Reinforcement Learning with Perturbed Rewards

Jingkang Wang
Jingkang Wang
Yang Liu
Yang Liu

national conference on artificial intelligence, 2020.

Cited by: 7|Bibtex|Views17|Links

Abstract:

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 reinforceme...More

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