# Abstracted Trajectory Visualization for Explainability in Reinforcement Learning

arXiv: https://arxiv.org/abs/2402.07928 · HTML: https://mlsanity.com/paper/2402.07928/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Interpretable and logic-guided RL, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## 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, 15 of 15)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Distributed Prioritized Experience Replay](https://mlsanity.com/paper/1803.00933/) (2018)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning](https://mlsanity.com/paper/1712.06567/) (2017)
- [Graying the black box: Understanding DQNs](https://mlsanity.com/paper/1602.02658/) (2016)
- [Understanding Hidden Memories of Recurrent Neural Networks](https://mlsanity.com/paper/1710.10777/) (2017)
- [Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences](https://mlsanity.com/paper/1807.08706/) (2018)
- [Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning](https://mlsanity.com/paper/2101.12446/) (2021)
- [An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents](https://mlsanity.com/paper/1812.07069/) (2018)
- [DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning](https://mlsanity.com/paper/1909.02982/) (2019)
- [ProjectionPathExplorer: Exploring Visual Patterns in Projected Decision-Making Paths](https://mlsanity.com/paper/2001.08372/) (2020)
- [Abstract representations of events arise from mental errors in learning and memory](https://mlsanity.com/paper/1805.12491/) (2018)

## Built on by (most cited first, 2 of 2)

- [Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents](https://mlsanity.com/paper/2505.11708/) (2025)
- [Know your Trajectory -- Trustworthy Reinforcement Learning deployment through Importance-Based Trajectory Analysis](https://mlsanity.com/paper/2512.06917/) (2025)

## Abstract (arXiv)

Explainable AI (XAI) has demonstrated the potential to help reinforcement learning (RL) practitioners to understand how RL models work. However, XAI for users who do not have RL expertise (non-RL experts), has not been studied sufficiently. This results in a difficulty for the non-RL experts to participate in the fundamental discussion of how RL models should be designed for an incoming society where humans and AI coexist. Solving such a problem would enable RL experts to communicate with the non-RL experts in producing machine learning solutions that better fit our society. We argue that abstracted trajectories, that depicts transitions between the major states of the RL model, will be useful for non-RL experts to build a mental model of the agents. Our early results suggest that by leveraging a visualization of the abstracted trajectories, users without RL expertise are able to infer the behavior patterns of RL.