A note on derivatives of Bernstein polynomials
Journal of Approximation Theory
Finite-sample convergence rates for Q-learning and indirect algorithms
Proceedings of the 1998 conference on Advances in neural information processing systems II
Gambling in a rigged casino: The adversarial multi-armed bandit problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Using Options for Knowledge Transfer in Reinforcement Learning TITLE2:
Using Options for Knowledge Transfer in Reinforcement Learning TITLE2:
Transfer of Experience Between Reinforcement Learning Environments with Progressive Difficulty
Artificial Intelligence Review
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Behavior transfer for value-function-based reinforcement learning
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Knowledge transfer via advice taking
Proceedings of the 3rd international conference on Knowledge capture
Autonomous shaping: knowledge transfer in reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
PAC model-free reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Probabilistic policy reuse in a reinforcement learning agent
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Transfer Learning via Inter-Task Mappings for Temporal Difference Learning
The Journal of Machine Learning Research
Transfer via inter-task mappings in policy search reinforcement learning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Accelerating reinforcement learning through implicit imitation
Journal of Artificial Intelligence Research
A Bayesian approach to imitation in reinforcement learning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Improved rates for the stochastic continuum-armed bandit problem
COLT'07 Proceedings of the 20th annual conference on Learning theory
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Transfer algorithms allow the use of knowledge previously learned on related tasks to speed-up learning of the current task. Recently, many complex reinforcement learning problems have been successfully solved by efficient transfer learners. However, most of these algorithms suffer from a severe flaw: they are implicitly tuned to transfer knowledge between tasks having a given degree of similarity. In other words, if the previous task is very dissimilar (resp. nearly identical) to the current task, then the transfer process might slow down the learning (resp. might be far from optimal speed-up). In this paper, we address this specific issue by explicitly optimizing the transfer rate between tasks and answer to the question : "can the transfer rate be accurately optimized, and at what cost ?". We show that this optimization problem is related to the continuum bandit problem. We then propose a generic adaptive transfer method (AdaTran), which allows to extend several existing transfer learning algorithms to optimize the transfer rate. Finally, we run several experiments validating our approach.