Creating advice-taking reinforcement learners
Machine Learning - Special issue on reinforcement learning
Knowledge extraction from reinforcement learning
New learning paradigms in soft computing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Scaling Reinforcement Learning toward RoboCup Soccer
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Integrating Experimentation and Guidance in Relational Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Rule extraction from linear support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Behavior transfer for value-function-based reinforcement learning
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Using advice to transfer knowledge acquired in one reinforcement learning task to another
ECML'05 Proceedings of the 16th European conference on Machine Learning
Cross-domain transfer for reinforcement learning
Proceedings of the 24th international conference on Machine learning
Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling
ECML '07 Proceedings of the 18th European conference on Machine Learning
Spatial Abstraction: Aspectualization, Coarsening, and Conceptual Classification
Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
Generalization and transfer learning in noise-affected robot navigation tasks
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Learning relational options for inductive transfer in relational reinforcement learning
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Relational macros for transfer in reinforcement learning
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Structural knowledge transfer by spatial abstraction for reinforcement learning agents
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Human-assisted neuroevolution through shaping, advice and examples
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Learning action descriptions of opponent behaviour in the robocup 2D simulation environment
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Expert Systems with Applications: An International Journal
Transferring evolved reservoir features in reinforcement learning tasks
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
Transfer in reinforcement learning via shared features
The Journal of Machine Learning Research
Transferring task models in Reinforcement Learning agents
Neurocomputing
Machine learning for interactive systems and robots: a brief introduction
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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We describe a reinforcement learning system that transfers skills from a previously learned source task to a related target task. The system uses inductive logic programming to analyze experience in the source task, and transfers rules for when to take actions. The target task learner accepts these rules through an advice-taking algorithm, which allows learners to benefit from outside guidance that may be imperfect. Our system accepts a human-provided mapping, which specifies the similarities between the source and target tasks and may also include advice about the differences between them. Using three tasks in the RoboCup simulated soccer domain, we demonstrate that this system can speed up reinforcement learning substantially.