# A Comparative User Evaluation of XRL Explanations using Goal Identification

arXiv: https://arxiv.org/abs/2510.16956 · HTML: https://mlsanity.com/paper/2510.16956/

## Key facts

- Cited by 1 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 11 of its 19 references that are in the map.
- First posted on arXiv 19 Oct 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, 11 of 11)

- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [Explainable Reinforcement Learning: A Survey](https://mlsanity.com/paper/2005.06247/) (2020)
- [Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution](https://mlsanity.com/paper/1912.12191/) (2019)
- [Transparency and Explanation in Deep Reinforcement Learning Neural Networks](https://mlsanity.com/paper/1809.06061/) (2018)
- [GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanations](https://mlsanity.com/paper/2302.12689/) (2023)
- [Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents](https://mlsanity.com/paper/2210.11825/) (2022)
- [Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC](https://mlsanity.com/paper/2411.03820/) (2024)
- [Experiential Explanations for Reinforcement Learning](https://mlsanity.com/paper/2210.04723/) (2022)
- [Objective Metrics for Human-Subjects Evaluation in Explainable Reinforcement Learning](https://mlsanity.com/paper/2501.19256/) (2025)

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

- [Evaluating RL Explainability Methods by How Much They Help Fix Bugs in Agents](https://mlsanity.com/paper/2608.17524/) (2026)

## Abstract (arXiv)

Debugging is a core application of explainable reinforcement learning (XRL) algorithms; however, limited comparative evaluations have been conducted to understand their relative performance. We propose a novel evaluation methodology to test whether users can identify an agent's goal from an explanation of its decision-making. Utilising the Atari's Ms. Pacman environment and four XRL algorithms, we find that only one achieved greater than random accuracy for the tested goals and that users were generally overconfident in their selections. Further, we find that users' self-reported ease of identification and understanding for every explanation did not correlate with their accuracy.