Pytorch implementations of the paper Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients

Related tags

Deep LearningLSF-SAC
Overview

LSF-SAC

Pytorch implementations of the paper Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients and several other multi-agent reinforcement learning algorithms, including IQL, QMIX, VDN, COMA, QTRAN(both QTRAN-base and QTRAN-alt), MAVEN, CommNet, DyMA-CL, and G2ANet, which are the state of the art MARL algorithms. The paper implementation and other algorithms' implementation is based on starry-sky6688's qmix impplementation.

Requirements

Acknowledgement

Quick Start

$ python main.py --map=3m

Directly run the main.py, then the algorithm will start training on map 3m. Note CommNet and G2ANet need an external training algorithm, so the name of them are like reinforce+commnet or central_v+g2anet, all the algorithms we provide are written in ./common/arguments.py.

If you just want to use this project for demonstration, you should set --evaluate=True --load_model=True.

The running of DyMA-CL is independent from others because it requires different environment settings, so we put it on another project. For more details, please read DyMA-CL documentation.

Result

We independently train these algorithms for 8 times and take the mean of the 8 independent results, and we evaluate them for 20 episodes every 100 training steps. All of the results are saved in ./result. Results on other maps are still in training, we will update them later.

1. Mean Win Rate of 8 Independent Runs with --difficulty=7(VeryHard)

Replay

Check the website for several replay examples here

If you want to see the replay from your own run, make sure the replay_dir is an absolute path, which can be set in ./common/arguments.py. Then the replays of each evaluation will be saved, you can find them in your path.

Citation

If you find this helpful to your research, please consider citing this paper as

@article{zhou2022value,
  title={Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients},
  author={Zhou, Hanhan and Lan, Tian and Aggarwal, Vaneet},
  journal={arXiv preprint arXiv:2201.01247},
  year={2022}
}
Owner
Hanhan
[2019.Fall- ] Ph.D Candidate, GWU ECE
Hanhan
PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning

Learning to Reweight Examples for Robust Deep Learning Unofficial PyTorch implementation of Learning to Reweight Examples for Robust Deep Learning. Th

Daniel Stanley Tan 325 Dec 28, 2022
RE3: State Entropy Maximization with Random Encoders for Efficient Exploration

State Entropy Maximization with Random Encoders for Efficient Exploration (RE3) (ICML 2021) Code for State Entropy Maximization with Random Encoders f

Younggyo Seo 47 Nov 29, 2022
E-RAFT: Dense Optical Flow from Event Cameras

E-RAFT: Dense Optical Flow from Event Cameras This is the code for the paper E-RAFT: Dense Optical Flow from Event Cameras by Mathias Gehrig, Mario Mi

Robotics and Perception Group 71 Dec 12, 2022
[CVPR 2022] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

Using Unreliable Pseudo Labels Official PyTorch implementation of Semi-Supervised Semantic Segmentation Using Unreliable Pseudo Labels, CVPR 2022. Ple

Haochen Wang 268 Dec 24, 2022
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search

B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search This is the offical implementation of the

SNU ADSL 0 Feb 07, 2022
A Broader Picture of Random-walk Based Graph Embedding

Random-walk Embedding Framework This repository is a reference implementation of the random-walk embedding framework as described in the paper: A Broa

Zexi Huang 23 Dec 13, 2022
Official Code for "Non-deep Networks"

Non-deep Networks arXiv:2110.07641 Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun Overview: Depth is the hallmark of DNNs. But more depth m

Ankit Goyal 567 Dec 12, 2022
Very deep VAEs in JAX/Flax

Very Deep VAEs in JAX/Flax Implementation of the experiments in the paper Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on I

Jamie Townsend 42 Dec 12, 2022
💡 Type hints for Numpy

Type hints with dynamic checks for Numpy! (❒) Installation pip install nptyping (❒) Usage (❒) NDArray nptyping.NDArray lets you define the shape and

Ramon Hagenaars 377 Dec 28, 2022
Adversarial Self-Defense for Cycle-Consistent GANs

Adversarial Self-Defense for Cycle-Consistent GANs This is the official implementation of the CycleGAN robust to self-adversarial attacks used in pape

Dina Bashkirova 10 Oct 10, 2022
Simulation of the solar system using various nummerical methods

solar-system Simulation of the solar system using various nummerical methods Download the repo Make shure matplotlib, scipy etc. are installed execute

Caspar 7 Jul 15, 2022
Official repository for GCR rerank, a GCN-based reranking method for both image and video re-ID

Official repository for GCR rerank, a GCN-based reranking method for both image and video re-ID

53 Nov 22, 2022
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks

StackGAN Pytorch implementation Inception score evaluation StackGAN-v2-pytorch Tensorflow implementation for reproducing main results in the paper Sta

Han Zhang 1.8k Dec 21, 2022
Simple image captioning model - CLIP prefix captioning.

CLIP prefix captioning. Inference Notebook: 🥳 New: 🥳 Our technical papar is finally out! Official implementation for the paper "ClipCap: CLIP Prefix

688 Jan 04, 2023
Face Depixelizer based on "PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models" repository.

NOTE We have noticed a lot of concern that PULSE will be used to identify individuals whose faces have been blurred out. We want to emphasize that thi

Denis Malimonov 2k Dec 29, 2022
Unsupervised Representation Learning via Neural Activation Coding

Neural Activation Coding This repository contains the code for the paper "Unsupervised Representation Learning via Neural Activation Coding" published

yookoon park 5 May 26, 2022
Learning Time-Critical Responses for Interactive Character Control

Learning Time-Critical Responses for Interactive Character Control Abstract This code implements the paper Learning Time-Critical Responses for Intera

Movement Research Lab 227 Dec 31, 2022
Instance-wise Occlusion and Depth Orders in Natural Scenes (CVPR 2022)

Instance-wise Occlusion and Depth Orders in Natural Scenes Official source code. Appears at CVPR 2022 This repository provides a new dataset, named In

27 Dec 27, 2022
The ICS Chat System project for NYU Shanghai Fall 2021

ICS_Chat_System [Catenger] This is the ICS Chat System project for NYU Shanghai Fall 2021 Creators: Shavarsh Melikyan, Skyler Chen and Arghya Sarkar,

1 Dec 20, 2021