Counterfactual Explanations for Continuous Action Reinforcement Learning
Reinforcement Learning (RL) has shown great promise in domains like healthcare and robotics but often struggles with adoption due to its lack of interpretability. Counterfactual explanations, which address "what if" scenarios, provide a promising avenue for understanding RL decisions but remain underexplored for continuous action spaces. We propose a novel approach for generating counterfactual explanations in continuous action RL by computing alternative action sequences that improve outcomes while minimizing deviations from the original sequence. Our approach leverages a distance metric for continuous actions and accounts for constraints such as adhering to predefined policies in specific states. Evaluations in two RL domains, Diabetes Control and Lunar Lander, demonstrate the effectiveness, efficiency, and generalization of our approach, enabling more interpretable and trustworthy RL applications.
Code (1)
Tasks
counterfactualreinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI
Counterfactual explanations are increasingly used to address interpretability, recourse, and bias in AI decisions. However, we do not know how well counterfactual explanations help users to understand a systems decisions…
counterfactualExplainable Artificial Intelligence (XAI)Counterfactual Explanation for Fairness in Recommendation
Fairness-aware recommendation eliminates discrimination issues to build trustworthy recommendation systems.Explaining the causes of unfair recommendations is critical, as it promotes fairness diagnostics, and thus secure…
AttributeCausal InferencecounterfactualCounterfactual Explanation+2ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies
Understanding how failure occurs and how it can be prevented in reinforcement learning (RL) is necessary to enable debugging, maintain user trust, and develop personalized policies. Counterfactual reasoning has often bee…
counterfactualCounterfactual ReasoningDiversityreinforcement-learning+1Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning
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 le…
counterfactualDeep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities
While AI algorithms have shown remarkable success in various fields, their lack of transparency hinders their application to real-life tasks. Although explanations targeted at non-experts are necessary for user trust and…
counterfactualreinforcement-learningReinforcement LearningReinforcement Learning (RL)