ppo-Huggy Open Source Agent Model - Free for Reinforcement Learning Tasks in Hajji Games

Ppo Huggy

Developed by PampX

This is a PPO agent model trained using the Unity ML-Agents library, specifically designed for reinforcement learning tasks in the Huggy game.

Downloads 16

Release Time : 12/3/2024

Model Overview

The model is trained in the Huggy game environment using the PPO algorithm and can accomplish specific game tasks.

Trained with Unity ML-Agents

Trained using Unity's official ML-Agents library, seamlessly integrated with the Unity game engine.

PPO Algorithm Implementation

Utilizes the Proximal Policy Optimization algorithm, a stable and efficient reinforcement learning method.

Browser Demo Support

The trained model can be directly viewed in a browser to observe its in-game performance.

Game AI Control

Reinforcement Learning Decision-Making

Environment Interaction

Game Development

Huggy Game AI

Training intelligent AI characters for the Huggy game.

Capable of completing specific game tasks.

Educational Demonstration

Reinforcement Learning Teaching

Serves as a teaching case for reinforcement learning algorithms.

Demonstrates the application of the PPO algorithm in a game environment.

Property	Details
Library Name	ml - agents
Tags	Huggy, deep - reinforcement - learning, reinforcement - learning, ML - Agents - Huggy

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