Simple Gridworld Gymnasium Environment
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Updated
Jun 25, 2024 - Python
Simple Gridworld Gymnasium Environment
A framework for creating rich, 3D, Minecraft-like single and multi-agent environments for AI research based on Minetest
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++
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
Automated stock trading strategy using deep reinforcement learning and recurrent neural networks
Reinforcement Learning Environment Connect X Game + Gymnasium + PyGame GUI
A Gymnasium environment for simulating and training reinforcement learning agents on the BlueROV2 underwater vehicle.
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.
Implementation of DQN and DDQN algorithms for Playing Car Racing Game
A sailing environment for OpenAI Gym / Gymnasium
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.
A reinforcement learning model for the Da Vinci Code game
Try to reproduce basic example of Deep Q Learning (DQN) with Pytorch
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