A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations
<|reference_start|>A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations: This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficiency. First, we formulate an augmented policy loss combining a maximum entropy reinforcement learning objective with a behavioral cloning loss that leverages a forward dynamics model. Then, we propose an algorithm that automatically adjusts the weights of each component in the augmented loss function. Experiments on a variety of continuous control tasks demonstrate that the proposed algorithm outperforms various benchmarks by effectively utilizing available expert observations.<|reference_end|>
@article{ozcan2024a,
    title={A Model-Based Approach for Improving Reinforcement Learning Efficiency
  Leveraging Expert Observations},
    author={Erhan Can Ozcan, Vittorio Giammarino, James Queeney, Ioannis Ch.
  Paschalidis},
    journal={arXiv preprint arXiv:2402.18836},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18836},
    primaryClass={cs.LG}
}