מושגי ליבה
The core message of this paper is that a Bayesian approach to model-based inverse reinforcement learning (BM-IRL) can lead to robust policies by simultaneously estimating the expert's reward function and their internal model of the environment dynamics. This is achieved by incorporating a prior that encodes the accuracy of the expert's dynamics model, which encourages the learner to plan against the worst-case dynamics outside the offline data distribution.
תקציר
The paper proposes a Bayesian approach to model-based inverse reinforcement learning (BM-IRL) that differs from existing offline model-based IRL methods by performing simultaneous estimation of the expert's reward function and their subjective model of the environment dynamics.
The key insights are:
- By using a class of priors that parameterizes how accurate the expert's model of the environment is, the BM-IRL framework can learn robust policies that exhibit good performance even when the expert is believed to have a highly accurate model of the environment.
- This connection to robust MDP allows the authors to derive a more efficient algorithm called RM-IRL that exploits this observation.
- The authors provide performance guarantees showing that the policy and dynamics estimation errors affect the learner's performance in the real environment.
The paper evaluates the proposed algorithms on MuJoCo continuous control benchmarks and shows that they outperform state-of-the-art offline IRL methods without the need for designing ad-hoc pessimistic penalties.
סטטיסטיקה
The paper does not contain any key metrics or important figures to support the author's key logics.
ציטוטים
The paper does not contain any striking quotes supporting the author's key logics.