Corpus ID: 13973870

Robust Bayesian Inverse Reinforcement Learning with Sparse Behavior Noise

@inproceedings{Zheng2014RobustBI,
  title={Robust Bayesian Inverse Reinforcement Learning with Sparse Behavior Noise},
  author={Jiangchuan Zheng and S. Liu and L. Ni},
  booktitle={AAAI},
  year={2014}
}
  • Jiangchuan Zheng, S. Liu, L. Ni
  • Published in AAAI 2014
  • Computer Science
  • Inverse reinforcement learning (IRL) aims to recover the reward function underlying a Markov Decision Process from behaviors of experts in support of decision-making. Most recent work on IRL assumes the same level of trustworthiness of all expert behaviors, and frames IRL as a process of seeking reward function that makes those behaviors appear (near)- optimal. However, it is common in reality that noisy expert behaviors disobeying the optimal policy exist, which may degrade the IRL performance… CONTINUE READING
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    References

    SHOWING 1-10 OF 28 REFERENCES
    Maximum Entropy Inverse Reinforcement Learning
    • 1,477
    • Highly Influential
    • PDF
    Bayesian Inverse Reinforcement Learning
    • 486
    • Highly Influential
    • PDF
    Apprenticeship learning via inverse reinforcement learning
    • 2,018
    • PDF
    Active Learning for Reward Estimation in Inverse Reinforcement Learning
    • 153
    • PDF
    Inverse Reinforcement Learning with PI 2
    • 9
    • PDF
    Nonlinear Inverse Reinforcement Learning with Gaussian Processes
    • 235
    • PDF
    Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods
    • 202
    • PDF
    Bayesian Multitask Inverse Reinforcement Learning
    • 76
    • PDF
    Maximum margin planning
    • 536
    • PDF
    Supervised Probabilistic Robust Embedding with Sparse Noise
    • 1
    • PDF