timefly-1989 Goto Github PK

pytorch-maddpg

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

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

rl-multi-agent-car-parking

Multi-Agent Car Parking using Reinforcement Learning

sched_net

simulator

Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning

smac_plus

An open source benchmark for Multi Agent Reinforcement Learning

smart-traffic-signals-in-india-using-deep-reinforcement-learning-and-advanced-computer-vision

We have used Deep Reinforcement Learning and Advanced Computer Vision techniques to for the creation of Smart Traffic Signals for Indian Roads. We have created the scripts for using SUMO as our environment for deploying all our RL models.

smarts

Scalable Multi-Agent RL Training School for Autonomous Driving

smarts-lite

Scalable Multi-Agent RL Training School for Autonomous Driving -> SMARTS

spatial-intention-maps

Learning multi-agent robotic mobile manipulation with deep reinforcement learning

starcraft

Implementations of QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II

sumo

Eclipse SUMO is an open source, highly portable, microscopic and continuous traffic simulation package designed to handle large networks. It allows for intermodal simulation including pedestrians and comes with a large set of tools for scenario creation.

super-mario-bros-ppo-pytorch

Proximal Policy Optimization (PPO) algorithm for Super Mario Bros

super-mario-bros-rl

This project explores deep reinforcement learning, hybrid actor-critic approach with A3C/PPO combined with curiosity for playing Super Mario Bros

traffic-simulation-and-visualization

Traffic Intersection Simulation Using C++ and Qt

traffic-simulation-de

Source code for javascript simulation of website

vbc

pytorch implementation of "Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control"

vectorizedmultiagentsimulator

VMAS is a vectorized framework designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.

warp-drive

Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU

webgs

LAR-19641-1: WebGS: Web-based Platform for Multi-UAV Flight Visualization and Simulation