TLDR: This research paper surveys over 250 works in Explainable Reinforcement Learning (XRL), proposing a new taxonomy based on ‘What’ (policy, sequence, action) and ‘How’ (methods like interpretable policies, policy summaries, visual analysis, counterfactuals, feature importance, and expected outcomes) explanations are provided. It also highlights related AI fields and critical needs for XRL, such as standardized comparisons, better metrics, user studies, and improved user interfaces.
Artificial Intelligence (AI) models, especially those using deep neural networks, have achieved remarkable success in various fields. However, their internal workings often remain a mystery, leading to what is commonly referred to as the “black box” problem. This lack of transparency can make it difficult to understand why an AI makes a particular decision, especially in critical applications like autonomous driving or medical diagnosis.
To address this challenge, the field of eXplainable AI (XAI) has emerged, focusing on methods to make AI models more understandable. Within XAI, a specialized area known as eXplainable Reinforcement Learning (XRL) aims to shed light on the actions of agents that learn through reinforcement learning (RL). RL agents learn by interacting with an environment, receiving rewards or penalties for their actions, and striving to maximize their cumulative reward over time. Understanding their decision-making process is crucial for trust and improvement.
A recent survey paper, titled “A Survey of Explainable Reinforcement Learning : Targets, Methods and Needs,” provides a comprehensive review of over 250 papers in the XRL domain. Authored by L´ eo Sauli` eres, this paper introduces an intuitive way to categorize XRL methods based on two fundamental questions: ‘What’ is being explained, and ‘How’ is the explanation provided? This taxonomy helps researchers and practitioners navigate the diverse landscape of XRL techniques.
What is Being Explained? The Targets of XRL
The survey identifies three main targets for explanation in XRL:
- The Agent’s Policy: This refers to the overall strategy or behavior the agent has learned. Explanations at this level aim to make the agent’s general decision-making transparent.
- A Specific Sequence of Interactions: Here, the focus is on understanding a particular series of actions taken by the agent within its environment. This helps in analyzing specific scenarios or trajectories.
- A Particular Action: This is the most granular level, explaining why an agent chose a specific action in a given situation.
How are Explanations Provided? The Methods of XRL
The ‘How’ aspect of the taxonomy delves into the different techniques used to generate explanations. These methods can broadly be grouped into several categories:
- Interpretable Policies: Some methods aim to make the agent’s policy inherently understandable from the start, often by designing the learning process to produce transparent models like decision trees or rule sets. Others use “surrogate models” – simpler, interpretable models that mimic the behavior of a complex, opaque RL agent.
- Policy Summaries: Instead of explaining every detail, these methods provide a high-level overview of the agent’s behavior. This can involve highlighting important sequences of actions, identifying “critical states” where the agent’s decisions are particularly impactful, or using techniques like SHAP (Shapley Additive Explanations) to show the overall influence of different features on the policy.
- Human-readable MDPs: This approach focuses on making the environment’s dynamics (how states change and rewards are given) more understandable. Techniques include clustering similar states into abstract groups or transforming state representations into more intuitive forms. Relational Reinforcement Learning (RRL) also falls into this category, representing environments using objects and their relationships.
- Visual Analysis: For agents that process visual information (like in video games), visual tools are crucial. Saliency maps, for instance, highlight which parts of an image the agent is “looking at” when making a decision. Other tools provide interactive interfaces to explore the agent’s internal states and learning process.
- Counterfactual Explanations: These explanations answer “what if” questions, such as “What if the agent had taken a different action?” or “What minimal changes to the environment would have led to a different outcome?” This helps in understanding the agent’s sensitivities and alternative possibilities.
- Feature Importance: This category focuses on identifying which specific features of the agent’s current state are most influential in its decision to take a particular action. Methods like SHAP and LIME (Local Interpretable Model-agnostic Explanations) are often adapted from general XAI to XRL for this purpose.
- Expected Outcomes: Instead of focusing on inputs, these explanations justify an action by showing its predicted future consequences, such as the resulting state, the expected rewards, or the probability of achieving certain sub-goals.
Also Read:
- Unpacking Optimization Decisions: A New Method for Explaining Mixed-Integer Linear Programs
- Unlocking AI’s Black Box: A New Method for Explaining Model Predictions
Related Domains and Future Needs
The survey also points to several related AI domains that could further enrich XRL research, including Explainable Planning (XAIP), Model Checking (for verifying properties of AI systems), and Algorithmic Recourse (for providing actionable recommendations to change an outcome). These fields offer valuable tools and perspectives for enhancing transparency in RL.
Finally, the paper identifies several critical needs for the XRL community to mature:
- Comparing Methods: There’s a strong need for standardized benchmarks and methodologies to rigorously compare different XRL techniques.
- Providing Metrics: Developing a unified set of metrics to evaluate the quality and effectiveness of explanations is essential.
- Performing User Studies: Given that explanations are ultimately for human understanding, more user studies are needed to validate XRL methods with real users, whether they are domain experts or laypersons.
- Developing User Interfaces: Intuitive and ergonomic user interfaces are crucial for making XRL methods accessible and useful to end-users.
This comprehensive survey serves as a vital resource for anyone interested in making reinforcement learning agents more transparent and understandable. It highlights the progress made and outlines a clear path for future research in this rapidly evolving field. For more in-depth details, you can refer to the full research paper: A Survey of Explainable Reinforcement Learning : Targets, Methods and Needs.
