Deep Q-Learning Network using PyTorch
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Updated
Jun 12, 2024 - Jupyter Notebook
Deep Q-Learning Network using PyTorch
Using Q-Learning methods in Gymnasium to solve various games, very basic implementation.
Repository contains codes for the course CS780: Deep Reinforcement Learning
Nokia's classic 'snake' game, written in NumPy and converted into a Gymnasium Environment() for use with gradient-based reinforcement learning algorithms
Gymnasium environment based on real room and robot
Maze gymnasium-compatible for Reinforcement learning
The Docker image for the isolated Mujoco environment
PettingZoo ConnectFour and TicTacToe examples, configured with Rye as dependency manager
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.
Spatio-temporal wildlife management Gymnasium RL Environment created and trained on for my Master's thesis
Implementation of DQN and DDQN algorithms for Playing Car Racing Game
Open-the-chests is a training environment for event-patterns recognition.
A Gymnasium environment and RL algorithms for navigation on human arms using ultrasound/MRI
Try to reproduce basic example of Deep Q Learning (DQN) with Pytorch
Reinforcement Learning Environment Connect X Game + Gymnasium + PyGame GUI
Lunar Lander envitoment of gymnasium solved using Double DQN and D3QN
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.
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.
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