AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Implicit Imitation in Multiagent Reinforcement Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Accelerating reinforcement learning through imitation
Accelerating reinforcement learning through imitation
Bias and variance in value function estimation
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Bayesian approach to imitation in reinforcement learning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Cooperative learning using advice exchange
Adaptive agents and multi-agent systems
Model based Bayesian exploration
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning Actions through Imitation and Exploration: Towards Humanoid Robots That Learn from Humans
Creating Brain-Like Intelligence
On Rendering Emotions on a Robotic Face
Proceedings of Conference on Advances In Robotics
Prediction from expert demonstrations for safe tele-surgery
International Journal of Automation and Computing
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Imitation learning, also called learning by watching or programming by demonstration, has emerged as a means of accelerating many reinforcement learning tasks. Previous work has shown the value of imitation in domains where a single mentor demonstrates execution of a known optimal policy for the benefit of a learning agent. We consider the more general scenario of learning from mentors who are themselves agents seeking to maximize their own rewards. We propose a new algorithm based on the concept of transferable utility for ensuring that an observer agent can learn efficiently in the context of a selfish, not necessarily helpful, mentor. We also address the questions of when an imitative agent should request help from a mentor, and when the mentor can be expected to acknowledge a request for help. In analogy with other types of active learning, we call the proposed approach active imitation learning.