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Dqn SpaceInvadersNoFrameskip V4
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Dqn SpaceInvadersNoFrameskip V4

Developed by sb3
This is a Deep Q-Network (DQN) based reinforcement learning agent, specifically trained to play the Atari game 'Space Invaders'
Video Processing#Atari game control#Deep Q-Learning#High frame rate optimization
Downloads 58
Release Time : 6/2/2022

Model Overview

This model is implemented using the stable-baselines3 library, trained through deep reinforcement learning in the SpaceInvadersNoFrameskip-v4 environment, capable of automatically playing the Atari game 'Space Invaders'

Model Features

Specialized for Atari games
Reinforcement learning model optimized specifically for Atari game environments
Frame stacking processing
Uses 4-frame stacking technique to capture game dynamics
Efficient exploration strategy
Employs epsilon-greedy exploration strategy with exploration rate linearly decaying from initial value to 0.01

Model Capabilities

Atari game control
Reinforcement learning decision-making
Image input processing

Use Cases

Game AI
Space Invaders game AI
Automatically plays the Atari game 'Space Invaders'
Average score 680±231.26
Reinforcement learning research
DQN algorithm validation
Used to validate the performance of Deep Q-Network on Atari games
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