36 citations · 55 across the 14 of their papers we have counts for
6 papers · 1 filter
Sequential Fair Resource Allocation under a Markov Decision Process Framework
Parisa Hassanzadeh, Eleonora Kreacic, Sihan Zeng +2
We study the sequential decision-making problem of allocating a limited resource to agents that reveal their stochastic demands on arrival over a finite horizon. Our goal is to des…
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Yuchen Xiao, Weihao Tan, Christopher Amato
Synchronizing decisions across multiple agents in realistic settings is problematic since it requires agents to wait for other agents to terminate and communicate about termination…
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning
Xueguang Lyu, Andrea Baisero, Yuchen Xiao +1
Centralized Training for Decentralized Execution, where training is done in a centralized offline fashion, has become a popular solution paradigm in Multi-Agent Reinforcement Learn…
Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning
Yuchen Xiao, Xueguang Lyu, Christopher Amato
Policy gradient methods have become popular in multi-agent reinforcement learning, but they suffer from high variance due to the presence of environmental stochasticity and explori…
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning
Xueguang Lyu, Yuchen Xiao, Brett Daley +1
Centralized Training for Decentralized Execution, where agents are trained offline using centralized information but execute in a decentralized manner online, has gained popularity…
Macro-Action-Based Deep Multi-Agent Reinforcement Learning
Yuchen Xiao, Joshua Hoffman, Christopher Amato
In real-world multi-robot systems, performing high-quality, collaborative behaviors requires robots to asynchronously reason about high-level action selection at varying time durat…