Counterfactual States For Atari Agents Via Generative Deep Learning

Abstract

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. We introduce a novel method to create counterfactual states from a generative deep learning architecture. In addition, we evaluate the effectiveness of counterfactual states on human participants who are not machine learning experts. Our user study results suggest that our generated counterfactual states are useful in helping non-expert participants gain a better understanding of an agent's decision making proce

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyolson2019counterfactual

Related papers

• Counterfactual State Explanations For Reinforcement Learning Agents Via Generative Deep Learning (2021)13.23
• Competitive Multi-agent Deep Reinforcement Learning With Counterfactual Thinking (2019)7.16
• Ganterfactual-rl: Understanding Reinforcement Learning Agents' Strategies Through Visual Counterfactual Explanations (2023)2.26
• Explaining Deep Reinforcement Learning Agents In The Atari Domain Through A Surrogate Model (2021)0.00
• Visualizing And Understanding Atari Agents (2017)0.00
• Playing Atari With Six Neurons (2018)0.00
• Reward Learning From Human Preferences And Demonstrations In Atari (2018)0.00
• Learn To Interpret Atari Agents (2018)0.00