Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Proceedings of the seventh international conference (1990) on Machine learning
Instance-Based Learning Algorithms
Machine Learning
ML92 Proceedings of the ninth international workshop on Machine learning
Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
A Framework for Behavioural Cloning
Machine Intelligence 15, Intelligent Agents [St. Catherine's College, Oxford, July 1995]
Skill reconstruction as induction of LQ controllers with subgoals
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Using Machine Learning to Understand Operator's Skill
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
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In behavioural cloning of the human operator's skill, a controller is usually induced directly as a classifier from system's states into actions. Experience shows that this often results in brittle controllers. In this paper we explore a decomposition of the cloning problem into two learning problems: the learning of operator's control trajectories and the learning of the system's dynamics separately. We analyse advantages of such indirect controllers. We give characterization of the learner's error that is plausible explanation of why this decomposition approach has empirically proved to be usually superior to direct cloning.