Advanced · Reinforcement Learning

Explainable Reinforcement Learning (XRL)

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Methods that provide insights into the decision-making processes of reinforcement learning agents.

Technical Definition

Methods that provide insights into the decision-making processes of reinforcement learning agents.

How it works

XRL aims to make the actions and policies of complex RL agents transparent and understandable to human users. This is important for trust and debugging, especially in high-stakes applications like autonomous systems or medical robotics. It often involves visualizing policies, attention mechanisms, or reward functions.

Related Concepts

• Reinforcement Learning — A paradigm where an agent learns to make decisions by receiving rewards or penalties from its environment through trial and error.
• Explainable AI (XAI) — A set of techniques that allow humans to understand the output of AI models, especially deep learning models.
• Model Interpretability — The degree to which a human can understand the causality of an AI model's prediction.