# DEEDEE: Fast and Scalable Out-of-Distribution Dynamics Detection

arXiv: https://arxiv.org/abs/2510.21638 · HTML: https://mlsanity.com/paper/2510.21638/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 24 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents](https://mlsanity.com/paper/2505.02077/) (2025)
- [Measuring the Reliability of Reinforcement Learning Algorithms](https://mlsanity.com/paper/1912.05663/) (2019)
- [High-dimensional, multiscale online changepoint detection](https://mlsanity.com/paper/2003.03668/) (2020)
- [Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and Detection](https://mlsanity.com/paper/2404.07099/) (2024)
- [Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning](https://mlsanity.com/paper/2001.00496/) (2019)
- [Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and Results](https://mlsanity.com/paper/2107.04982/) (2021)
- [Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning](https://mlsanity.com/paper/2112.02694/) (2021)
- [Guaranteeing Out-Of-Distribution Detection in Deep RL via Transition Estimation](https://mlsanity.com/paper/2503.05238/) (2025)

## Abstract (arXiv)

Deploying reinforcement learning (RL) in safety-critical settings is constrained by brittleness under distribution shift. We study out-of-distribution (OOD) detection for RL time series and introduce DEEDEE, a two-statistic detector that revisits representation-heavy pipelines with a minimal alternative. DEEDEE uses only an episodewise mean and an RBF kernel similarity to a training summary, capturing complementary global and local deviations. Despite its simplicity, DEEDEE matches or surpasses contemporary detectors across standard RL OOD suites, delivering a 600-fold reduction in compute (FLOPs / wall-time) and an average 5% absolute accuracy gain over strong baselines. Conceptually, our results indicate that diverse anomaly types often imprint on RL trajectories through a small set of low-order statistics, suggesting a compact foundation for OOD detection in complex environments.