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Ppo PongNoFrameskip V4
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Ppo PongNoFrameskip V4

Developed by sb3
This is a reinforcement learning model based on the PPO algorithm, specifically designed for the PongNoFrameskip-v4 environment.
Video Processing#Atari Game AI#Frame Stacking Reinforcement Learning#Multi-environment Parallel Training
Downloads 46
Release Time : 6/2/2022

Model Overview

The model is developed using the stable-baselines3 library and RL Zoo training framework, capable of implementing efficient game strategies in the no-frame-skip Pong game environment.

Model Features

Efficient PPO Algorithm
Uses the Proximal Policy Optimization algorithm to achieve efficient learning while maintaining training stability.
Atari-specific Preprocessing
Includes frame stacking and Atari-specific preprocessing to optimize game environment input.
Multi-environment Parallel Training
Supports training across 8 parallel environments to accelerate the learning process.

Model Capabilities

Atari Game Control
Reinforcement Learning Strategy Optimization
Real-time Decision Making

Use Cases

Game AI
Pong Game AI
Achieves high-level automated gameplay capability in Pong
Average reward reaches 21.00
Reinforcement Learning Research
Algorithm Benchmarking
Serves as a performance benchmark for the PPO algorithm in Atari environments
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