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Ppo Huggy

Developed by ThomasSimonini
This is a PPO agent model trained using the Unity ML-Agents library, specifically designed for the Huggy game.
Image Generation
TensorBoard
#Unity Game AI#Deep Reinforcement Learning#PPO Algorithm
Downloads 28
Release Time : 4/20/2022

Model Overview

This model is trained based on the PPO (Proximal Policy Optimization) algorithm and can perform reinforcement learning in the Huggy game environment to achieve game objectives.

Model Features

Based on PPO Algorithm
Trained using the Proximal Policy Optimization algorithm, ensuring stable learning performance.
Unity Environment Integration
Designed specifically for Unity game environments and can be deployed directly in the Unity Editor.
Continuous Learning Capability
Supports further training of the model via the mlagents-learn command.

Model Capabilities

Game Control
Reinforcement Learning
Environment Interaction

Use Cases

Game AI
Huggy Game Agent
Controls the character as an AI player in the Huggy game.
Capable of achieving the game's set objectives.
Reinforcement Learning Research
PPO Algorithm Validation
Serves as an implementation case of the PPO algorithm in a game environment.
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