Add to ORKGIn this paper we introduce proto-transfer leaning, a new framework for transfer learning. We explore solutions to transfer learning within reinforcement learning through the use of spectral methods. Proto-value functions (PVFs) are basis functions computed from a spectral analysis of random walks on the state space graph. They naturally lead to the ability to transfer knowledge and representation between related tasks or domains. We investigate task transfer by using the same PVFs in Markov decision processes (MDPs) with different rewards functions. Additionally, our experiments in domain transfer explore applying the Nyström method for interpolation of PVFs between MDPs of different sizes
Copyright © 2014 Amin Mousavi et al.This is an open access article distributed under the Creative Co...
In a reinforcement learning setting, the goal of transfer learning is to improve performance on a ta...
Transfer in machine learning is the process of using knowledge learned in a source domain to speedup...
In this paper we introduce proto-transfer leaning, a new framework for transfer learning. We explore...
Reinforcement Learning research is traditionally devoted to solve single-task problems. Therefore, a...
Ai miei genitori Transfer learning is a process that occurs when learning in one context af-fects th...
This paper presents a novel framework called proto-reinforcement learning (PRL), based on a mathemat...
We examine the problem of Transfer in Reinforcement Learning and present a method to utilize knowled...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
The paper investigates the possibility of applying value function based reinforcement learn-ing (RL)...
Transfer learning can improve the reinforcement learn-ing of a new task by allowing the agent to reu...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
We propose a new reinforcement learning algorithm for partially observable Markov decision processes...
Reinforcement learning is a general computational framework for learning sequential decision strate...
In this paper, we study the problem of transferring the available Markov Decision Process (MDP) mode...
Copyright © 2014 Amin Mousavi et al.This is an open access article distributed under the Creative Co...
In a reinforcement learning setting, the goal of transfer learning is to improve performance on a ta...
Transfer in machine learning is the process of using knowledge learned in a source domain to speedup...
In this paper we introduce proto-transfer leaning, a new framework for transfer learning. We explore...
Reinforcement Learning research is traditionally devoted to solve single-task problems. Therefore, a...
Ai miei genitori Transfer learning is a process that occurs when learning in one context af-fects th...
This paper presents a novel framework called proto-reinforcement learning (PRL), based on a mathemat...
We examine the problem of Transfer in Reinforcement Learning and present a method to utilize knowled...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
The paper investigates the possibility of applying value function based reinforcement learn-ing (RL)...
Transfer learning can improve the reinforcement learn-ing of a new task by allowing the agent to reu...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
We propose a new reinforcement learning algorithm for partially observable Markov decision processes...
Reinforcement learning is a general computational framework for learning sequential decision strate...
In this paper, we study the problem of transferring the available Markov Decision Process (MDP) mode...
Copyright © 2014 Amin Mousavi et al.This is an open access article distributed under the Creative Co...
In a reinforcement learning setting, the goal of transfer learning is to improve performance on a ta...
Transfer in machine learning is the process of using knowledge learned in a source domain to speedup...