Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Using Reinforcement Learning for Similarity Assessment in Case-Based Systems
IEEE Intelligent Systems
Retrieval, reuse, revision and retention in case-based reasoning
The Knowledge Engineering Review
Probabilistic policy reuse in a reinforcement learning agent
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Accelerating autonomous learning by using heuristic selection of actions
Journal of Heuristics
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
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
A case-based approach for coordinated action selection in robot soccer
Artificial Intelligence
Using Homomorphisms to transfer options across continuous reinforcement learning domains
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Case-Based Reasoning in Transfer Learning
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Improving Reinforcement Learning by Using Case Based Heuristics
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Accelerating reinforcement learning by composing solutions of automatically identified subtasks
Journal of Artificial Intelligence Research
General game learning using knowledge transfer
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Transfer learning in real-time strategy games using hybrid CBR/RL
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
Using advice to transfer knowledge acquired in one reinforcement learning task to another
ECML'05 Proceedings of the 16th European conference on Machine Learning
CBR for state value function approximation in reinforcement learning
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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In this paper we propose to combine three AI techniques to speed up a Reinforcement Learning algorithm in a Transfer Learning problem: Case-based Reasoning, Heuristically Accelerated Reinforcement Learning and Neural Networks. To do so, we propose a new algorithm, called L3, which works in 3 stages: in the first stage, it uses Reinforcement Learning to learn how to perform one task, and stores the optimal policy for this problem as a case-base; in the second stage, it uses a Neural Network to map actions from one domain to actions in the other domain and; in the third stage, it uses the case-base learned in the first stage as heuristics to speed up the learning performance in a related, but different, task. The RL algorithm used in the first phase is the Q-learning and in the third phase is the recently proposed Case-based Heuristically Accelerated Q-learning. A set of empirical evaluations were conducted in transferring the learning between two domains, the Acrobot and the Robocup 3D: the policy learned during the solution of the Acrobot Problem is transferred and used to speed up the learning of stability policies for a humanoid robot in the Robocup 3D simulator. The results show that the use of this algorithm can lead to a significant improvement in the performance of the agent.