# Know your Trajectory -- Trustworthy Reinforcement Learning deployment through Importance-Based Trajectory Analysis

arXiv: https://arxiv.org/abs/2512.06917 · HTML: https://mlsanity.com/paper/2512.06917/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Interpretable and logic-guided RL, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 7 Dec 2025.

## Placed in

- Deep reinforcement learning methods / [Interpretable and logic-guided RL](https://mlsanity.com/deeprl/interpretable-and-logic-guided-rl/)

## Builds on (most cited first, 3 of 3)

- [Re-understanding Finite-State Representations of Recurrent Policy Networks](https://mlsanity.com/paper/2006.03745/) (2020)
- [Explaining Reinforcement Learning Policies through Counterfactual Trajectories](https://mlsanity.com/paper/2201.12462/) (2022)
- [Abstracted Trajectory Visualization for Explainability in Reinforcement Learning](https://mlsanity.com/paper/2402.07928/) (2024)

## Abstract (arXiv)

As Reinforcement Learning (RL) agents are increasingly deployed in real-world applications, ensuring their behavior is transparent and trustworthy is paramount. A key component of trust is explainability, yet much of the work in Explainable RL (XRL) focuses on local, single-step decisions. This paper addresses the critical need for explaining an agent's long-term behavior through trajectory-level analysis. We introduce a novel framework that ranks entire trajectories by defining and aggregating a new state-importance metric. This metric combines the classic Q-value difference with a "radical term" that captures the agent's affinity to reach its goal, providing a more nuanced measure of state criticality. We demonstrate that our method successfully identifies optimal trajectories from a heterogeneous collection of agent experiences. Furthermore, by generating counterfactual rollouts from critical states within these trajectories, we show that the agent's chosen path is robustly superior to alternatives, thereby providing a powerful "Why this, and not that?" explanation. Our experiments in standard OpenAI Gym environments validate that our proposed importance metric is more effective at identifying optimal behaviors compared to classic approaches, offering a significant step towards trustworthy autonomous systems.