Deep Q-Learning Network using PyTorch
-
Updated
Jun 12, 2024 - Jupyter Notebook
Deep Q-Learning Network using PyTorch
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.
Using Q-Learning methods in Gymnasium to solve various games, very basic implementation.
Gymnasium environment based on real room and robot
Spatio-temporal wildlife management Gymnasium RL Environment created and trained on for my Master's thesis
Nokia's classic 'snake' game, written in NumPy and converted into a Gymnasium Environment() for use with gradient-based reinforcement learning algorithms
Experiments with Dyna-Q
Repository contains codes for the course CS780: Deep Reinforcement Learning
Lunar Lander envitoment of gymnasium solved using Double DQN and D3QN
A Gymnasium environment and RL algorithms for navigation on human arms using ultrasound/MRI
Reinforcement learning with Deep Convolutional Q Learning model
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
Try to reproduce basic example of Deep Q Learning (DQN) with Pytorch
Maze gymnasium-compatible for Reinforcement learning
Open-the-chests is a training environment for event-patterns recognition.
Simple AI playing the 421 dice game using Reinforcement Learning.
The Room (Gymnasium) environment
Add a description, image, and links to the gymnasium-environment topic page so that developers can more easily learn about it.
To associate your repository with the gymnasium-environment topic, visit your repo's landing page and select "manage topics."