Python Drl Resources

Voronoi Multi_Robot Collaborate Exploration Introduction In the unknown environment, the cooperative exploration of multiple robots is completed by Vo

ES_OTN_Public Carlos Güemes Palau, Paul Almasan, Pere Barlet Ros, Albert Cabellos Aparicio Contact us: [email protected], contactus@bn

Tianshou (天授) is a reinforcement learning platform based on pure PyTorch. Unlike existing reinforcement learning libraries, which are mainly based on

ElegantRL “小雅”: Scalable and Elastic Deep Reinforcement Learning ElegantRL is developed for researchers and practitioners with the following advantage

FinRL-Meta: A Universe of Market Environments. FinRL-Meta is a universe of market environments for data-driven financial reinforcement learning. Users

5dof-robot-writing this project aim to use PPO control a 5 dof manipulator to draw lines in 3d space. Introduction to the files the pybullet environme

Learning to Communicate with Deep Multi-Agent Reinforcement Learning This is a PyTorch implementation of the original Lua code release. Overview This

Lightweight, efficient and stable implementations of deep reinforcement learning algorithms using PyTorch. 🔥

This repository is the implementation of the paper "Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning", linked here. The project makes use of the Deep Reinforcement Library stable-baselines3 to derive a control policy that maximizes melt pool depth consistency.

AutoPentest-DRL: Automated Penetration Testing Using Deep Reinforcement Learning AutoPentest-DRL is an automated penetration testing framework based o