Add to ORKGThe aim of this thesis is to use methods of transfer learning for training neural network on a reinforcement learning tasks. As test environment, I am using old 2D console games, such as space invaders or phoenix. I am testing the impact of re-purposing already trained models for different environments. Next I use methods for domain feature transfer. Lastly i focus on the topic of multi-task learning. From the results we can gain insight into possibilities of using transfer learning for reinforcement learning algorithms
Abstract Transfer learning problems are typically framed as leveragingknowledge learned on a source ...
Recent successes in applying Deep Learning techniques on Reinforcement Learning algorithms have led ...
Relational reinforcement learning has allowed results from reinforcement learning tasks to be re-use...
Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recen...
Abstract Transfer in reinforcement learning is a novel research area that focuses on the development...
Optimizing Deep Reinforcement Learning Process by Applying Transfer Learning. In this thesis we try ...
Transfer learning is an inherent aspect of human learning. When humans learn to perform a task, we r...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
The main objective of Transfer Learning is to reuse knowledge acquired in a previous learned task, i...
Transfer learning has recently gained popularity due to the development of algorithms that can succe...
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...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
International audienceTransfer in reinforcement learning is a novel research area that focuses on th...
A general approach to knowledge transfer is introduced in which an agent controlled by a neural netw...
Abstract Transfer learning problems are typically framed as leveragingknowledge learned on a source ...
Recent successes in applying Deep Learning techniques on Reinforcement Learning algorithms have led ...
Relational reinforcement learning has allowed results from reinforcement learning tasks to be re-use...
Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recen...
Abstract Transfer in reinforcement learning is a novel research area that focuses on the development...
Optimizing Deep Reinforcement Learning Process by Applying Transfer Learning. In this thesis we try ...
Transfer learning is an inherent aspect of human learning. When humans learn to perform a task, we r...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
The main objective of Transfer Learning is to reuse knowledge acquired in a previous learned task, i...
Transfer learning has recently gained popularity due to the development of algorithms that can succe...
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...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
International audienceTransfer in reinforcement learning is a novel research area that focuses on th...
A general approach to knowledge transfer is introduced in which an agent controlled by a neural netw...
Abstract Transfer learning problems are typically framed as leveragingknowledge learned on a source ...
Recent successes in applying Deep Learning techniques on Reinforcement Learning algorithms have led ...
Relational reinforcement learning has allowed results from reinforcement learning tasks to be re-use...