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Ppo SpaceInvadersNoFrameskip V4
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Ppo SpaceInvadersNoFrameskip V4

Developed by sb3
This is a reinforcement learning agent based on the PPO algorithm, specifically designed for training and gameplay in the SpaceInvadersNoFrameskip-v4 game environment.
Video Processing#Atari game reinforcement learning#Multi-environment parallel training#Frame stacking input
Downloads 8,999
Release Time : 6/2/2022

Model Overview

The model is trained using the stable-baselines3 library and RL Zoo framework, enabling autonomous gameplay in the Atari game Space Invaders.

Model Features

High-performance Game AI
Achieved an average reward performance of 886.50 ± 417.30 in the SpaceInvaders game
Stable Training Framework
Based on the stable-baselines3 and RL Zoo training framework, providing a reliable training process
Specially Optimized Hyperparameters
Hyperparameters are specifically optimized for the SpaceInvaders game environment

Model Capabilities

Atari game control
Reinforcement learning decision-making
Game state understanding

Use Cases

Game AI
Space Invaders Game AI
Automatically plays the Space Invaders game
Average reward 886.50 ± 417.30
Reinforcement Learning Research
PPO Algorithm Research
Research on the performance of PPO algorithm in Atari games
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