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 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.
Lunar Lander envitoment of gymnasium solved using Double DQN and D3QN
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Gymnasium car environment. Autonomous Racing with Proximal Policy Optimization and custom tracks.
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.
Try to reproduce basic example of Deep Q Learning (DQN) with Pytorch
Implementation of DQN and DDQN algorithms for Playing Car Racing Game
A reinforcement learning model for the Da Vinci Code game
Using Q-Learning methods in Gymnasium to solve various games, very basic implementation.
Spatio-temporal wildlife management Gymnasium RL Environment created and trained on for my Master's thesis
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