Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance

no code implementations • 6 Jul 2023 • Yuchen Fang, Zhenggang Tang, Kan Ren, Weiqing Liu, Li Zhao, Jiang Bian, Dongsheng Li, Weinan Zhang, Yong Yu, Tie-Yan Liu

Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets.

Reinforcement Learning (RL)

RGB-Only Reconstruction of Tabletop Scenes for Collision-Free Manipulator Control

no code implementations • 21 Oct 2022 • Zhenggang Tang, Balakumar Sundaralingam, Jonathan Tremblay, Bowen Wen, Ye Yuan, Stephen Tyree, Charles Loop, Alexander Schwing, Stan Birchfield

We present a system for collision-free control of a robot manipulator that uses only RGB views of the world.

Model Predictive Control

A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning

no code implementations • 14 Aug 2021 • Zhenggang Tang, Kai Yan, Liting Sun, Wei Zhan, Changliu Liu

To efficiently simulate with massive amounts of agents in MPS, we propose Scalable Million-Agent DQN (SMADQN).

Reinforcement Learning (RL)

Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization

2 code implementations • ICLR 2021 • Zhenggang Tang, Chao Yu, Boyuan Chen, Huazhe Xu, Xiaolong Wang, Fei Fang, Simon Du, Yu Wang, Yi Wu

We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games.