55 citations · 81 across the 18 of their papers we have counts for
4 papers · 1 filter
From Actions to Words: Towards Abstractive-Textual Policy Summarization in RL
Sahar Admoni, Assaf Hallak, Yftah Ziser +2
Explaining reinforcement learning agents is challenging because policies emerge from complex reward structures and neural representations that are difficult for humans to interpret…
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents
Yael Septon, Tobias Huber, Elisabeth André +1
Explaining the behavior of reinforcement learning agents operating in sequential decision-making settings is challenging, as their behavior is affected by a dynamic environment and…
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
Tobias Huber, Katharina Weitz, Elisabeth André +1
With advances in reinforcement learning (RL), agents are now being developed in high-stakes application domains such as healthcare and transportation. Explaining the behavior of th…
Exploring Computational User Models for Agent Policy Summarization
Isaac Lage, Daphna Lifschitz, Finale Doshi-Velez +1
AI agents are being developed to support high stakes decision-making processes from driving cars to prescribing drugs, making it increasingly important for human users to understan…