# Explainable Reinforcement Learning Through a Causal Lens

## TL;DR

This paper introduces a causal model-based approach to explain reinforcement learning agent behavior, improving understanding, satisfaction, and trust through counterfactual analysis, validated by a human study in a complex game environment.

## Contribution

It presents a novel method that learns causal models during reinforcement learning to generate explanations, bridging cognitive science theories with AI interpretability.

## Key findings

- Causal explanations improved task prediction accuracy.
- Participants reported higher satisfaction with causal explanations.
- Trust in agents increased with causal model explanations.

## Abstract

Prevalent theories in cognitive science propose that humans understand and represent the knowledge of the world through causal relationships. In making sense of the world, we build causal models in our mind to encode cause-effect relations of events and use these to explain why new events happen. In this paper, we use causal models to derive causal explanations of behaviour of reinforcement learning agents. We present an approach that learns a structural causal model during reinforcement learning and encodes causal relationships between variables of interest. This model is then used to generate explanations of behaviour based on counterfactual analysis of the causal model. We report on a study with 120 participants who observe agents playing a real-time strategy game (Starcraft II) and then receive explanations of the agents' behaviour. We investigated: 1) participants' understanding gained by explanations through task prediction; 2) explanation satisfaction and 3) trust. Our results show that causal model explanations perform better on these measures compared to two other baseline explanation models.