PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control

Deep Reinforcement Learning for Robotic Grasping from Octrees

Our codebase trials provide an implementation of the Select and Trade paper, which proposes a new paradigm for pair trading using hierarchical reinforcement learning. It includes the code for the proposed method and experimental results on real-world stock data to demonstrate its effectiveness.

OpenAI Gym environment solutions using Deep Reinforcement Learning.

SocialGym 2: A lightweight benchmark and simulator for multi-robot social navigation using ROS and the OpenAI gym.

Stable-Baselines3 (SB3) reinforcement learning tutorial for the Reinforcement Learning Virtual School 2021.

OpenAI Gym environment designed for training RL agents to control the flight of a two-dimensional drone.

Deep Reinforcement Learning based autonomous navigation for quadcopters using PPO algorithm.

Godot Gym API is an Open Source framework for using Godot3 game engine as 3d-environment for training reinforcement learning agents implemented in Python on any data, including images and point clouds.

This repository contains an application using ROS2 Humble, Gazebo, OpenAI Gym and Stable Baselines3 to train reinforcement learning agents for a path planning problem.

🚗 This repository offers a ready-to-use training and evaluation environment for conducting various experiments using Deep Reinforcement Learning (DRL) in the CARLA simulator with the help of Stable Baselines 3 library.

Reinforcement Learning tool for Network Slice Placement problems

My implementation of a reinforcement learning model using Stable-Baselines3 to play the NES Super Mario Bros.

Implementation of Jump-Start Reinforcement Learning (JSRL) with Stable Baselines3

A highly-customizable OpenAI gym environment to train & evaluate RL agents trading stocks and crypto.

Train quadruped locomotion using reinforcement learning in Mujoco

[IROS 22'] Model-free Neural Lyapunov Control

Data Center Environment and Reinforcement Learning (RL) Control

Study on the application of reinforcement learning to the management of a traffic light intersection.

Python package implementing task generators, traditional and ML-based scheduling algorithms, and assessment tools.