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Ppo Huggy
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Ppo Huggy

Developed by alex17127
This is a PPO agent model trained using the Unity ML-Agents library, specifically designed for the Huggy game.
Multimodal Fusion
TensorBoard
#Unity Game AI#Deep Reinforcement Learning#PPO Algorithm
Downloads 75
Release Time : 3/31/2025

Model Overview

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

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 toolkit, making it easy to deploy and use in Unity environments.
Game Agent Control
Agent specifically designed for the Huggy game, capable of performing specific tasks within the game.

Model Capabilities

Game Agent Control
Reinforcement Learning Decision Making
Unity Environment Interaction

Use Cases

Game Development
Huggy Game AI
Acts as a non-player character (NPC) or opponent AI in the Huggy game
Capable of autonomously completing specific tasks in the game
Educational Demonstration
Reinforcement Learning Teaching
Used to demonstrate the application of the PPO algorithm in a game environment
Visually demonstrates how deep reinforcement learning works
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