Transfer Learning for RL Domains
Transfer learning refers to the process of conveying experience from a simple
task to another more complex (and related) task in order to reduce the
amount of time that is required to learn the latter task. Typically,
in a transfer learning procedure the agent learns a behavior in
a source task, and it uses the gained knowledge in order to
speed up the learning process in a target task. Reinforcement
Learning algorithms are time expensive when they learn from scratch, especially in complex domains, and
transfer learning comprises a suitable solution
to speed up the training process.
Here you can find our transfer learning
algorithm for the
Mountain Car Domain.
The software is distributed under the GNU
GPL licence. It requires RL-Glue
3.0 and tile
Partalas for bug reports, comments, suggestions or request
for help with the source code.
Source code developers: Ioannis Partalas.
- Fachantidis, A., Partalas, I., Taylor, M., & Vlahavas, I., “Autonomous Selection of Inter-Task Mappings in Transfer Learning (extended abstract)”, Proc. AAAI Spring Symposium Lifelong Machine Learning, Stanford, U.S.A, 2013 (accepted for presentation)
- Fachantidis, A., Partalas, I., Tsoumakas, G., & Vlahavas, I., “Transferring task models in reinforcement learning agents”. Neurocomputing, Elsevier, 2013 (to be published)
- Fachantidis, A., Partalas, I., Taylor, M., & Vlahavas, I, “Transfer learning via multiple inter-task mappings", 9th European Workshop on Reinforcement Learning (EWRL 2012), Athens, Greece, Recent Advances in Reinforcement Learning, Lecture Notes in Computer Science, pp. 225–236, 2012
- Fachantidis, A., Partalas, I., Tsoumakas, G., & Vlahavas, I. “Transferring models in hybrid reinforcement learning agents”, Proc. Engineering Applications of Neural Networks (EANN 2011), Corfu, Greece, IFIP Advances in Information and Communication Technology. Springer Boston, pp. 162–171, 2011.
- I. Partalas, G. Tsoumakas, K. Tzevanidis,
I. Vlahavas, "Transerring Experience in Reinforcement Learning through
Task Decomposition", Proc. 8th International Conferences on Autonomous
Agents and Multiagent Systems (AAMAS 2009), Budapest, Hungary,2009.