activity
20172022
most citedAsynchronous Actor-Critic for Multi-Agent Reinforcement Learning

5 citations · 5 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG20225 cited

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…

cs.AI2022

Macro-Action-Based Multi-Agent/Robot Deep Reinforcement Learning under Partial Observability

Yuchen Xiao

The state-of-the-art multi-agent reinforcement learning (MARL) methods have provided promising solutions to a variety of complex problems. Yet, these methods all assume that agents…

math.FA2020

Adaptive directional Haar tight framelets on bounded domains for digraph signal representations

Yuchen Xiao, Xiaosheng Zhuang

Based on hierarchical partitions, we provide the construction of Haar-type tight framelets on any compact set . In particular, on the unit block ,…

cs.RO2019

Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net

Yuchen Xiao, Joshua Hoffman, Tian Xia +1

In many real-world multi-robot tasks, high-quality solutions often require a team of robots to perform asynchronous actions under decentralized control. Decentralized multi-agent r…

cs.AI2017

Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems

Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar +2

A key challenge in multi-robot and multi-agent systems is generating solutions that are robust to other self-interested or even adversarial parties who actively try to prevent the…