A framework for creating rich, 3D, Minecraft-like single and multi-agent environments for AI research based on Minetest

Simple Gridworld Gymnasium Environment

SustainDC is a set of Python environments for Data Center simulation and control using Heterogeneous Multi Agent Reinforcement Learning. Includes customizable environments for workload scheduling, cooling optimization, and battery management, with integration into Gymnasium.

A collection of RL gymnasium environments for learning to grasp 3D deformable objects.

Autonomous driving episode generation for the Carla simulator in a gym environment. This framework makes it easy to create driving scenarios to train/test the agent.

Environments for reinforcement learning with C++

Automated stock trading strategy using deep reinforcement learning and recurrent neural networks

Lunar Lander envitoment of gymnasium solved using Double DQN and D3QN

A Complete Collection of Deep RL Famous Algorithms implemented in Gymnasium most Popular environments

A Gymnasium environment for simulating and training reinforcement learning agents on the BlueROV2 underwater vehicle.

Implementation of DQN and DDQN algorithms for Playing Car Racing Game

Reinforcement Learning Environment Connect X Game + Gymnasium + PyGame GUI

A sailing environment for OpenAI Gym / Gymnasium

A Gymnasium environment and RL algorithms for navigation on human arms using ultrasound/MRI

Open-the-chests is a training environment for event-patterns recognition.

Gymnasium car environment. Autonomous Racing with Proximal Policy Optimization and custom tracks.

The Room (Gymnasium) environment

Green-DCC is a benchmark environment for evaluating dynamic workload distribution techniques for sustainable Data Center Clusters (DCC) using reinforcement learning and other control algorithms.

Try to reproduce basic example of Deep Q Learning (DQN) with Pytorch

A reinforcement learning model for the Da Vinci Code game