ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies
This academic paper presents ACTER (Actionable CounTerfactual Sequences for Explaining Reinforcement Learning Outcomes), a new algorithm for generating counterfactual sequences to help understand and prevent failures in reinforcement learning. The…
Continue reading