PPO - Huggy Open Source Model - Free Deployment for Precise Control of the Behavior of Virtual Dog Huggy

Ppo Huggy

Developed by hellonihao

This is a PPO agent model trained using the Unity ML-Agents library, specifically designed to control the behavior of the virtual dog Huggy.

Object Detection

TensorBoard

#Unity Agent Training #Deep Reinforcement Learning #Virtual Pet Interaction

Downloads 52

Release Time : 2/18/2025

Model Overview

This model is trained using the PPO algorithm in deep reinforcement learning to control the virtual dog Huggy in completing specific tasks, such as interactive behaviors like fetching sticks.

Model Features

Based on Unity ML-Agents

Trained and deployed using the Unity ML-Agents library, compatible with Unity virtual environments.

PPO Algorithm

Utilizes the PPO algorithm in deep reinforcement learning to balance training stability and efficiency.

Interactive Demo

Supports viewing the agent's performance directly in a browser.

Model Capabilities

Virtual Character Control

Reinforcement Learning Decision-Making

Environment Interaction

Use Cases

Game AI

Virtual Pet Control

Control the virtual dog to complete interactive tasks like fetching sticks.

Observe the agent's learned behavior.

Educational Demo

Reinforcement Learning Teaching

Used to demonstrate the application of the PPO algorithm in virtual environments.

Visually showcases the reinforcement learning training process.

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Ppo Huggy

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 PPO Agent Playing Huggy

🚀 Quick Start

Usage (with ML - Agents)

Resume the training

Watch your Agent play