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Ppo Huggy

Developed by ErenDoymus
This is a PPO agent model trained using the Unity ML-Agents library, specifically designed to run the Huggy Game.
Multimodal Fusion
TensorBoard
#Unity Game AI#Deep Reinforcement Learning#Agent Training
Downloads 30
Release Time : 2/18/2025

Model Overview

This model is a deep reinforcement learning agent based on the PPO (Proximal Policy Optimization) algorithm, used to perform specific tasks in the Huggy Game environment.

Model Features

Based on PPO Algorithm
Trained using the Proximal Policy Optimization algorithm, an advanced deep reinforcement learning method.
Unity ML-Agents Integration
Fully compatible with the Unity ML-Agents framework, making it easy to deploy in Unity game environments.
Browser Demo Support
You can directly watch the agent's performance in a browser.

Model Capabilities

Game Agent Control
Reinforcement Learning Decision Making
Environment Interaction

Use Cases

Game AI
Huggy Game Agent
Controls the game character to perform specific tasks in the Huggy Game environment
Capable of achieving the game's set objectives
Educational Demonstration
Reinforcement Learning Teaching
Serves as a teaching case for deep reinforcement learning
Helps students understand the PPO algorithm and game AI development
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