# Explaining Reinforcement Learning Decisions in Self-adaptive Systems

arXiv: https://arxiv.org/abs/2608.14620 · HTML: https://mlsanity.com/paper/2608.14620/

## 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 17 of its 31 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## 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, 17 of 17)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Visualizing and Understanding Atari Agents](https://mlsanity.com/paper/1711.00138/) (2017)
- [Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution](https://mlsanity.com/paper/1912.12191/) (2019)
- [Counterfactual Explanations for Machine Learning: Challenges Revisited](https://mlsanity.com/paper/2106.07756/) (2021)
- [GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanations](https://mlsanity.com/paper/2302.12689/) (2023)
- [Counterfactual States for Atari Agents via Generative Deep Learning](https://mlsanity.com/paper/1909.12969/) (2019)
- [Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2210.11846/) (2022)
- [Automatic Discovery of Interpretable Planning Strategies](https://mlsanity.com/paper/2005.11730/) (2020)
- [CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning](https://mlsanity.com/paper/2308.06594/) (2023)
- [RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning](https://mlsanity.com/paper/2303.04475/) (2023)
- [Making Things Explainable vs Explaining: Requirements and Challenges under the GDPR](https://mlsanity.com/paper/2110.00758/) (2021)
- [ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning](https://mlsanity.com/paper/2203.11211/) (2022)
- [Enhancing Human-in-the-Loop Adaptive Systems through Digital Twins and VR Interfaces](https://mlsanity.com/paper/2103.10804/) (2021)
- [A User Study on Explainable Online Reinforcement Learning for Adaptive Systems](https://mlsanity.com/paper/2307.04098/) (2023)
- [Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems](https://mlsanity.com/paper/2408.01188/) (2024)

## Abstract (arXiv)

Reinforcement Learning (RL) has been extensively used in autonomous and self-* systems, but RL policies, especially deep RL ones relying on neural networks, lack transparency and are difficult to understand. This can lead to diminished user trust, and makes for a more challenging verification of systems. To address this challenge, this paper introduces Explanations using Alternative Realities for Reinforcement Learning (EARL), a Python library to produce counterfactual explanations in RL settings. This library allows the user to produce explanations by exploring What-if scenarios to clarify agent behavior by comparing possible outcomes. Counterfactual explanations have been shown to be intuitive and user-friendly in psychology research, but have only recently been explored in RL, with existing implementations usually limited to toy examples and benchmarks. EARL supports counterfactual explanation generation in realistic RL-based self-adaptive systems. To demonstrate its applicability, we demonstrate its use in a simulation of CitiBikes, a self-adaptive bike-sharing system, and we provide evaluations showing how it performs in real applications.