Deep Q-Learning Network using PyTorch
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Updated
Jun 12, 2024 - Jupyter Notebook
Deep Q-Learning Network using PyTorch
Gymnasium environment based on real room and robot
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.
Repository contains codes for the course CS780: Deep Reinforcement Learning
Lunar Lander envitoment of gymnasium solved using Double DQN and D3QN
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
Try to reproduce basic example of Deep Q Learning (DQN) with Pytorch
Maze gymnasium-compatible for Reinforcement learning
A Gymnasium environment and RL algorithms for navigation on human arms using ultrasound/MRI
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
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.
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