MLAgents-Pyramids Open-Source Agent Model - Free for Reinforcement Learning Tasks in Pyramid Environments

Mlagents Pyramids

Developed by PraveenKishore

This is a PPO algorithm agent model trained using the Unity ML-Agents library, specifically designed for reinforcement learning tasks in pyramid environments.

Molecular Model

TensorBoard

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

Downloads 28

Release Time : 10/26/2022

Model Overview

This model is based on the PPO (Proximal Policy Optimization) algorithm and trained in Unity's ML-Agents pyramid environment, capable of performing specific 3D environment navigation and task solving.

Model Features

Based on Unity ML-Agents

Trained using Unity's powerful ML-Agents framework, supporting complex 3D environment interactions

PPO Algorithm Implementation

Utilizes the Proximal Policy Optimization algorithm, a stable and efficient reinforcement learning algorithm

Pyramid Environment Training

Specially optimized for Unity's pyramid environment, capable of handling its unique navigation challenges

Model Capabilities

3D Environment Navigation

Reinforcement Learning Decision Making

Environment Interaction

Use Cases

Game AI

Smart NPC Behavior

Can be used to develop non-player characters with autonomous decision-making capabilities in games

Enables NPCs to autonomously navigate complex 3D environments

Robot Simulation

Virtual Robot Training

Trains robot navigation algorithms in virtual environments

Reduces costs and risks associated with physical robot training

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Mlagents Pyramids

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 PPO Agent Playing Pyramids

🚀 Quick Start

📚 Documentation

💻 Usage Examples

Resume the training

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