Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Using Options for Knowledge Transfer in Reinforcement Learning TITLE2:
Using Options for Knowledge Transfer in Reinforcement Learning TITLE2:
Transfer of Experience Between Reinforcement Learning Environments with Progressive Difficulty
Artificial Intelligence Review
Integrating Guidance into Relational Reinforcement Learning
Machine Learning
Probabilistic policy reuse in a reinforcement learning agent
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Journal of Artificial Intelligence Research
Building portable options: skill transfer in reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Top-down induction of first-order logical decision trees
Artificial Intelligence
Skill acquisition via transfer learning and advice taking
ECML'06 Proceedings of the 17th European conference on Machine Learning
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
Relational macros for transfer in reinforcement learning
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Policy transfer via Markov logic networks
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Abstraction and generalization in reinforcement learning: a summary and framework
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
Expert Systems with Applications: An International Journal
Transfer in reinforcement learning via shared features
The Journal of Machine Learning Research
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In reinforcement learning problems, an agent has the task of learning a good or optimal strategy from interaction with his environment. At the start of the learning task, the agent usually has very little information. Therefore, when faced with complex problems that have a large state space, learning a good strategy might be infeasible or too slow to work in practice. One way to overcome this problem, is the use of guidance to supply the agent with traces of "reasonable policies". However, in a lot of cases it will be hard for the user to supply such a policy. In this paper, we will investigate the use of transfer learning in Relational Reinforcement Learning. The goal of transfer learning is to accelerate learning on a target task after training on a different, but related, source task. More specifically, we introduce an extension of the options framework to the relational setting and show how one can learn skills that can be transferred across similar, but different domains. We present experiments showing the possible benefits of using relational options for transfer learning.