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Ppo Huggy
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Ppo Huggy

Developed by Mouryahimanshu
This is a deep reinforcement learning agent based on the PPO algorithm, designed to play the Huggy game, trained using the Unity ML-Agents library.
Object Detection
TensorBoard
#Huggy Game AI#PPO Reinforcement Learning#Unity Agent
Downloads 60
Release Time : 4/3/2025

Model Overview

This model is a trained PPO algorithm agent specifically designed for playing the Huggy game. It demonstrates the application of deep reinforcement learning in game AI.

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, facilitating deployment and use in Unity environments.
Game AI Control
Specifically trained for the Huggy game, showcasing the potential of reinforcement learning in game AI applications.

Model Capabilities

Game Control
Reinforcement Learning Decision Making
Real-time Environment Interaction

Use Cases

Game Development
Huggy Game AI
Acts as an AI opponent or assistant in the Huggy game
Capable of autonomously completing specific tasks within the game
Educational Demonstration
Reinforcement Learning Teaching
Demonstrates the application of the PPO algorithm in a game environment
Visually illustrates how deep reinforcement learning works
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