TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Reinforcement learning with replacing eligibility traces
Machine Learning - Special issue on reinforcement learning
Creating advice-taking reinforcement learners
Machine Learning - Special issue on reinforcement learning
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Integrated learning for interactive synthetic characters
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Cross-domain transfer for reinforcement learning
Proceedings of the 24th international conference on Machine learning
Multi-task reinforcement learning: a hierarchical Bayesian approach
Proceedings of the 24th international conference on Machine learning
Transfer Learning via Inter-Task Mappings for Temporal Difference Learning
The Journal of Machine Learning Research
Autonomous Learning of Stable Quadruped Locomotion
RoboCup 2006: Robot Soccer World Cup X
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Interactively shaping agents via human reinforcement: the TAMER framework
Proceedings of the fifth international conference on Knowledge capture
Potential-based shaping and Q-value initialization are equivalent
Journal of Artificial Intelligence Research
Accelerating reinforcement learning through implicit imitation
Journal of Artificial Intelligence Research
Training and tracking in robotics
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
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
Combining manual feedback with subsequent MDP reward signals for reinforcement learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Using advice to transfer knowledge acquired in one reinforcement learning task to another
ECML'05 Proceedings of the 16th European conference on Machine Learning
Policy transfer in mobile robots using neuro-evolutionary navigation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Reinforcement learning from simultaneous human and MDP reward
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Strategy-Based learning through communication with humans
KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
Multi model transfer learning with RULES family
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
A comparison between a communication-based and a data mining-based learning approach for agents
Intelligent Decision Technologies
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This work introduces Human-Agent Transfer (HAT), an algorithm that combines transfer learning, learning from demonstration and reinforcement learning to achieve rapid learning and high performance in complex domains. Using experiments in a simulated robot soccer domain, we show that human demonstrations transferred into a baseline policy for an agent and refined using reinforcement learning significantly improve both learning time and policy performance. Our evaluation compares three algorithmic approaches to incorporating demonstration rule summaries into transfer learning, and studies the impact of demonstration quality and quantity, as well as the effect of combining demonstrations from multiple teachers. Our results show that all three transfer methods lead to statistically significant improvement in performance over learning without demonstration. The best performance was achieved by combining the best demonstrations from two teachers.