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
Using Options for Knowledge Transfer in Reinforcement Learning TITLE2:
Using Options for Knowledge Transfer in Reinforcement Learning TITLE2:
Integrating Guidance into Relational Reinforcement Learning
Machine Learning
Relational temporal difference learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Cross-domain transfer for reinforcement learning
Proceedings of the 24th international conference on Machine learning
Using Homomorphisms to transfer options across continuous reinforcement learning domains
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Value functions for RL-based behavior transfer: a comparative study
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Learning relational options for inductive transfer in relational reinforcement learning
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Skill acquisition via transfer learning and advice taking
ECML'06 Proceedings of the 17th European conference on Machine Learning
Using advice to transfer knowledge acquired in one reinforcement learning task to another
ECML'05 Proceedings of the 16th European conference on Machine Learning
Transfer in variable-reward hierarchical reinforcement learning
Machine Learning
POIROT: acquiring workflows by combining models learned from interpreted traces
Proceedings of the fifth international conference on Knowledge capture
POIROT: integrated learning of web service procedures
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Transfer learning from minimal target data by mapping across relational domains
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A Two-Stage Relational Reinforcement Learning with Continuous Actions for Real Service Robots
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Democratic approximation of lexicographic preference models
Artificial Intelligence
Integrating reinforcement learning with human demonstrations of varying ability
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Abstraction and generalization in reinforcement learning: a summary and framework
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
Imitation learning in relational domains: a functional-gradient boosting approach
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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We describe an application of inductive logic programming to transfer learning. Transfer learning is the use of knowledge learned in a source task to improve learning in a related target task. The tasks we work with are in reinforcement-learning domains. Our approach transfers relational macros, which are finite-state machines in which the transition conditions and the node actions are represented by first-order logical clauses. We use inductive logic programming to learn a macro that characterizes successful behavior in the source task, and then use the macro for decision-making in the early learning stages of the target task. Through experiments in the RoboCup simulated soccer domain, we show that Relational Macro Transfer via Demonstration (RMT-D) from a source task can provide a substantial head start in the target task.