Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Metrics for finite Markov decision processes
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Relating reinforcement learning performance to classification performance
ICML '05 Proceedings of the 22nd international conference on Machine learning
Teaching robots by moulding behavior and scaffolding the environment
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Efficient training of artificial neural networks for autonomous navigation
Neural Computation
Apprenticeship learning using linear programming
Proceedings of the 25th international conference on Machine learning
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Apprenticeship learning and reinforcement learning with application to robotic control
Apprenticeship learning and reinforcement learning with application to robotic control
Learning grasping affordances from local visual descriptors
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
Maximum entropy inverse reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Journal of Artificial Intelligence Research
Interactive policy learning through confidence-based autonomy
Journal of Artificial Intelligence Research
Bayesian inverse reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Training parsers by inverse reinforcement learning
Machine Learning
Active learning of visual descriptors for grasping using non-parametric smoothed beta distributions
Robotics and Autonomous Systems
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In this paper we address the problem of learning a policy from demonstration. Assuming that the policy to be learned is the optimal policy for an underlying MDP, we propose a novel way of leveraging the underlying MDP structure in a kernel-based approach. Our proposed approach rests on the insight that the MDP structure can be encapsulated into an adequate state-space metric. In particular we show that, using MDP metrics, we are able to cast the problem of learning from demonstration as a classification problem and attain similar generalization performance as methods based on inverse reinforcement learning at a much lower online computational cost. Our method is also able to attain superior generalization than other supervised learning methods that fail to consider the MDP structure.