XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

Concise pytorch implements of MARL algorithms, including MAPPO, MADDPG, MATD3, QMIX and VDN.

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

A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm

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"

implementation of MADDPG using PettingZoo and PyTorch

MARLToolkit: The Multi-Agent Rainforcement Learning Toolkit. Include implementation of MAPPO, MADDPG, QMIX, VDN, COMA, IPPO, QTRAN, MAT...

Multi-agent project (commnet, bicnet, maddpg) in pytorch for Multi-Agent Particle Environment

This is a reinforcement learning algorithm library. The code takes into account both performance and simplicity, with little dependence.

Distributed Multi-Agent Cooperation Algorithm based on MADDPG with prioritized batch data.

Code for the RL method MATD3 described in the paper "Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics"

Implementation of Multi-Agent Reinforcement Learning algorithm(s). Currently includes: MADDPG

Multi-UAV Assisted Wireless Powered Mobile Edge Computing: A hybrid optimization approach of combining multi-agent RL techniques (including attention mechanisms) with collaborative and adaptive caching policies for supporting mobile edge computing through UAVs.

Project on multi agent reinforcement learning applied on patrolling agents

implementation of MADDPG using PyTorch and multiagent-particle-envs

Implementation Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm in keras

This is Multi agent deep reinforcement learning repo which trains an agent to play Tennis. It trains by playing against itself.

RLToolkit is a flexible and high-efficient reinforcement learning framework. Include implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

Multi-Agent RL for UAV Control for Fair and Energy-Efficient Coverage Maximisation

Developed a Multi-Agent DDPG to solve Vehicle Scheduling problem.