PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
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Updated
Jun 7, 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.
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.
Deep Reinforcement Learning based autonomous navigation for quadcopters using PPO algorithm.
OpenAI Gym environment designed for training RL agents to control the flight of a two-dimensional drone.
🚗 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 repository contains an application using ROS2 Humble, Gazebo, OpenAI Gym and Stable Baselines3 to train reinforcement learning agents for a path planning problem.
Implementation of Jump-Start Reinforcement Learning (JSRL) with Stable Baselines3
Reinforcement Learning tool for Network Slice Placement problems
A highly-customizable OpenAI gym environment to train & evaluate RL agents trading stocks and crypto.
[IROS 22'] Model-free Neural Lyapunov Control
Worst-case MSE Minimization for RIS-assisted mmWave MU-MISO Systems with Hardware Impairments and CSI Imperfection
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.
Predator-Prey-Grass gridworld environment using PettingZoo, with dynamic deletion and spawning of partially observant agents.
OpenAI Gym environment designed for training RL agents to balance double CartPole.
Reinforcement learning scripts for sofa_env environments.
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