The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Transfer of Experience Between Reinforcement Learning Environments with Progressive Difficulty
Artificial Intelligence Review
Autonomous shaping: knowledge transfer in 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
Autonomous transfer for reinforcement learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
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
Using Homomorphisms to transfer options across continuous reinforcement learning domains
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
Skill acquisition via transfer learning and advice taking
ECML'06 Proceedings of the 17th European conference on Machine Learning
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In the context of reinforcement learning, transfer learning refers to the concept of reusing knowledge acquired in past tasks to speed up the learning procedure in new tasks. Transfer learning methods have been succesfully applied in single-agent reinforcement learning algorithms, but no prior work has focused on applying them in a multi-agent environment. We propose a novel method for transfer learning in multi-agent reinforcement learning domains. We proceed to test the proposed approach in a multi-agent domain under various configurations. The results demonstrate that the method can reduce the learning time and increase the asymptotic performance of the learning algorithm.