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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.

Model Features

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.

Model Capabilities

Game AI Control
Reinforcement Learning Decision-Making
Environment Interaction

Use Cases

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.
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