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20222024
most citedCommunication-Efficient Actor-Critic Methods for Homogeneous Markov Games

2 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2024

Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control

Jinzhu Luo, Dingyang Chen, Qi Zhang

Data augmentation creates new data points by transforming the original ones for a reinforcement learning (RL) agent to learn from, which has been shown to be effective for the obje…

cs.LG2024

Efficient Sequential Decision Making with Large Language Models

Dingyang Chen, Qi Zhang, Yinglun Zhu

This paper focuses on extending the success of large language models (LLMs) to sequential decision making. Existing efforts either (i) re-train or finetune LLMs for decision making…

cs.MA2023

-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning

Dingyang Chen, Qi Zhang

Identification and analysis of symmetrical patterns in the natural world have led to significant discoveries across various scientific fields, such as the formulation of gravitatio…

cs.MA2023★ 1 cited

Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning

Dingyang Chen, Qi Zhang

Executing actions in a correlated manner is a common strategy for human coordination that often leads to better cooperation, which is also potentially beneficial for cooperative mu…

cs.MA2022★ 2 cited

Convergence and Price of Anarchy Guarantees of the Softmax Policy Gradient in Markov Potential Games

Dingyang Chen, Qi Zhang, Thinh T. Doan

We study the performance of policy gradient methods for the subclass of Markov games known as Markov potential games (MPGs), which extends the notion of normal-form potential games…

cs.MA2022★ 2 cited

Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games

Dingyang Chen, Yile Li, Qi Zhang

Recent success in cooperative multi-agent reinforcement learning (MARL) relies on centralized training and policy sharing. Centralized training eliminates the issue of non-stationa…