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Testpyramidsrnd

Developed by Mahmoud7
This is a reinforcement learning agent based on the PPO algorithm, specifically trained for the Unity ML-Agents pyramid environment.
Multimodal Fusion
TensorBoard
#Unity Reinforcement Learning#3D Environment Navigation#PPO Algorithm
Downloads 16
Release Time : 8/11/2022

Model Overview

This model uses the PPO (Proximal Policy Optimization) algorithm to train in Unity's ML-Agents pyramid environment, capable of achieving specific navigation or task-solving objectives.

Model Features

Unity Environment Integration
Designed specifically for the Unity ML-Agents pyramid environment, it can be directly deployed and run in Unity.
PPO Algorithm
Utilizes the Proximal Policy Optimization algorithm to balance exploration and exploitation, achieving stable policy learning.
Live Demonstration
Supports live demonstrations via Hugging Face Spaces.

Model Capabilities

Environment Navigation
Task Solving
Reinforcement Learning Decision Making

Use Cases

Game AI
Pyramid Environment Navigation
The agent navigates and completes tasks in a pyramid maze.
The agent's decision-making path in the environment can be observed.
Educational Demonstration
Reinforcement Learning Teaching
Demonstrates the application of the PPO algorithm in a real-world environment.
Visually showcases the effects of reinforcement learning training.
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