An elegant PyTorch deep reinforcement learning library.

Deep Reinforcement Learning

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

Deep Reinforcement Learning Lab, a platform designed to make DRL technology and fun for everyone

PPO x Family DRL Tutorial Course（决策智能入门级公开课：8节课帮你盘清算法理论，理顺代码逻辑，玩转决策AI应用实践 ）

Deep Reinforcement Learning: Zero to Hero!

Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.

Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

D, the Roguelike

Code implementation of "Learning Efficient Online 3D Bin Packing on Packing Configuration Trees". We propose to enhance the practical applicability of online 3D Bin Packing Problem (BPP) via learning on a hierarchical packing configuration tree which makes the deep reinforcement learning (DRL) model easy to deal with practical constraints and well-performing even with continuous solution space.

Code for paper "Computation Offloading Optimization for UAV-assisted Mobile Edge Computing: A Deep Deterministic Policy Gradient Approach"

This is a project about deep reinforcement learning autonomous obstacle avoidance algorithm for UAV.

PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.

This repository contains the implementation of paper Online 3D Bin Packing with Constrained Deep Reinforcement Learning.

Playing Flappy Bird Using Deep Reinforcement Learning (Based on Deep Q Learning DQN using Tensorflow)

DRLib：a Concise Deep Reinforcement Learning Library, Integrating HER, PER and D2SR for Almost Off-Policy RL Algorithms.

深度强化学习路径规划, SAC路径规划, Soft Actor-Critic算法, SAC-pytorch，激光雷达Lidar避障，激光雷达仿真，Adaptive-SAC

This is a new repo used for training UAV navigation (local path planning) policy using DRL methods.

Implementation of: Nazari, Mohammadreza, et al. "Deep Reinforcement Learning for Solving the Vehicle Routing Problem." arXiv preprint arXiv:1802.04240 (2018).

Deep Reinforcement Learning for Robotic Grasping from Octrees

gym-gazebo2 is a toolkit for developing and comparing reinforcement learning algorithms using ROS 2 and Gazebo

AutoPentest-DRL: Automated Penetration Testing Using Deep Reinforcement Learning

Deep reinforcement learning for mobile edge computing

最好使用的规则引擎之一，可以直接使用SQL语句定义规则，简化了编码的负荷，也可以使用XML， drl文件配置规则，还支持drools文件导入。One of the best rule engines, is easy to use SQL statements to define rules, simplify the workload of coding, it also can use XML, DRL file to configure rules, and support import drools file directly.

Drltrace is a library calls tracer for Windows and Linux applications.

This is the official code of the publised paper 'A Multi-action Deep Reinforcement Learning Framework for Flexible Job-shop Scheduling Problem'

Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

Code of Paper "Joint Task Offloading and Resource Optimization in NOMA-based Vehicular Edge Computing: A Game-Theoretic DRL Approach", JSA 2022.

Code for paper "基于多智能体深度强化学习的车联网通信资源分配优化"