Counterfactual States for Atari Agents via Generative Deep Learning

Understand

Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans.

• To address this problem, past work has mainly focused on explaining why an action was chosen in a given state.
• A different type of explanation that is useful is a counterfactual, which deals with "what if?" scenarios.
• In this work, we introduce the concept of a counterfactual state to help humans gain a better understanding of what would need to change (minimally) in an Atari game image for the agent to choose a different action.