Why The Agent Made That Decision: Contrastive Explanation Learning For Reinforcement Learning
2024 · Rui Zuo, Simon Khan, Zifan Wang, et al.
Abstract
Reinforcement learning (RL) has demonstrated remarkable success in solving complex decision-making problems, yet its adoption in critical domains is hindered by the lack of interpretability in its decision-making processes. Existing explainable AI (xAI) approaches often fail to provide meaningful explanations for RL agents, particularly because they overlook the contrastive nature of human reasoning--answering "why this action instead of that one?". To address this gap, we propose a novel framework of contrastive learning to explain RL selected actions, named \(\textbf\{VisionMask\}\). VisionMask is trained to generate explanations by explicitly contrasting the agent's chosen action with alternative actions in a given state using a self-supervised manner. We demonstrate the efficacy of our method through experiments across diverse RL environments, evaluating it in terms of faithfulness, robustness, and complexity. Our results show that VisionMask significantly improves human understand
Authors
(none)
Tags
Stats
Related papers
- Experiential Explanations For Reinforcement Learning (2022)2.26
- Contrastive Explanations For Reinforcement Learning In Terms Of Expected Consequences (2018)0.00
- Ganterfactual-rl: Understanding Reinforcement Learning Agents' Strategies Through Visual Counterfactual Explanations (2023)2.26
- Redefining Counterfactual Explanations For Reinforcement Learning: Overview, Challenges And Opportunities (2022)0.00
- Explaining Reinforcement Learning Agents Through Counterfactual Action Outcomes (2023)5.84
- Talktoagent: A Human-centric Explanation Of Reinforcement Learning Agents With Large Language Models (2025)0.00
- MAGIC-MASK: Multi-agent Guided Inter-agent Collaboration With Mask-based Explainability For Reinforcement Learning (2025)0.00
- (when) Are Contrastive Explanations Of Reinforcement Learning Helpful? (2022)0.00