activity
20182023
most citedRMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents

16 citations · 60 across the 13 of their papers we have counts for

collaborators
Showing cs.AIShow all

6 papers · 1 filter

cs.AI20213 cited

Solving Large-Scale Extensive-Form Network Security Games via Neural Fictitious Self-Play

Wanqi Xue, Youzhi Zhang, Shuxin Li +3

Securing networked infrastructures is important in the real world. The problem of deploying security resources to protect against an attacker in networked domains can be modeled as…

cs.AI20212 cited

CFR-MIX: Solving Imperfect Information Extensive-Form Games with Combinatorial Action Space

Shuxin Li, Youzhi Zhang, Xinrun Wang +2

In many real-world scenarios, a team of agents coordinate with each other to compete against an opponent. The challenge of solving this type of game is that the team's joint action…

cs.AI20204 cited

Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Execution

Rundong Wang, Hongxin Wei, Bo An +2

Portfolio management via reinforcement learning is at the forefront of fintech research, which explores how to optimally reallocate a fund into different financial assets over the…

cs.AI20208 cited

Learning Behaviors with Uncertain Human Feedback

Xu He, Haipeng Chen, Bo An

Human feedback is widely used to train agents in many domains. However, previous works rarely consider the uncertainty when humans provide feedback, especially in cases that the op…

cs.AI20191 cited

Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep Reinforcement Learning

Runsheng Yu, Zhenyu Shi, Xinrun Wang +5

Existing value-factorized based Multi-Agent deep Reinforce-ment Learning (MARL) approaches are well-performing invarious multi-agent cooperative environment under thecen-tralized t…

cs.AI2019

Learning Efficient Multi-agent Communication: An Information Bottleneck Approach

Rundong Wang, Xu He, Runsheng Yu +3

We consider the problem of the limited-bandwidth communication for multi-agent reinforcement learning, where agents cooperate with the assistance of a communication protocol and a…