Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Metrics for finite Markov decision processes
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Tree-Based Batch Mode Reinforcement Learning
The Journal of Machine Learning Research
Identifying useful subgoals in reinforcement learning by local graph partitioning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Transfer Learning via Inter-Task Mappings for Temporal Difference Learning
The Journal of Machine Learning Research
Model-based function approximation in reinforcement learning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Transfer of samples in batch reinforcement learning
Proceedings of the 25th international conference on Machine learning
Transferring Instances for Model-Based Reinforcement Learning
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Building portable options: skill transfer in reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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The main objective of transfer in reinforcement learning is to reduce the complexity of learning the solution of a target task by effectively reusing the knowledge retained from solving a set of source tasks. One of the main problems is to avoid negative transfer, that is the transfer of knowledge across tasks that are significantly different that may worsen the learning performance. In this paper, we introduce a novel algorithm that selectively transfers samples (i.e., tuples ) from source to target tasks and that uses them as input for batch reinforcement-learning algorithms. By transferring samples from source tasks that are mostly similar to the target task, we reduce the number of samples actually collected from the target task to learn its solution. We show that the proposed approach is effective in reducing the learning complexity, even when some source tasks are significantly different from the target task.