Use Deep Q-Learning model to optimize energy consumption of a data center

Geometry Dash Bot

Reinforcement Learning with the classic snake game

Recommendation System using Deep Q-Networks and Double Deep Q-Networks

The objective is to teach robot to find and reach the target object in the minimum number of steps and using the shortest path and avoiding any obstacles such as humans, walls, etc usinf reinforcement learning algorithms.

Reinforcement learning using CARLA simulator

Deep Reinforcement Learning navigation of autonomous vehicles. Implementation of deep-Q learning, dyna-Q learning, Q-learning agents including SSMR(Skid-steering_mobile robot) Kinematics in various OpenAi gym environments

Solving MountainCar-v0 environment in Keras with Deep Q Learning an Deep Reinforcement Learning algorithm

Utilizing reinforcement learning, this project implements a dynamic algorithmic trading strategy based on Q-learning with a deep Q-network. The Jupyter Notebook explores agent decisions on buying, selling, or holding Nasdaq stocks over a ten-year period (2014-2023).

A trading strategy using optimal algorithms for k-Search

Tic Tac Toe game, designed to be used to train a Deep Neural Network via Reinforcement Learning (DQN). It can also be played by 2 humans and features a hard coded AI that never looses and will win if you do not do perfect play against it.

Reinforcement learning algorithm implements.

Pacman game and AI agents

AI learns to play Flappy Bird using Deep Q Learning

This is a demo of a self driving car in python using PyTorch and Kivy

This project investigates the intuitions/ideas behind Double DQN, and evaluate how much it can improve Q-value overestimation and agent performance. We aim to describe how the learning/update process in Double DQN ends up with better Q-value estimates and agent performance when comparing to that of DQN.

Reinforcement learning project on driving a car in a 2d environment

RL Agent for Pong Game using Openai Gym and PyTorch

Using Deep Q Learning to solve a maze