DQN-SpaceInvadersNoFrameskip-v4 Open-Source Game Model - Free Deployment to Enjoy the Space Invaders Game

Dqn SpaceInvadersNoFrameskip V4

Developed by awalmeida

This is a deep reinforcement learning model based on the DQN algorithm, specifically designed for the SpaceInvadersNoFrameskip-v4 game environment.

Downloads 13

Release Time : 6/8/2022

Model Overview

The model is trained using the stable-baselines3 library and can achieve high scores in the Space Invaders game through reinforcement learning.

Specialized for Atari Games

Optimized specifically for the Atari game environment SpaceInvadersNoFrameskip-v4

Stable Training Framework

Uses the stable-baselines3 library and RL Zoo training framework to ensure training stability

Efficient Exploration Strategy

Employs epsilon-greedy exploration strategy to balance exploration and exploitation

Atari Game Control

Reinforcement Learning Decision Making

Real-time Game Response

Game AI

Space Invaders Game AI

Automatically plays Space Invaders

Average reward 657.00 +/- 102.03

Reinforcement Learning Research

DQN Algorithm Validation

Used to validate the performance of Deep Q-Network in Atari games

Property	Details
Library Name	stable-baselines3
Tags	SpaceInvadersNoFrameskip-v4, deep-reinforcement-learning, reinforcement-learning, stable-baselines3
Model Name	DQN
Mean Reward	657.00 +/- 102.03
Task	reinforcement-learning
Dataset	SpaceInvadersNoFrameskip-v4

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