MADT: Offline Pre-trained Multi-Agent Decision Transformer

Overview

MADT: Offline Pre-trained Multi-Agent Decision Transformer

A link to our paper can be found on Arxiv.

Overview

Official codebase for Offline Pre-trained Multi-Agent Decision Transformer. Contains scripts to reproduce experiments.

image info

Instructions

It may be necessary to add the respective directories to your PYTHONPATH.

The offline smac dataset for this repo is available at here.

## password is vp1ulk

How to run experiments

  1. setup python environment with pip install -r requirements.txt
  2. to install StarCraft II & SMAC, you could run bash install_sc2.sh. Or you could install them manually to other path you like, following the official link: https://github.com/oxwhirl/smac.
  3. enter the ./sc2/ folder.
  4. set hyper-parameters in run_madt_sc2.py line 19-49 according to appendix.
  5. select a maps to test in envs/config.py line 142
  6. run the run_madt_sc2.py script
Owner
Linghui Meng
Phd Candidate
Linghui Meng
DeepFaceEditing: Deep Face Generation and Editing with Disentangled Geometry and Appearance Control

DeepFaceEditing: Deep Face Generation and Editing with Disentangled Geometry and Appearance Control One version of our system is implemented using the

260 Nov 28, 2022
⚓ Eurybia monitor model drift over time and securize model deployment with data validation

View Demo · Documentation · Medium article 🔍 Overview Eurybia is a Python library which aims to help in : Detecting data drift and model drift Valida

MAIF 172 Dec 27, 2022
Code and models for ICCV2021 paper "Robust Object Detection via Instance-Level Temporal Cycle Confusion".

Robust Object Detection via Instance-Level Temporal Cycle Confusion This repo contains the implementation of the ICCV 2021 paper, Robust Object Detect

Xin Wang 69 Oct 13, 2022
Face-Recognition-based-Attendance-System - An implementation of Attendance System in python.

Face-Recognition-based-Attendance-System A real time implementation of Attendance System in python. Pre-requisites To understand the implentation of F

Muhammad Zain Ul Haque 1 Dec 31, 2021
Lab course materials for IEMBA 8/9 course "Coding and Artificial Intelligence"

IEMBA 8/9 - Coding and Artificial Intelligence Dear IEMBA 8/9 students, welcome to our IEMBA 8/9 elective course Coding and Artificial Intelligence, t

Artificial Intelligence & Machine Learning (AI:ML Lab) @ HSG 1 Jan 11, 2022
Paddle implementation for "Highly Efficient Knowledge Graph Embedding Learning with Closed-Form Orthogonal Procrustes Analysis" (NAACL 2021)

ProcrustEs-KGE Paddle implementation for Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis 🙈 A more detailed re

Lincedo Lab 4 Jun 09, 2021
On-device wake word detection powered by deep learning.

Porcupine Made in Vancouver, Canada by Picovoice Porcupine is a highly-accurate and lightweight wake word engine. It enables building always-listening

Picovoice 2.8k Dec 29, 2022
Convolutional Neural Network to detect deforestation in the Amazon Rainforest

Convolutional Neural Network to detect deforestation in the Amazon Rainforest This project is part of my final work as an Aerospace Engineering studen

5 Feb 17, 2022
Code for paper [ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot] (ICCV 2021, oral))

ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot This repository is the official PyTorch implementation of ICCV-21 pape

Jiarui 21 May 09, 2022
This code is an unofficial implementation of HiFiSinger.

HiFiSinger This code is an unofficial implementation of HiFiSinger. The algorithm is based on the following papers: Chen, J., Tan, X., Luan, J., Qin,

Heejo You 87 Dec 23, 2022
RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids

RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids Real-time detection performance. This repo contains the code an

0 Nov 10, 2021
[CVPR2021] UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles

UAV-Human Official repository for CVPR2021: UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicle Paper arXiv Res

129 Jan 04, 2023
YOLOv4-v3 Training Automation API for Linux

This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset or label your dataset using our

BMW TechOffice MUNICH 626 Dec 31, 2022
Model Zoo for AI Model Efficiency Toolkit

We provide a collection of popular neural network models and compare their floating point and quantized performance.

Qualcomm Innovation Center 137 Jan 03, 2023
Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond

CRF - Conditional Random Fields A library for dense conditional random fields (CRFs). This is the official accompanying code for the paper Regularized

Đ.Khuê Lê-Huu 21 Nov 26, 2022
Rendering color and depth images for ShapeNet models.

Color & Depth Renderer for ShapeNet This library includes the tools for rendering multi-view color and depth images of ShapeNet models. Physically bas

Yinyu Nie 41 Dec 19, 2022
Code for "Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification", ECCV 2020 Spotlight

Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification Implementation of "Learning From Multiple Experts: Se

27 Nov 05, 2022
Denoising Diffusion Probabilistic Models

Denoising Diffusion Probabilistic Models This repo contains code for DDPM training. Based on Denoising Diffusion Probabilistic Models, Improved Denois

Alexander Markov 7 Dec 15, 2022
Pytorch implementation of the paper "COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking."

Expert-Linking Pytorch implementation of the paper "COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking." This is

BoChen 12 Jan 01, 2023
Predict the latency time of the deep learning models

Deep Neural Network Prediction Step 1. Genernate random parameters and Run them sequentially : $ python3 collect_data.py -gp -ep -pp -pl pooling -num

QAQ 1 Nov 12, 2021