There are 2 repositories under deepq-learning topic.
Use Deep Q-Learning model to optimize energy consumption of a data center
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
A trading strategy using optimal algorithms for k-Search
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).
Reinforcement learning algorithm implements.
AI learns to play Flappy Bird using Deep Q Learning
Pacman game and AI agents
RL Agent for Pong Game using Openai Gym and PyTorch
The purpose of this project is to train a Deep Q-Network agent (https://daiwk.github.io/assets/dqn.pdf) using the OpenAI Gym environment (https://gym.openai.com/) to play the famous Atari game BreakOut. The DQN agent has 3 main components: the online Q-network, the target Q-network, and a replay buffer.
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.
Using Deep Q Learning to solve a maze
Here are few solutions made for OpenAI Gym tasks (TaxiCar, FrozenLake, Mountain Car, Cliff Walking...) using Tensorflow Keras
Deep Q-Learning, a reinforcement learning method, to autonomously land a spacecraft on the moon.
This is a demo of a self driving car in python using PyTorch and Kivy
Using Deep Q Reinforcement Learning, watch our Minecraft agent, Steve, protect himself for as long as possible against Ghasts by building a shelter-like block structure.
Q and DeepQ Reinforcement Learning for NIM
Explore the role of prior knowledge in the learning curve and adaptability of both humans and RL agents
Double Deep Q-Learnig for the FrozenLake environment.
The development of an AI agent proficient in the classic game 'Breakout,' utilizing deep reinforcement learning and a DQN architecture.
I utilized the A3C (Asynchronous Advantage Actor-Critic) algorithm to train a Deep Q-Learning (DQN) model, specifically tailored to solve the Kungfu gym environment.
Trained a Deep Q-Learning agent to autonomously land a lunar module in OpenAI's Gymnasium Lunar Lander environment.
This project is about implementing the reinforcement learning algorithm Deep Q-Learning on the Nokia's Snake Game to predict the actions. It makes use of the Python's Pygame and Pytorch libraries.
AI reinforcement learning virtual lunar lander project
This repository contains code for a project on training reinforcement learning agents to play the game of Hex. This involved testing various RL algorithms such as PPO, AlphaZero, SAC, and Deep Q-learning on different board sizes.
This repository contains implementations of various reinforcement learning algorithms, including Q-Learning, Deep Q Networks (DQN), Policy Gradient methods, and more. Explore, learn, and apply these algorithms to solve challenging problems in AI and machine learning.
Train an AI agent to play snake game