7 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)…
Explaining Reinforcement Learning Agents Through Counterfactual Action Outcomes
Yotam Amitai, Yael Septon, Ofra Amir
Explainable reinforcement learning (XRL) methods aim to help elucidate agent policies and decision-making processes. The majority of XRL approaches focus on local explanations, see…
ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents
Yotam Amitai, Guy Avni, Ofra Amir
As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial. Most explainable reinforcement learning (XRL)…
"I Don't Think So": Summarizing Policy Disagreements for Agent Comparison
Yotam Amitai, Ofra Amir
With Artificial Intelligence on the rise, human interaction with autonomous agents becomes more frequent. Effective human-agent collaboration requires users to understand the agent…