Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Apprenticeship learning using linear programming
Proceedings of the 25th international conference on Machine learning
Maximum entropy inverse reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Bayesian inverse reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A Computational Model of Social-Learning Mechanisms
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Robot self-initiative and personalization by learning through repeated interactions
Proceedings of the 6th international conference on Human-robot interaction
Comparing action-query strategies in semi-autonomous agents
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Designing robot learners that ask good questions
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Bayesian nonparametric inverse reinforcement learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Active learning of inverse models with intrinsically motivated goal exploration in robots
Robotics and Autonomous Systems
Human behavior understanding for robotics
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Learning the combinatorial structure of demonstrated behaviors with inverse feedback control
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Machine learning for interactive systems and robots: a brief introduction
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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Inverse reinforcement learning addresses the general problem of recovering a reward function from samples of a policy provided by an expert/demonstrator. In this paper, we introduce active learning for inverse reinforcement learning. We propose an algorithm that allows the agent to query the demonstrator for samples at specific states, instead of relying only on samples provided at "arbitrary" states. The purpose of our algorithm is to estimate the reward function with similar accuracy as other methods from the literature while reducing the amount of policy samples required from the expert. We also discuss the use of our algorithm in higher dimensional problems, using both Monte Carlo and gradient methods. We present illustrative results of our algorithm in several simulated examples of different complexities.