An RL implementation in Keras

Evolutionary Reinforcement Learning for Dino Game: Train an AI agent to master Google Chrome's Dino Game using a genetic algorithm and reinforcement learning.

Reinforcement Learning to train an AI agent to play Dino Run on Chrome.

This reference playing atari with deep reinforcement learning and use https://blog.paperspace.com/dino-run/ code to play Google Chrome's offline Dino Run game.

Pytorch implementation of DQN, 1-step SARSA, REINFORCE, A2C, 1-step actor critic on Google Chrome dinosaur

Chrome Dino Game Bot, that automatically learns and plays the game by reinforcement learning

Reinforcement learning on Chrome's Dino game

An implementation of Reinforcement Learning for Dino Game using TF-Agents

The project is about using Reinforcement Learning to train an AI that can play Google Dino Game and compare the learning performance among different reward systems.

A Reinforcement Learning project that trains a Double Deep Q-Network to excel in the Chrome Dino Game by dodging obstacles and maximizing its score through iterative learning.

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Using the NeuroEvolution of Augmenting Topologies (NEAT) algorithm to create a model to play the chrome dino game

Play Chrome Dino game using deep reinforcement learning

Deep Reinforcement Learning (DQN) with Chrome Dino Game

A reinforcement learning ai model to play the chrome Dino game!

Artificial Intelligence playing Chrome Dino game trying to improve using Reinforcement Learning.

The AI model here can play Google Dino AI using Reinforcement Learning (DQN algorithm)

Reinforcement Learning Based Open AI gym custom environment for a Chrome Dino inspired game

Using a Reinforcement Learning Algorithm to create Artifical Intelligence capable of playing the Chrome Dino Game

Capstone I project for Fall 2021. Reinforcement learning with Deep QLearning to play the google dino game

Getting an AI to play Chrome Dino game using Reinforcement Learning. Part of the recruitment process for Coding Club, BITS Pilani

This repository features a Deep Q-Learning agent implemented with PyTorch and Pygame. The agent is designed to play and learn from the Chrome Dino game, showcasing reinforcement learning techniques

This project implements a deep reinforcement learning system that autonomously learns to play Chrome's Dino game. The agent uses a ResNet-inspired Double DQN architecture with 1.5M parameters, processing 4-frame stacks for temporal velocity information and making real-time decisions at 16.67 FPS.

Dino Bot Made by using Selenium-Webdriver module, OpenCv And Reinforcement Learning(Q learning) to play Dino Game of Google Chrome.

An implementation of reinforcement learning on a pre-built PyGame version of Dino Run.

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Segundo trabalho da disciplina de Inteligencia Artifical, utilizando aprendizado por reforço para construir um agente inteligente para jogar o jogo do DINO