Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement

1 code implementation • 2 Nov 2022 • Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden Petersen, Daniel Faissol, Robert Anderson

Comprehensive experiments show that VDGN policies significantly outperform error threshold-based policies in global error and cost metrics.

Multi-agent Reinforcement Learning reinforcement-learning +1

Do Deep Neural Networks Always Perform Better When Eating More Data?

1 code implementation • 30 May 2022 • Jiachen Yang, Zhuo Zhang, Yicheng Gong, Shukun Ma, Xiaolan Guo, Yue Yang, Shuai Xiao, Jiabao Wen, Yang Li, Xinbo Gao, Wen Lu, Qinggang Meng

Data has now become a shortcoming of deep learning.

A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning

no code implementations • 29 Sep 2021 • Yan Li, Lingxiao Wang, Jiachen Yang, Ethan Wang, Zhaoran Wang, Tuo Zhao, Hongyuan Zha

To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation- invariant actor-critic neural architecture.

Inductive Bias Multi-agent Reinforcement Learning +2

Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach

no code implementations • 18 May 2021 • Yan Li, Lingxiao Wang, Jiachen Yang, Ethan Wang, Zhaoran Wang, Tuo Zhao, Hongyuan Zha

To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation-invariant actor-critic neural architecture.

Inductive Bias Multi-agent Reinforcement Learning

Reinforcement Learning for Adaptive Mesh Refinement

no code implementations • 1 Mar 2021 • Jiachen Yang, Tarik Dzanic, Brenden Petersen, Jun Kudo, Ketan Mittal, Vladimir Tomov, Jean-Sylvain Camier, Tuo Zhao, Hongyuan Zha, Tzanio Kolev, Robert Anderson, Daniel Faissol

Large-scale finite element simulations of complex physical systems governed by partial differential equations (PDE) crucially depend on adaptive mesh refinement (AMR) to allocate computational budget to regions where higher resolution is required.

Inductive Bias reinforcement-learning +1

GraphOpt: Learning Optimization Models of Graph Formation

no code implementations • ICML 2020 • Rakshit Trivedi, Jiachen Yang, Hongyuan Zha

Formation mechanisms are fundamental to the study of complex networks, but learning them from observations is challenging.

Decision Making Link Prediction

Learning to Incentivize Other Learning Agents

2 code implementations • NeurIPS 2020 • Jiachen Yang, Ang Li, Mehrdad Farajtabar, Peter Sunehag, Edward Hughes, Hongyuan Zha

The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years.

General Reinforcement Learning Reinforcement Learning (RL)

Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery

1 code implementation • 7 Dec 2019 • Jiachen Yang, Igor Borovikov, Hongyuan Zha

The interpretability of the learned skills show the promise of the proposed method for achieving human-AI cooperation in team sports games.

Multi-agent Reinforcement Learning Q-Learning +2

Single Episode Policy Transfer in Reinforcement Learning

1 code implementation • ICLR 2020 • Jiachen Yang, Brenden Petersen, Hongyuan Zha, Daniel Faissol

An even greater challenge is performing near-optimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience rollouts for adaptation.

reinforcement-learning Reinforcement Learning (RL) +1

Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization

no code implementations • 23 Sep 2019 • Zhi Zhang, Jiachen Yang, Hongyuan Zha

Traffic congestion in metropolitan areas is a world-wide problem that can be ameliorated by traffic lights that respond dynamically to real-time conditions.

Multi-agent Reinforcement Learning reinforcement-learning +1

CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning

1 code implementation • ICLR 2020 • Jiachen Yang, Alireza Nakhaei, David Isele, Kikuo Fujimura, Hongyuan Zha

To address both challenges, we restructure the problem into a novel two-stage curriculum, in which single-agent goal attainment is learned prior to learning multi-agent cooperation, and we derive a new multi-goal multi-agent policy gradient with a credit function for localized credit assignment.

Autonomous Vehicles Efficient Exploration +3

Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis

no code implementations • 8 Feb 2018 • Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol

To the best of our knowledge, this work is the first to consider adaptive, personalized multi-cytokine mediation therapy for sepsis, and is the first to exploit deep reinforcement learning on a biological simulation.

reinforcement-learning Reinforcement Learning (RL)

Learning Deep Mean Field Games for Modeling Large Population Behavior

no code implementations • ICLR 2018 • Jiachen Yang, Xiaojing Ye, Rakshit Trivedi, Huan Xu, Hongyuan Zha

We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space.

Fake News Mitigation via Point Process Based Intervention

no code implementations • ICML 2017 • Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit Trivedi, Elias Khalil, Shuang Li, Le Song, Hongyuan Zha

We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model.

reinforcement-learning Reinforcement Learning (RL)