# Detecting Rewards Deterioration in Episodic Reinforcement Learning

arXiv: https://arxiv.org/abs/2010.11660 · HTML: https://mlsanity.com/paper/2010.11660/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [Train Hard, Fight Easy: Robust Meta Reinforcement Learning](https://mlsanity.com/paper/2301.11147/) (2023)
- [Task-Relevant Failure Detection for Trajectory Predictors in Autonomous Vehicles](https://mlsanity.com/paper/2207.12380/) (2022)
- [Novelty Detection in Reinforcement Learning with World Models](https://mlsanity.com/paper/2310.08731/) (2023)
- [Align to Structure: Aligning Large Language Models with Structural Information](https://mlsanity.com/paper/2504.03622/) (2025)
- [Task-Driven Detection of Distribution Shifts with Statistical Guarantees for Robot Learning](https://mlsanity.com/paper/2106.13703/) (2021)
- [e-COP : Episodic Constrained Optimization of Policies](https://mlsanity.com/paper/2406.09563/) (2024)
- [Continuous Forecasting via Neural Eigen Decomposition](https://mlsanity.com/paper/2202.00117/) (2022)
- [Individualized Dosing Dynamics via Neural Eigen Decomposition](https://mlsanity.com/paper/2306.14020/) (2023)

## Abstract (arXiv)

In many RL applications, once training ends, it is vital to detect any deterioration in the agent performance as soon as possible. Furthermore, it often has to be done without modifying the policy and under minimal assumptions regarding the environment. In this paper, we address this problem by focusing directly on the rewards and testing for degradation. We consider an episodic framework, where the rewards within each episode are not independent, nor identically-distributed, nor Markov. We present this problem as a multivariate mean-shift detection problem with possibly partial observations. We define the mean-shift in a way corresponding to deterioration of a temporal signal (such as the rewards), and derive a test for this problem with optimal statistical power. Empirically, on deteriorated rewards in control problems (generated using various environment modifications), the test is demonstrated to be more powerful than standard tests - often by orders of magnitude. We also suggest a novel Bootstrap mechanism for False Alarm Rate control (BFAR), applicable to episodic (non-i.i.d) signal and allowing our test to run sequentially in an online manner. Our method does not rely on a learned model of the environment, is entirely external to the agent, and in fact can be applied to detect changes or drifts in any episodic signal.