Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
ML92 Proceedings of the ninth international workshop on Machine learning
Reinforcement Learning Applied to Linear Quadratic Regulation
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Problem Decomposition for Behavioural Cloning
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Improving the Robustness and Encoding Complexity of Behavioural Clones
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
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
Imitation and Reinforcement Learning in Agents with Heterogeneous Actions
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Programming Robosoccer agents by modeling human behavior
Expert Systems with Applications: An International Journal
Imitation guided learning in learning classifier systems
Natural Computing: an international journal
Accelerating reinforcement learning through implicit imitation
Journal of Artificial Intelligence Research
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Controlling a complex dynamic system, such as a plane or a crane, usually requires a skilled operator. Such a control skill is typically hard to reconstruct through introspection. Therefore an attractive approach to the reconstruction of control skill involves machine learning from operators' control traces, also known as behavioural cloning. In the most common approach to behavioural cloning, a controller is induced in the form of a rule set or a decision or regression tree that maps system states to actions. Unfortunately, induced controllers usually suffer from lack of robustness and lack typical elements of human control strategies, such as subgoals and substages of the control plan. In this paper we present a new approach to behavioural cloning which involves the induction of a model of the controlled system and enables the identification of subgoals that the operator is pursuing at various stages of the execution trace. The underlying formal basis for the present approach to behavioural cloning is the theory of LQ controllers. Experimental results show that this approach greatly improves the robustness of the induced controllers and also offers a new way of understanding the operator's subcognitive skill.