# Distal Explanations for Model-free Explainable Reinforcement Learning

arXiv: https://arxiv.org/abs/2001.10284 · HTML: https://mlsanity.com/paper/2001.10284/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 12 of 12)

- [Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero](https://mlsanity.com/paper/2310.16410/) (2023)
- [Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods](https://mlsanity.com/paper/2102.13045/) (2021)
- [Explainable Autonomous Robots: A Survey and Perspective](https://mlsanity.com/paper/2105.02658/) (2021)
- [Explainability in reinforcement learning: perspective and position](https://mlsanity.com/paper/2203.11547/) (2022)
- [Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents](https://mlsanity.com/paper/2112.09462/) (2021)
- [Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios](https://mlsanity.com/paper/2207.03214/) (2022)
- [Seven challenges for harmonizing explainability requirements](https://mlsanity.com/paper/2108.05390/) (2021)
- [ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies](https://mlsanity.com/paper/2402.06503/) (2024)
- [Leveraging Reward Consistency for Interpretable Feature Discovery in Reinforcement Learning](https://mlsanity.com/paper/2309.01458/) (2023)
- [A Framework of Explanation Generation toward Reliable Autonomous Robots](https://mlsanity.com/paper/2105.02670/) (2021)
- [M2-PALE: A Framework for Explaining Multi-Agent MCTS--Minimax Hybrids via Process Mining and LLMs](https://mlsanity.com/paper/2604.14687/) (2026)
- [Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees](https://mlsanity.com/paper/2606.29681/) (2026)

## Abstract (arXiv)

In this paper we introduce and evaluate a distal explanation model for model-free reinforcement learning agents that can generate explanations for `why' and `why not' questions. Our starting point is the observation that causal models can generate opportunity chains that take the form of `A enables B and B causes C'. Using insights from an analysis of 240 explanations generated in a human-agent experiment, we define a distal explanation model that can analyse counterfactuals and opportunity chains using decision trees and causal models. A recurrent neural network is employed to learn opportunity chains, and decision trees are used to improve the accuracy of task prediction and the generated counterfactuals. We computationally evaluate the model in 6 reinforcement learning benchmarks using different reinforcement learning algorithms. From a study with 90 human participants, we show that our distal explanation model results in improved outcomes over three scenarios compared with two baseline explanation models.