arXiv subjects

Wenze Chen

Publications and source records attributed to Wenze Chen.

4 recordsLinked to original sources

arXiv2026

Susceptible-Infected Epidemics on Evolving Graphs at Critical Infection Rate

Consider an SI process on a graph $G$ where each S--I connection becomes I--I at rate $\lambda$. Here S and I stand for ``susceptible'' and ``infected'' respectively. The evoSI model is a modification of the SI model in which S--I edges are broken at rate $\rho$ and the ``S'' connects to a randomly chosen vertex. It is proven in Durrett and Yao [2022, Electron. J. Probab.] that, for the supercritical evoSI process on the configuration model, there exists a quantity $\Delta$ depending on the first three moments of the degree distribution such that the sign of $\Delta$ governs the continuity of the phase transition of the final epidemic size near the critical infection rate $\lambda_c$. In this paper, we consider the critical evoSI model on the configuration model, i.e., $\lambda=\lambda_c$. We show that, if $\Delta>0$, then the probability of a major outbreak starting from a single infected individual is $Cn^{-1/3}(1+o(1))$ for some explicit constant $C>0$, where $n$ is the size of the graph. On the contrary, if $\Delta<0$, then this probability is $o(n^{-1/3})$. The case $\Delta<0$ is reminiscent of the critical {\ER} graphs, where the probability for the size of the largest component to be of order $n$ decays exponentially in $n$.

math.PR↗

arXiv2025

SIR Epidemics on Evolving Erdős-Rényi Graphs

In the standard SIR model, infected vertices infect their neighbors at rate $λ$ independently across each edge. They also recover at rate $γ$. In this work we consider the SIR-$ω$ model where the graph structure itself co-evolves with the SIR dynamics. Specifically, $S-I$ connections are broken at rate $ω$. Then, with probability $α$, $S$ rewires this edge to another uniformly chosen vertex; and with probability $1-α$, this edge is simply dropped. When $α=1$ the SIR-$ω$ model becomes the evoSIR model. Jiang et al. proved in \cite{DOMath} that the probability of an outbreak in the evoSIR model converges to 0 as $λ$ approaches the critical infection rate $λ_c$. On the other hand, numerical experiments in \cite{DOMath} revealed that, as $λ\to λ_c$, (conditionally on an outbreak) the fraction of infected vertices may not converge to 0, which is referred to as a discontinuous phase transition. In \cite{BB} Ball and Britton give two (non-matching) conditions for continuous and discontinuous phase transitions for the fraction of infected vertices in the SIR-$ω$ model. In this work, we obtain a necessary and sufficient condition for the emergence of a discontinuous phase transition of the final epidemic size of the SIR-$ω$ model on \ER\, graphs, thus closing the gap between these two conditions.

math.PR↗

arXiv2023

TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play

Multi-agent football poses an unsolved challenge in AI research. Existing work has focused on tackling simplified scenarios of the game, or else leveraging expert demonstrations. In this paper, we develop a multi-agent system to play the full 11 vs. 11 game mode, without demonstrations. This game mode contains aspects that present major challenges to modern reinforcement learning algorithms; multi-agent coordination, long-term planning, and non-transitivity. To address these challenges, we present TiZero; a self-evolving, multi-agent system that learns from scratch. TiZero introduces several innovations, including adaptive curriculum learning, a novel self-play strategy, and an objective that optimizes the policies of multiple agents jointly. Experimentally, it outperforms previous systems by a large margin on the Google Research Football environment, increasing win rates by over 30%. To demonstrate the generality of TiZero's innovations, they are assessed on several environments beyond football; Overcooked, Multi-agent Particle-Environment, Tic-Tac-Toe and Connect-Four.

cs.AI↗

arXiv2021

TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations

Deep reinforcement learning (DRL) has achieved super-human performance on complex video games (e.g., StarCraft II and Dota II). However, current DRL systems still suffer from challenges of multi-agent coordination, sparse rewards, stochastic environments, etc. In seeking to address these challenges, we employ a football video game, e.g., Google Research Football (GRF), as our testbed and develop an end-to-end learning-based AI system (denoted as TiKick) to complete this challenging task. In this work, we first generated a large replay dataset from the self-playing of single-agent experts, which are obtained from league training. We then developed a distributed learning system and new offline algorithms to learn a powerful multi-agent AI from the fixed single-agent dataset. To the best of our knowledge, Tikick is the first learning-based AI system that can take over the multi-agent Google Research Football full game, while previous work could either control a single agent or experiment on toy academic scenarios. Extensive experiments further show that our pre-trained model can accelerate the training process of the modern multi-agent algorithm and our method achieves state-of-the-art performances on various academic scenarios.

cs.AI↗