Walking the Values in Bayesian Inverse Reinforcement Learning
<|reference_start|>Walking the Values in Bayesian Inverse Reinforcement Learning: The goal of Bayesian inverse reinforcement learning (IRL) is recovering a posterior distribution over reward functions using a set of demonstrations from an expert optimizing for a reward unknown to the learner. The resulting posterior over rewards can then be used to synthesize an apprentice policy that performs well on the same or a similar task. A key challenge in Bayesian IRL is bridging the computational gap between the hypothesis space of possible rewards and the likelihood, often defined in terms of Q values: vanilla Bayesian IRL needs to solve the costly forward planning problem - going from rewards to the Q values - at every step of the algorithm, which may need to be done thousands of times. We propose to solve this by a simple change: instead of focusing on primarily sampling in the space of rewards, we can focus on primarily working in the space of Q-values, since the computation required to go from Q-values to reward is radically cheaper. Furthermore, this reversion of the computation makes it easy to compute the gradient allowing efficient sampling using Hamiltonian Monte Carlo. We propose ValueWalk - a new Markov chain Monte Carlo method based on this insight - and illustrate its advantages on several tasks.<|reference_end|>
@article{bajgar2024walking,
    title={Walking the Values in Bayesian Inverse Reinforcement Learning},
    author={Ondrej Bajgar, Alessandro Abate, Konstantinos Gatsis and Michael A.
  Osborne},
    journal={arXiv preprint arXiv:2407.10971},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10971},
    primaryClass={cs.LG}
}