7 citations · 12 across the 5 of their papers we have counts for
6 papers
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
Matthias Gerstgrasser, Tom Danino, Sarah Keren
We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe…
Value of Assistance for Mobile Agents
Adi Amuzig, David Dovrat, Sarah Keren
Mobile robotic agents often suffer from localization uncertainty which grows with time and with the agents' movement. This can hinder their ability to accomplish their task. In som…
Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
Guy Azran, Mohamad H. Danesh, Stefano V. Albrecht +1
Recent studies show that deep reinforcement learning (DRL) agents tend to overfit to the task on which they were trained and fail to adapt to minor environment changes. To expedite…
Explainable Reinforcement Learning via Model Transforms
Mira Finkelstein, Lucy Liu, Nitsan Levy Schlot +4
Understanding emerging behaviors of reinforcement learning (RL) agents may be difficult since such agents are often trained in complex environments using highly complex decision ma…
Collaboration Promotes Group Resilience in Multi-Agent RL
Ilai Shraga, Guy Azran, Matthias Gerstgrasser +3
To effectively operate in various dynamic scenarios, RL agents must be resilient to unexpected changes in their environment. Previous work on this form of resilience has focused on…
Designing Environments Conducive to Interpretable Robot Behavior
Anagha Kulkarni, Sarath Sreedharan, Sarah Keren +3
Designing robots capable of generating interpretable behavior is a prerequisite for achieving effective human-robot collaboration. This means that the robots need to be capable of…