Explainable Deep Reinforcement Learning: State of the Art and Challenges (2301.09937v1)

Abstract: Interpretability, explainability and transparency are key issues to introducing Artificial Intelligence methods in many critical domains: This is important due to ethical concerns and trust issues strongly connected to reliability, robustness, auditability and fairness, and has important consequences towards keeping the human in the loop in high levels of automation, especially in critical cases for decision making, where both (human and the machine) play important roles. While the research community has given much attention to explainability of closed (or black) prediction boxes, there are tremendous needs for explainability of closed-box methods that support agents to act autonomously in the real world. Reinforcement learning methods, and especially their deep versions, are such closed-box methods. In this article we aim to provide a review of state of the art methods for explainable deep reinforcement learning methods, taking also into account the needs of human operators - i.e., of those that take the actual and critical decisions in solving real-world problems. We provide a formal specification of the deep reinforcement learning explainability problems, and we identify the necessary components of a general explainable reinforcement learning framework. Based on these, we provide a comprehensive review of state of the art methods, categorizing them in classes according to the paradigm they follow, the interpretable models they use, and the surface representation of explanations provided. The article concludes identifying open questions and important challenges.

• The paper presents a comprehensive overview of XDRL by categorizing methods into generic, interpretable box design, and distillation paradigms to explain DRL decisions.
• It details the implementation of techniques like LIME, SHAP, attention mechanisms, and contrastive explanations to enhance transparency and trust in AI systems.
• The study emphasizes the need for standardized metrics and experimental protocols to effectively evaluate explanation quality in complex deep RL environments.

Explainable Deep Reinforcement Learning: A State of the Art Overview

Introduction to Explainability in Deep Reinforcement Learning

The paper "Explainable Deep Reinforcement Learning: State of the Art and Challenges" (2301.09937) provides a comprehensive overview of methodologies in the domain of explainable deep reinforcement learning (XDRL). It addresses the necessity of explainability in AI systems, particularly in reinforcement learning (RL) methods that involve deep neural network models (DRL). Explainability is crucial not only due to ethical considerations but also for ensuring trust, reliability, robustness, auditability, and fairness in AI systems. The paper discusses the main components of XDRL approaches, categorizes various methodologies, and highlights key challenges faced in the field.

Key Concepts and Definitions

The paper distinguishes between interpretability, explainability, and transparency. Interpretability refers to the degree to which a human can understand the cause of a decision. Explainability involves conveying the system’s workings in an accessible manner, often post-hoc. Transparency is described as the clarity and openness regarding the system's operations in context.

To formalize these concepts in the context of DRL, the paper defines specific problems like the model explanation problem, outcome explanation problem, and model inspection problem. These problems aim to offer explanations for the policy logic, specific responses, and internal mechanics of the DRL methods.

Framework for Explainability in DRL

The paper provides a blueprint for XDRL methods, incorporating existing DRL frameworks. It emphasizes interpretable models within DRL architectures, which can be trained alongside or independently from DRL models. The approaches are categorized mainly into three paradigms: generic methods, interpretable box design, and distillation/mimicking methods.

1. Generic Methods: These include techniques like LIME or SHAP that are model-agnostic and can provide explanations for any machine learning method, including DRL models.
2. Interpretable Box Design: This paradigm focuses on designing inherently interpretable models, use interpretable policy representations such as decision trees or hierarchical plans.
3. Distillation and Mimicking: This involves training an interpretable model to mimic the DRL model’s decision-making process or distill the DRL knowledge into a simpler model for explanation purposes.

Review of State of the Art Methods

The paper meticulously reviews multiple state-of-the-art methodologies and frameworks in XDRL:

• Highlighting Trajectories: Methods like HIGHLIGHTS utilize trajectory summarization focusing on important state-action pairs for behavior understanding.
• Contrastive Explanations: Offering explanations by comparing the agent’s policy with user-specified alternatives to elucidate decision differentiation.
• Attention Mechanisms: Implementing attention-based models that reason over visual inputs to highlight task-related features, such as decisions based on salient visual aspects.
• Critical States Identification: Focusing on key states that heavily influence agent behavior to establish trust or diagnose policy quality.
• Opportunity Chains: Using causal explanations to reveal long-term dependencies between events, improving understanding of agent strategies.

Conclusions and Future Perspectives

The paper concludes by emphasizing the need for a systematic toolbox for explainable DRL, comprehensive model evaluation, and addressing transparency requirements. It advocates for experimental protocols and standards to better assess the effectiveness and user-acceptance of explanations. The need for more research into explainability tailored to practical and ethical deployment issues in diverse domains is underscored.

Critical challenges include the exploration of interpretable box design paradigms, addressing explainability in complex multi-agent settings, and establishing universally agreed-upon metrics and protocols for explanation quality. The development of principles and methodologies for embedding explainability and transparency from the inception of DRL systems design remains a pivotal future direction. The paper serves as both a guidepost and a call to action for continued advancement in explainable DRL methodologies.