Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Intra-Option Learning about Temporally Abstract Actions
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Model Minimization in Hierarchical Reinforcement Learning
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Behavior transfer for value-function-based reinforcement learning
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Efficient solution algorithms for factored MDPs
Journal of Artificial Intelligence Research
Model minimization in Markov decision processes
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Cross-domain transfer for reinforcement learning
Proceedings of the 24th international conference on Machine learning
Transfer via inter-task mappings in policy search reinforcement learning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Towards reinforcement learning representation transfer
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Autonomous transfer for reinforcement learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Transfer via soft homomorphisms
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A task specification language for bootstrap learning
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Transferring experience in reinforcement learning through task decomposition
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Autonomous inter-task transfer in reinforcement learning domains
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning and multiagent reasoning for autonomous agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Probabilistic Policy Reuse for inter-task transfer learning
Robotics and Autonomous Systems
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Abstraction and generalization in reinforcement learning: a summary and framework
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
Reinforcement learning transfer via common subspaces
ALA'11 Proceedings of the 11th international conference on Adaptive and Learning Agents
Using cases as heuristics in reinforcement learning: a transfer learning application
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Automatic construction of temporally extended actions for MDPs using bisimulation metrics
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Transfer learning in multi-agent reinforcement learning domains
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Budgeted knowledge transfer for state-wise heterogeneous RL agents
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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We examine the problem of Transfer in Reinforcement Learning and present a method to utilize knowledge acquired in one Markov Decision Process (MDP) to bootstrap learning in a more complex but related MDP. We build on work in model minimization in Reinforcement Learning to define relationships between state-action pairs of the two MDPs. Our main contribution in this work is to provide a way to compactly represent such mappings using relationships between state variables in the two domains. We use these functions to transfer a learned policy in the first domain into an option in the new domain, and apply intra-option learning methods to bootstrap learning in the new domain. We first evaluate our approach in the well known Blocksworld domain. We then demonstrate that our approach to transfer is viable in a complex domain with a continuous state space by evaluating it in the Robosoccer Keepaway domain.