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Ppo PongNoFrameskip V4

Developed by ThomasSimonini
This is a PPO agent trained using the stable-baselines3 library, specifically designed to play the Atari game PongNoFrameskip-v4.
Downloads 148
Release Time : 3/2/2022

Model Overview

The model is trained with the PPO algorithm and can compete as the green side in the PongNoFrameskip-v4 game, achieving an average reward of 21 points.

Model Features

High-performance Game AI
Achieves an excellent performance with an average score of 21 in the PongNoFrameskip-v4 game
Based on Stable Reinforcement Learning Framework
Implemented using the stable-baselines3 library, a widely recognized reinforcement learning framework
Frame Stack Processing
Uses a 4-frame stacking technique to process game screens, enhancing the model's understanding of dynamic environments

Model Capabilities

Atari game PongNoFrameskip-v4 competition
Reinforcement learning environment interaction
Real-time game decision making

Use Cases

Game AI
Pong Game Competition
Acts as an AI player to compete against humans or other AIs in Pong
Average reward of 21 points
Reinforcement Learning Research
Serves as a benchmark model for reinforcement learning algorithm research
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