Reinforcement learning with replacing eligibility traces
Machine Learning - Special issue on reinforcement learning
On integrating apprentice learning and reinforcement learning
On integrating apprentice learning and reinforcement learning
Probabilistic policy reuse in a reinforcement learning agent
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Accelerating reinforcement learning through implicit imitation
Journal of Artificial Intelligence Research
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
Non-reciprocating Sharing Methods in Cooperative Q-Learning Environments
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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Significant advances have been made in allowing agents to learn, both autonomously and with human guidance. However, less attention has been paid to the question of how agents could best teach each other. For instance, an existing robot in a factory should be able to instruct a newly arriving robot, even if it is from a different manufacturer, has a different knowledge representation, or is not optimal itself.