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baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

caffe

Caffe: a fast open framework for deep learning.

codecraft

deepgtav

A plugin for GTAV that transforms it into a vision-based self-driving car research environment.

deepmind-atari-deep-q-learner

The original code from the DeepMind article + my tweaks

drqn-tensorflow

Deep recurrent Q Learning using Tensorflow, openai/gym and openai/retro

eazy-for-ss

A Bypassgfw Collection

epciclr2020

fatigue-detection

Detect the drivers fatigue real-time using python and OpenCV

garage

A toolkit for reproducible reinforcement learning research

instructions

Some useful instructions

ma_goal_gan

maddpg

madrl

Repo containing code for multi-agent deep reinforcement learning (MADRL).

mappo

mappo_new

marl_papers

microrts

object-detector

Object Detection Framework using HOG as descriptor and Linear SVM as classifier.

opencv

Open Source Computer Vision Library

pymarl

Beta code release for Python Multi-Agent Reinforcement Learning framework

pysc2-tutorial

Tutorials for building a PySC2 bot

pytorch-maddpg

A pytorch implementation of MADDPG (multi-agent deep deterministic policy gradient)

rllab

rllab is a framework for developing and evaluating reinforcement learning algorithms, fully compatible with OpenAI Gym.

rllab-curriculum

slbo

Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

smac

SMAC: The StarCraft Multi-Agent Challenge

starcraft

Implementations of QMIX, VDN and COMA on SMAC, corresponding papers are "QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning", "Value-Decomposition Networks For Cooperative Multi-Agent Learning", and "Counterfactual Multi-Agent Policy Gradients".

tensorflow-2.x-tutorials

TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码，实战教程。

thu-digital-image-processing

the homework for the Digital Image Processing course of professor Yang Wenming