PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
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Updated
May 12, 2024 - Python
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.
Deep Reinforcement Learning based autonomous navigation for quadcopters using PPO algorithm.
OpenAI Gym environment solutions using Deep Reinforcement Learning.
Stable-Baselines3 (SB3) reinforcement learning tutorial for the Reinforcement Learning Virtual School 2021.
SocialGym 2: A lightweight benchmark and simulator for multi-robot social navigation using ROS and the OpenAI gym.
OpenAI Gym environment designed for training RL agents to control the flight of a two-dimensional drone.
Regen is an end-to-end application that showcases how to train and deploy reinforcement learning trading agents
Reinforcement learning scripts for sofa_env environments.
🚗 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.
This is a solution for the second project of the Udacity deep reinforcement learning course. It includes code for training an agent and for using it in a simulation environment.
A highly-customizable OpenAI gym environment to train & evaluate RL agents trading stocks and crypto.
Python package implementing task generators, traditional and ML-based scheduling algorithms, and assessment tools.
This repository contains an application using ROS2 Humble, Gazebo, OpenAI Gym and Stable Baselines3 to train reinforcement learning agents for a path planning problem.
Predator-Prey-Grass gridworld environment using PettingZoo, with dynamic deletion and spawning of partially observant agents.
Reinforcement Learning for VRP
Implementation of Jump-Start Reinforcement Learning (JSRL) with Stable Baselines3
OpenAI Gym environment designed for training RL agents to bring CartPole upright and its further balancing.
OpenAI Gym environment designed for training RL agents to balance double CartPole.
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