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.
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.
A collection of RL gymnasium environments for learning to grasp 3D deformable objects.
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.
Deep Q-Learning Network using PyTorch
Using Q-Learning methods in Gymnasium to solve various games, very basic implementation.
Nokia's classic 'snake' game, written in NumPy and converted into a Gymnasium Environment() for use with gradient-based reinforcement learning algorithms
A Gymnasium environment and RL algorithms for navigation on human arms using ultrasound/MRI
Repository contains codes for the course CS780: Deep Reinforcement Learning
Lunar Lander envitoment of gymnasium solved using Double DQN and D3QN
Gymnasium environment based on real room and robot
Maze gymnasium-compatible for Reinforcement learning
Try to reproduce basic example of Deep Q Learning (DQN) with Pytorch
Open-the-chests is a training environment for event-patterns recognition.
The Docker image for the isolated Mujoco environment
Implementation of DQN and DDQN algorithms for Playing Car Racing Game
PettingZoo ConnectFour and TicTacToe examples, configured with Rye as dependency manager
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