3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 1 cited
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning
Majd Ibrahim, Ammar Fayad
Adequate strategizing of agents behaviors is essential to solving cooperative MARL problems. One intuitively beneficial yet uncommon method in this domain is predicting agents futu…
cs.LG2021
Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents
Ammar Fayad, Majd Ibrahim
Discovering successful coordinated behaviors is a central challenge in Multi-Agent Reinforcement Learning (MARL) since it requires exploring a joint action space that grows exponen…
cs.LG2021★ 3 cited
Behavior-Guided Actor-Critic: Improving Exploration via Learning Policy Behavior Representation for Deep Reinforcement Learning
Ammar Fayad, Majd Ibrahim
In this work, we propose Behavior-Guided Actor-Critic (BAC), an off-policy actor-critic deep RL algorithm. BAC mathematically formulates the behavior of the policy through autoenco…