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.

🚗 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.

Train quadruped locomotion using reinforcement learning in Mujoco

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

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

OpenAI Gym environment solutions using Deep Reinforcement Learning.

Code base for SICNav T-RO paper and SICNav-Diffusion RA-L paper

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

RosNav-RL is a modular DRL framework for ROS 2 with a pluggable architecture, allowing you to switch between RL backends like Stable-Baselines3 and DreamerV3 to accelerate research and deployment.

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.

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

Reinforcement Learning tool for Network Slice Placement problems

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.

Developed a reinforcement learning framework using Deep Q-Networks (DQN) to optimize scheduling in Wireless Sensor Networks (WSN), enhancing energy efficiency and state estimation through a custom simulation environment.

stable-baselines3 reinforcement learning on SUMO traffic light system

[IROS 22'] Model-free Neural Lyapunov Control