Walking the Values in Bayesian Inverse Reinforcement Learning

• URL: http://arxiv.org/abs/2407.10971v1
• Date: Mon, 15 Jul 2024 17:59:52 GMT
• Title: Walking the Values in Bayesian Inverse Reinforcement Learning
• Authors: Ondrej Bajgar, Alessandro Abate, Konstantinos Gatsis, Michael A. Osborne,
• Abstract summary: Key challenge in Bayesian IRL is bridging the computational gap between the hypothesis space of possible rewards and the likelihood. We propose ValueWalk - a new Markov chain Monte Carlo method based on this insight.
• Score: 66.68997022043075
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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