Reinforcement Learning with Perturbed Rewards

Understand

Recent studies have shown that reinforcement learning (RL) models are vulnerable in various noisy scenarios.

• For instance, the observed reward channel is often subject to noise in practice (e.g., when rewards are collected through sensors), and is therefore not credible.
• In addition, for applications such as robotics, a deep reinforcement learning (DRL) algorithm can be manipulated to produce arbitrary errors by receiving corrupted rewards.
• In this paper, we consider noisy RL problems with perturbed rewards, which can be approximated with a confusion matrix.