12 citations · 21 across the 3 of their papers we have counts for
9 papers
FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning
Bo Zhao, Peng Sun, Liming Fang +2
Federated learning (FL) is a promising privacy-preserving distributed machine learning methodology that allows multiple clients (i.e., workers) to collaboratively train statistical…
TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning
Peng Sun, Jiechao Xiong, Lei Han +5
Competitive Self-Play (CSP) based Multi-Agent Reinforcement Learning (MARL) has shown phenomenal breakthroughs recently. Strong AIs are achieved for several benchmarks, including D…
A novel control mode of bionic morphing tail based on deep reinforcement learning
Liming Zheng, Zhou Zhou, Pengbo Sun +2
In the field of fixed wing aircraft, many morphing technologies have been applied to the wing, such as adaptive airfoil, variable span aircraft, variable swept angle aircraft, etc.…
Arena: a toolkit for Multi-Agent Reinforcement Learning
Qing Wang, Jiechao Xiong, Lei Han +5
We introduce Arena, a toolkit for multi-agent reinforcement learning (MARL) research. In MARL, it usually requires customizing observations, rewards and actions for each agent, cha…
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space
Jiechao Xiong, Qing Wang, Zhuoran Yang +7
Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely. Motivated by applications in computer games, we…
TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game
Peng Sun, Xinghai Sun, Lei Han +8
Starcraft II (SC2) is widely considered as the most challenging Real Time Strategy (RTS) game. The underlying challenges include a large observation space, a huge (continuous and i…