Implementation of Deep Q Learning on solving the honeypot placement problem.
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Updated
Jun 12, 2024 - Python
Implementation of Deep Q Learning on solving the honeypot placement problem.
In this repository you will see an agent playing the Snake game
Les intelligences artificielles
A coding project for Math 480 Combinatorial Game Theory (Spring 2024) exploring the relationship between AI and combinatorial game theory. Specifically, I will study the game Toads and Frogs by implementing a DQN reinforcement learning algorithm from scratch.
Implement Deep Q Learning to train agent for Lunar Landing
repo for learning reinforcement learning from scratch
reinforcement learning applied to simple board games
This project provides a comprehensive understanding of reinforcement learning, focusing on Deep Q-Learning (DQN). It involves exploring the OpenAI Gym library, implementing DQN from DeepMind's seminal paper, and enhancing the DQN algorithm for improved performance and stability.
Try to reproduce basic example of Deep Q Learning (DQN) with Pytorch
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Scratch solution to the OpenAI Discrete Lunar Lander Environment using Deep Q-Learning.
Lunar Lander training using Deep-Q-Learning
Reinforcement learning for playing flappy bird game
Deep Q-Learning consists of combining Q-Learning with Artificial Neural Networks. Inputs are encoded vectors, each one defining a state of the environment. These inputs go to an Artificial Neural Network, where the output is the action to play
Implementation of the Double Deep Q-Learning algorithm with a prioritized experience replay memory to train an agent to play the minichess variante Gardner Chess
The code release of "Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning" paper, ICAART 2021
🤖 A deep Q-learning AI model trained with TensorFlow in Python powers a 3D Connect 4 web application.
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