4 papers
Evaluating RL Explainability Methods by How Much They Help Fix Bugs in Agents
Ram Rachum, Yotam Amitai, Bálint Gyevnár +2
This preliminary paper outlines a planned evaluation benchmark for Explainable Reinforcement Learning (XRL) methods. Current evaluations rely on functionally-grounded metrics like…
BXRL: Behavior-Explainable Reinforcement Learning
Ram Rachum, Yotam Amitai, Yonatan Nakar +2
A major challenge of Reinforcement Learning is that agents often learn undesired behaviors that seem to defy the reward structure they were given. Explainable Reinforcement Learnin…
Gap the (Theory of) Mind: Sharing Beliefs About Teammates' Goals Boosts Collaboration Perception, Not Performance
Yotam Amitai, Reuth Mirsky, Ofra Amir
In human-agent teams, openly sharing goals is often assumed to enhance planning, collaboration, and effectiveness. However, direct communication of these goals is not always feasib…
Interactive Explanations for Reinforcement-Learning Agents
Yotam Amitai, Ofra Amir, Guy Avni
As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial. Most explainable reinforcement learning (XRL)…