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Testworm
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Testworm

Developed by curt-tigges
A reinforcement learning agent based on the PPO algorithm, specifically trained to play the Snake game
Image Generation
TensorBoard
#Unity Game AI#Deep Reinforcement Learning#Snake Game Agent
Downloads 85
Release Time : 9/5/2022

Model Overview

This model is trained using the Unity ML-Agents framework with the PPO (Proximal Policy Optimization) algorithm and can autonomously play the Snake game.

Model Features

Based on PPO Algorithm
Trained using the Proximal Policy Optimization algorithm, an advanced reinforcement learning algorithm
Unity ML-Agents Integration
Fully compatible with the Unity ML-Agents framework, making it easy to deploy and run in Unity environments
Specialized for Snake Game
Specifically trained for the Snake game, capable of autonomous decision-making to achieve game objectives

Model Capabilities

Snake Game Control
Reinforcement Learning Decision-Making
Game Strategy Optimization

Use Cases

Game AI
Snake Game AI
Acts as an AI opponent or demo AI for the Snake game
Capable of autonomously completing the Snake game
Reinforcement Learning Education
PPO Algorithm Example
Serves as a practical application case of the PPO algorithm
Demonstrates the performance of the PPO algorithm in simple games
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