Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning

Understand

Counterfactual explanations, which deal with "why not?" scenarios, can provide insightful explanations to an AI agent's behavior.

• In this work, we focus on generating counterfactual explanations for deep reinforcement learning (RL) agents which operate in visual input environments like Atari.
• We introduce counterfactual state explanations, a novel example-based approach to counterfactual explanations based on generative deep learning.
• Specifically, a counterfactual state illustrates what minimal change is needed to an Atari game image such that the agent chooses a different action.