TestPyramidsRND Open-source Reinforcement Learning Model - Adapted to Unity Pyramid Environment Based on PPO Algorithm

Home

Testpyramidsrnd

Developed by AdiKompella

This is a reinforcement learning agent model trained using the PPO algorithm in the Unity ML-Agents pyramid environment

Protein Model

TensorBoard

#Unity Reinforcement Learning #3D Environment Navigation #PPO Algorithm Optimization

Downloads 15

Release Time : 7/13/2022

Model Overview

This model is based on the PPO (Proximal Policy Optimization) algorithm, specifically trained for the Unity ML-Agents pyramid environment, capable of performing specific reinforcement learning tasks in this environment

Model Features

Unity ML-Agents Compatibility

This model is fully compatible with the Unity ML-Agents framework and can be directly deployed and run in the Unity environment

PPO Algorithm Implementation

Trained using the advanced reinforcement learning algorithm Proximal Policy Optimization (PPO)

Specialized for Pyramid Environment

Specifically trained for Unity's pyramid environment, excelling in this setting

Model Capabilities

Environment Navigation

Decision Making

Reinforcement Learning Task Execution

Use Cases

Game AI

Pyramid Environment Navigation

Autonomous navigation and decision-making in Unity's pyramid environment

Capable of completing specific tasks in the pyramid environment

Reinforcement Learning Research

PPO Algorithm Application

Can serve as a case study for the application of the PPO algorithm in Unity environments

Demonstrates the performance of the PPO algorithm in 3D environments

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご

Testpyramidsrnd

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 PPO Agent Playing Pyramids

🚀 Quick Start

📚 Documentation

💻 Usage Examples

Resume the training

Watch your Agent play