A notebook walking through how to use Keras RL to solve Atari environments.

A collection of my implemented RL agents for games like Pacman, Pong, SpaceInvaders, Frozenlake, Taxi, Pixelcopter, Pyramids and a lot more by implementing various DRL algorithms using gym, unity-ml, pygame, sb3, rl-zoo and pandagym libraries. To see more advanced & interesting agents, please visit below link:

This project implements a Deep Reinforcement Learning (DRL) agent to play the classic Atari game Space Invaders using the DQN (Deep Q-Network) algorithm. The project includes a Jupyter notebook (space_invaders.ipynb) that walks through the process of training and testing the agent.

Implementation of DQN algorithm on the atari game Space Invaders

RL Atari SpaceInvaders using DQN

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Training an agent using deep Q network and convolutional neural networks

Tutorial on Reinforcement Learning using Atari Space Invaders

A Space Invader Bot

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A Reinforcement Learning model that utilizes a convolutional neural network to determine the loss of a particular action given the state and discounted future rewards. It is optimized through RMSProp in order to converge to a policy that maximizes score (rewards).

A reinforcement learning agent trained to play Atari's Space Invaders game

we gonna make a computer shoot things.

Deep reinforcement learning for custom Space Invaders game

Deep Reinforcement Learning for Atari Game Python | AI Plays Space Invaders

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Deep Reinforcement Learning - Space Invaders

Train an agent to play Space Invaders

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El objetivo de esta actividad es aplicar el conocimiento adquirido durante las sesiones prácticas de la asignatura. Para ello, vamos a trabajar con uno de los algoritmos base que hemos estudiado, Deep Q-network. Implementaremos este algoritmo utilizando la librería Keras-rl para llegar a una puntuación máxima en el entorno de SpaceInvaders-v0.