activity
20192022
most citedCityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

243 citations · 247 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG20222 cited

Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization

Zihan Zhou, Wei Fu, Bingliang Zhang +1

We present Reward-Switching Policy Optimization (RSPO), a paradigm to discover diverse strategies in complex RL environments by iteratively finding novel policies that are both loc…

cs.MA2021

Temporal Induced Self-Play for Stochastic Bayesian Games

Weizhe Chen, Zihan Zhou, Yi Wu +1

One practical requirement in solving dynamic games is to ensure that the players play well from any decision point onward. To satisfy this requirement, existing efforts focus on eq…

cs.LG2020

Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning

Qian Long, Zihan Zhou, Abhibav Gupta +3

In multi-agent games, the complexity of the environment can grow exponentially as the number of agents increases, so it is particularly challenging to learn good policies when the…

cs.MA2019243 cited

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

Huichu Zhang, Siyuan Feng, Chang Liu +7

Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic…

cs.CL20192 cited

Image Based Review Text Generation with Emotional Guidance

Xuehui Sun, Zihan Zhou, Yuda Fan

In the current field of computer vision, automatically generating texts from given images has been a fully worked technique. Up till now, most works of this area focus on image con…