# Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agents

arXiv: https://arxiv.org/abs/1810.08811 · HTML: https://mlsanity.com/paper/1810.08811/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2018.

## Built on by (most cited first, 10 of 10)

- [A Survey on Interpretable Reinforcement Learning](https://mlsanity.com/paper/2112.13112/) (2021)
- [Explainable Deep Reinforcement Learning: State of the Art and Challenges](https://mlsanity.com/paper/2301.09937/) (2023)
- [Explainable Reinforcement Learning via a Causal World Model](https://mlsanity.com/paper/2305.02749/) (2023)
- [Explainable Autonomous Robots: A Survey and Perspective](https://mlsanity.com/paper/2105.02658/) (2021)
- [Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents](https://mlsanity.com/paper/2112.09462/) (2021)
- [A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges](https://mlsanity.com/paper/2211.06665/) (2022)
- [Explainable Goal Recognition: A Framework Based on Weight of Evidence](https://mlsanity.com/paper/2303.05622/) (2023)
- [XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques](https://mlsanity.com/paper/2402.12685/) (2024)
- [One-shot Policy Elicitation via Semantic Reward Manipulation](https://mlsanity.com/paper/2101.01860/) (2021)
- [Continual Adversarial Reinforcement Learning (CARL) of False Data Injection detection: forgetting and explainability](https://mlsanity.com/paper/2411.10367/) (2024)

## Abstract (arXiv)

In cooperation, the workers must know how co-workers behave. However, an agent's policy, which is embedded in a statistical machine learning model, is hard to understand, and requires much time and knowledge to comprehend. Therefore, it is difficult for people to predict the behavior of machine learning robots, which makes Human Robot Cooperation challenging. In this paper, we propose Instruction-based Behavior Explanation (IBE), a method to explain an autonomous agent's future behavior. In IBE, an agent can autonomously acquire the expressions to explain its own behavior by reusing the instructions given by a human expert to accelerate the learning of the agent's policy. IBE also enables a developmental agent, whose policy may change during the cooperation, to explain its own behavior with sufficient time granularity.