Simple Gridworld Gymnasium Environment
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Updated
Jun 25, 2024 - Python
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.
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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.
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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
A Gymnasium environment and RL algorithms for navigation on human arms using ultrasound/MRI
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
Spatio-temporal wildlife management Gymnasium RL Environment created and trained on for my Master's thesis
Using Q-Learning methods in Gymnasium to solve various games, very basic implementation.
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