Learning the Reward Function for a Misspecified Model

TL;DR

This paper introduces a new error bound for reward functions in flawed model-based reinforcement learning, extending existing algorithms and demonstrating significant empirical improvements in control when the dynamics model is imperfect.

Contribution

It proposes a novel error bound for reward functions in misspecified models and extends the Hallucinated DAgger-MC algorithm to improve performance under flawed dynamics.

Findings

The new bound accounts for reward behavior in sampled states.

Extended algorithm guarantees performance even with model errors.

Empirical results show substantial control improvements with flawed models.

Abstract

In model-based reinforcement learning it is typical to decouple the problems of learning the dynamics model and learning the reward function. However, when the dynamics model is flawed, it may generate erroneous states that would never occur in the true environment. It is not clear a priori what value the reward function should assign to such states. This paper presents a novel error bound that accounts for the reward model's behavior in states sampled from the model. This bound is used to extend the existing Hallucinated DAgger-MC algorithm, which offers theoretical performance guarantees in deterministic MDPs that do not assume a perfect model can be learned. Empirically, this approach to reward learning can yield dramatic improvements in control performance when the dynamics model is flawed.