Add to ORKGCopyright © 2014 Amin Mousavi et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper discusses the notion of context transfer in reinforcement learning tasks. Context transfer, as defined in this paper, implies knowledge transfer between source and target tasks that share the same environment dynamics and reward function but have different states or action spaces. In other words, the agents learn the same task while using different sensors and actuators. This requires the existence of an underlying common Markov decision process (MDP) to which all the agents ’ MDPs c...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
Transfer in machine learning is the process of using knowledge learned in a source domain to speedup...
In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to address compl...
Transfer learning is an inherent aspect of human learning. When humans learn to perform a task, we r...
Modelling human behaviour is a complicated task that requires the use of knowledge from many domains...
Transfer learning can improve the reinforcement learn-ing of a new task by allowing the agent to reu...
Transfer learning has recently gained popularity due to the development of algorithms that can succe...
Abstract Transfer in reinforcement learning is a novel research area that focuses on the development...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
We examine the problem of Transfer in Reinforcement Learning and present a method to utilize knowled...
Agents, physical and virtual entities that interact with theirenvironment, are becoming increasingly...
Transfer learning focuses on developing methods to reuse information gathered from a source task in ...
In a reinforcement learning setting, the goal of transfer learning is to improve performance on a ta...
In a reinforcement learning setting, the goal of transfer learn-ing is to improve performance on a t...
Ai miei genitori Transfer learning is a process that occurs when learning in one context af-fects th...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
Transfer in machine learning is the process of using knowledge learned in a source domain to speedup...
In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to address compl...
Transfer learning is an inherent aspect of human learning. When humans learn to perform a task, we r...
Modelling human behaviour is a complicated task that requires the use of knowledge from many domains...
Transfer learning can improve the reinforcement learn-ing of a new task by allowing the agent to reu...
Transfer learning has recently gained popularity due to the development of algorithms that can succe...
Abstract Transfer in reinforcement learning is a novel research area that focuses on the development...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
We examine the problem of Transfer in Reinforcement Learning and present a method to utilize knowled...
Agents, physical and virtual entities that interact with theirenvironment, are becoming increasingly...
Transfer learning focuses on developing methods to reuse information gathered from a source task in ...
In a reinforcement learning setting, the goal of transfer learning is to improve performance on a ta...
In a reinforcement learning setting, the goal of transfer learn-ing is to improve performance on a t...
Ai miei genitori Transfer learning is a process that occurs when learning in one context af-fects th...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
Transfer in machine learning is the process of using knowledge learned in a source domain to speedup...
In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to address compl...