HongdaZhang's repositories
datasets
A collection of datasets of ML problem solving
deepmind-research
This repository contains implementations and illustrative code to accompany DeepMind publications
Diffusion_RL
This repo has the code and suplementary materials of our 2024 RAL submission.
eat_tensorflow2_in_30_days
Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋
epymarl
An extension of the PyMARL codebase that includes additional algorithms and environment support
formation
ROS package for formation and rendezvous of multi-drone (T-Cyber 2020)
homework
Assignments for CS294-112.
KaTeX
Fast math typesetting for the web.
MADDPG_torch
The code for maddpg using pytorch
MAgent
A Platform for Many-agent Reinforcement Learning
ML-NLP
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
Multi-Agent-Deep-Deterministic-Policy-Gradients
A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm
Multi-Agent-Reinforcement-Learning
PyTorch implementations of MADDPG, MAPPO (coming)
nmea_navsat_driver
ROS package containing drivers for NMEA devices that can output satellite navigation data (e.g. GPS or GLONASS).
off-policy
PyTorch implementations of popular off-policy multi-agent reinforcement learning algorithms, including QMix, VDN, MADDPG, and MATD3.
on-policy
This is the official implementation of Multi-Agent PPO (MAPPO).
open_spiel
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
orbbec_competition
第四届3DV创新应用竞赛
pymarl2
Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)
Python
All Algorithms implemented in Python
ray
A fast and simple framework for building and running distributed applications.
rl-book
Source codes for the book "Reinforcement Learning: Theory and Python Implementation"
ROS-ENKI_robot_simulation
A framework for the development of new closed-loop AI algorithms
smac
SMAC: The StarCraft Multi-Agent Challenge
spinningup
An educational resource to help anyone learn deep reinforcement learning.
StarCraft
Implementations of IQL, QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II
tensorflow_study
tensorflow学习代码
WorldModels
An implementation of the ideas from this paper https://arxiv.org/pdf/1803.10122.pdf