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
Learning in the presence of concept drift and hidden contexts
Machine Learning
Machine Learning - Special issue on inductive transfer
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Brains, Behavior and Robotics
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
PEGASUS: A policy search method for large MDPs and POMDPs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
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
Transfer of samples in batch reinforcement learning
Proceedings of the 25th international conference on Machine learning
Autonomous transfer for reinforcement learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Machine learning for fast quadrupedal locomotion
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Model-based exploration in continuous state spaces
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Model based Bayesian exploration
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Using advice to transfer knowledge acquired in one reinforcement learning task to another
ECML'05 Proceedings of the 16th European conference on Machine Learning
Improving Batch Reinforcement Learning Performance through Transfer of Samples
Proceedings of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium
Knowledge transfer based on feature representation mapping for text classification
Expert Systems with Applications: An International Journal
Metric learning for reinforcement learning agents
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Investigation in transfer learning: better way to apply transfer learning between agents
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
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
Transferring evolved reservoir features in reinforcement learning tasks
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Transfer learning via multiple inter-task mappings
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
Transferring task models in Reinforcement Learning agents
Neurocomputing
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Reinforcement learningagents typically require a significant amount of data before performing well on complex tasks. Transfer learningmethods have made progress reducing sample complexity, but they have primarily been applied to model-free learning methods, not more data-efficient model-based learning methods. This paper introduces timbrel, a novel method capable of transferring information effectively into a model-based reinforcement learning algorithm. We demonstrate that timbrelcan significantly improve the sample efficiency and asymptotic performance of a model-based algorithm when learning in a continuous state space. Additionally, we conduct experiments to test the limits of timbrel's effectiveness.