGThis paper presents a novel framework called proto-reinforcement learning (PRL), based on a mathematical model of a proto-value function: these are task-independent basis functions that form the building blocks of all value functions on a given state space manifold. Proto-value functions are learned not from rewards, but instead from analyzing the topology of the state space. Formally, proto-value functions are Fourier eigenfunctions of the Laplace-Beltrami diffusion operator on the state space manifold. Proto-value functions facilitate structural decomposition of large state spaces, and form geodesically smooth orthonormal basis functions for approximating any value function. The theoretical basis for proto-value functions combines insight...
When applying reinforcement learning in domains with very large or continuous state spaces, the expe...
A longstanding goal of reinforcement learning is to develop nonparametric representations of policie...
AbstractThis work presents the restricted gradient-descent (RGD) algorithm, a training method for lo...
This paper presents a novel framework called proto-reinforcement learning (PRL), based on a mathemat...
In reinforcement learning (RL), an important sub-problem is learning the value function, which is ch...
Reinforcement Learning research is traditionally devoted to solve single-task problems. Therefore, a...
In this paper we introduce proto-transfer leaning, a new framework for transfer learning. We explore...
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 describe the Fourier Basis, a linear value function approximation scheme based on the Fourier Ser...
Ai miei genitori Transfer learning is a process that occurs when learning in one context af-fects th...
Taking inspiration from inverse reinforcement learning, the proposed Direct Value Learning for Reinf...
Reinforcement learning (RL) is a computational framework for learning sequential decision strategies...
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to ...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...
When applying reinforcement learning in domains with very large or continuous state spaces, the expe...
A longstanding goal of reinforcement learning is to develop nonparametric representations of policie...
AbstractThis work presents the restricted gradient-descent (RGD) algorithm, a training method for lo...
This paper presents a novel framework called proto-reinforcement learning (PRL), based on a mathemat...
In reinforcement learning (RL), an important sub-problem is learning the value function, which is ch...
Reinforcement Learning research is traditionally devoted to solve single-task problems. Therefore, a...
In this paper we introduce proto-transfer leaning, a new framework for transfer learning. We explore...
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 describe the Fourier Basis, a linear value function approximation scheme based on the Fourier Ser...
Ai miei genitori Transfer learning is a process that occurs when learning in one context af-fects th...
Taking inspiration from inverse reinforcement learning, the proposed Direct Value Learning for Reinf...
Reinforcement learning (RL) is a computational framework for learning sequential decision strategies...
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to ...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...
When applying reinforcement learning in domains with very large or continuous state spaces, the expe...
A longstanding goal of reinforcement learning is to develop nonparametric representations of policie...
AbstractThis work presents the restricted gradient-descent (RGD) algorithm, a training method for lo...