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.

Lunar Lander envitoment of gymnasium solved using Double DQN and D3QN

Try to reproduce basic example of Deep Q Learning (DQN) with Pytorch

This repository is a development environment for my thesis which is "Reinforcement learning based automated parking systems".

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.

Implementation of DQN and DDQN algorithms for Playing Car Racing Game

Gymnasium environment based on real room and robot

Deep Q-Learning Network using PyTorch

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

Using Q-Learning methods in Gymnasium to solve various games, very basic implementation.

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