drl
There are 132 repositories under drl topic.
thu-ml/tianshou
An elegant PyTorch deep reinforcement learning library.
opendilab/DI-engine
OpenDILab Decision AI Engine
erlerobot/gym-gazebo
Refer to https://github.com/AcutronicRobotics/gym-gazebo2 for the new version
ChenglongChen/pytorch-DRL
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
AcutronicRobotics/gym-gazebo2
gym-gazebo2 is a toolkit for developing and comparing reinforcement learning algorithms using ROS 2 and Gazebo
minqi/learning-to-communicate-pytorch
Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch
MLJejuCamp2017/DRL_based_SelfDrivingCarControl
Deep Reinforcement Learning (DQN) based Self Driving Car Control with Vehicle Simulator
cszhangzhen/DRL4Recsys
Courses on Deep Reinforcement Learning (DRL) and DRL papers for recommender systems
Kyushik/DRL
Repository for codes of 'Deep Reinforcement Learning'
jgvictores/awesome-deep-reinforcement-learning
Curated list for Deep Reinforcement Learning (DRL): software frameworks, models, datasets, gyms, baselines...
amiralansary/rl-medical
Deep Reinforcement Learning (DRL) agents applied to medical images
kevin031060/RL_TSP_4static
Deep Reinforcement Learning for Multiobjective Optimization. Code for this paper
ChenDRAG/mujoco-benchmark
Provide full reinforcement learning benchmark on mujoco environments, including ddpg, sac, td3, pg, a2c, ppo, library
jingweiz/pytorch-distributed
Ape-X DQN & DDPG with pytorch & tensorboard
mymusise/Trading-Gym
A Trading environment base on Gym
ZYunfeii/DRL_algorithm_library
This is a reinforcement learning algorithm library. The code takes into account both performance and simplicity, with little dependence.
cfl-minds/drl_shape_optimization
Deep reinforcement learning to perform shape optimization
IamWangYunKai/RL-Gallery
A gallery for reinforcement learning, including frameworks, tutorials, papers, implementations, applications, etc.
DonsetPG/fenics-DRL
Repository from the paper https://arxiv.org/abs/1908.04127, to train Deep Reinforcement Learning in Fluid Mechanics Setup.
inoryy/Deep-RL-Bootcamp-Labs
Solutions to the Deep RL Bootcamp labs
NoteDance/Note
Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, Gemma, CLIP, ViT, ConvNeXt, BEiT, Swin Transformer, Segformer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.
qlt315/UCMEC_COMMAG
Simulation code and mathematic details of our paper in IEEE Communications Magazine: ''When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization''
ugurkanates/NeurIRS2019DroneChallengeRL
Long-Term Planning with Deep Reinforcement Learning on Autonomous Drones
symoon94/DRQN-keras
Atari-DRQN (keras ver.)
cgel/DRL
A collection of Deep Reinforcement Learning algorithms implemented in tensorflow. Very extensible. High performing DQN implementation.
DRL-Navigation/img_env
A customized grid map-based navigation simulation platform following the Gym API.
kdally/fault-tolerant-flight-control-drl
Deep Reinforcement Learning for Flight Control
alga-hopf/drl-graph-partitioning
DRL models for graph partitioning and sparse matrix ordering.
QasimWani/RL-Unity
Implementation of Deep Reinforcement Learning algorithms in the Unity game engine.
xuemei-ye/maddpg-mpe
Transplant a implementation of MADDPG to the environment provided by openAI (multiagent-particle-envs).
StevenJokess/d2rl
Not interactive deep reinforcement learning book with no-framework code, copied math, no discussions. Adopted at only -1 university(Shanhe University, SHU). BTW, I like this virtual university, which english abbreviation happens to be the pinyin of one part of my Chinese name(Cai "Shu"qi).
Arena-Rosnav/rosnav-rl
Rosnav planner, based on DRL and designed to work with ROS and the whole arena-rosnav infrastructure.
MuGeminorum/Snake-AI
Using deep reinforcement learning to play Snake game. The used algorithm is PPO for discrete! It has the brilliant performance in the field of discrete action space just like in continuous action space. You just need half an hour to train the snake and then it can be as smart as you.|使用深度强化学习玩蛇游戏。 使用的算法是离散的 PPO！ 它在离散动作空间领域有着与连续动作空间一样的出色表现。