# Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios

arXiv: https://arxiv.org/abs/2207.03214 · HTML: https://mlsanity.com/paper/2207.03214/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 16 of 16)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Programmatically Interpretable Reinforcement Learning](https://mlsanity.com/paper/1804.02477/) (2018)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [Visualizing and Understanding Atari Agents](https://mlsanity.com/paper/1711.00138/) (2017)
- [Explainable Reinforcement Learning: A Survey](https://mlsanity.com/paper/2005.06247/) (2020)
- [Interpretable Policies for Reinforcement Learning by Genetic Programming](https://mlsanity.com/paper/1712.04170/) (2017)
- [The Emerging Landscape of Explainable AI Planning and Decision Making](https://mlsanity.com/paper/2002.11697/) (2020)
- [Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations](https://mlsanity.com/paper/1702.07826/) (2017)
- [Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey](https://mlsanity.com/paper/2108.09003/) (2021)
- [Levels of explainable artificial intelligence for human-aligned conversational explanations](https://mlsanity.com/paper/2107.03178/) (2021)
- [Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario](https://mlsanity.com/paper/2006.13615/) (2020)
- [Distal Explanations for Model-free Explainable Reinforcement Learning](https://mlsanity.com/paper/2001.10284/) (2020)
- [Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment](https://mlsanity.com/paper/2007.03363/) (2020)
- [A Comparison of Humanoid Robot Simulators: A Quantitative Approach](https://mlsanity.com/paper/2008.04627/) (2020)
- [Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents](https://mlsanity.com/paper/2007.16045/) (2020)

## Built on by (most cited first, 5 of 5)

- [Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal](https://mlsanity.com/paper/2302.03180/) (2023)
- [Introspection-based Explainable Reinforcement Learning in Episodic and Non-episodic Scenarios](https://mlsanity.com/paper/2211.12930/) (2022)
- [Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario](https://mlsanity.com/paper/2212.06967/) (2022)
- [Trust Through Transparency: Explainable Social Navigation for Autonomous Mobile Robots via Vision-Language Models](https://mlsanity.com/paper/2504.05477/) (2025)
- [Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots](https://mlsanity.com/paper/2509.06768/) (2025)

## Abstract (arXiv)

Explainable artificial intelligence is a research field that tries to provide more transparency for autonomous intelligent systems. Explainability has been used, particularly in reinforcement learning and robotic scenarios, to better understand the robot decision-making process. Previous work, however, has been widely focused on providing technical explanations that can be better understood by AI practitioners than non-expert end-users. In this work, we make use of human-like explanations built from the probability of success to complete the goal that an autonomous robot shows after performing an action. These explanations are intended to be understood by people who have no or very little experience with artificial intelligence methods. This paper presents a user trial to study whether these explanations that focus on the probability an action has of succeeding in its goal constitute a suitable explanation for non-expert end-users. The results obtained show that non-expert participants rate robot explanations that focus on the probability of success higher and with less variance than technical explanations generated from Q-values, and also favor counterfactual explanations over standalone explanations.