# Detecting Spiky Corruption in Markov Decision Processes

arXiv: https://arxiv.org/abs/1907.00452 · HTML: https://mlsanity.com/paper/1907.00452/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Is the Rush to Machine Learning Jeopardizing Safety? Results of a Survey](https://mlsanity.com/paper/2111.14324/) (2021)

## Abstract (arXiv)

Current reinforcement learning methods fail if the reward function is imperfect, i.e. if the agent observes reward different from what it actually receives. We study this problem within the formalism of Corrupt Reward Markov Decision Processes (CRMDPs). We show that if the reward corruption in a CRMDP is sufficiently "spiky", the environment is solvable. We fully characterize the regret bound of a Spiky CRMDP, and introduce an algorithm that is able to detect its corrupt states. We show that this algorithm can be used to learn the optimal policy with any common reinforcement learning algorithm. Finally, we investigate our algorithm in a pair of simple gridworld environments, finding that our algorithm can detect the corrupt states and learn the optimal policy despite the corruption.