Investigating the extent to which inverse reinforcement learning can learn Rrewards from noisy demonstrations

Abstract

Inverse Reinforcement Learning (IRL) aims to recover a reward function from expert demonstrations in a Markov Decision Process (MDP). The objective is to understand the underlying intentions and behaviors of experts and derive a reward function based on their reasoning, rather than their exact actions. However, expert demonstrations can be influenced by various types of noise (e.g., from random behavior) which can affect their accuracy and effectiveness in solving the MDP. This research investigates the capability of IRL to recover reward functions from noisy demonstrations. Three types of noises, namely Random Action Noise, Random Bias Noise, and Sparse Noise, are introduced and modeled. Demonstrations are generated with these noises, and the corresponding reward functions are recovered. Comparisons are made between the noisy and optimal recovered rewards using various metrics. The results indicate that IRL exhibits certain tolerance level against Random Events and Sparse Noise, while being more vulnerable to Random Bias Noise.