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Testpyramidsrnd

Developed by mariastull
This is a reinforcement learning agent based on the PPO algorithm, specifically trained for the Unity ML-Agents pyramid environment.
Downloads 22
Release Time : 7/11/2022

Model Overview

This model is trained using the PPO (Proximal Policy Optimization) algorithm and can perform navigation or goal achievement tasks in Unity's ML-Agents pyramid environment.

Model Features

Unity Environment Integration
Designed specifically for the Unity ML-Agents pyramid environment, it can run directly in the Unity simulator.
PPO Algorithm
Uses the Proximal Policy Optimization algorithm to balance exploration and exploitation for stable training.
Visual Demonstration
Supports direct viewing of the agent's performance via Hugging Face Spaces.

Model Capabilities

3D Environment Navigation
Target Recognition and Achievement
Reinforcement Learning Decision Making

Use Cases

Game AI
Pyramid Exploration AI
The agent autonomously explores and completes tasks in the pyramid environment.
Observe the agent's decision-making process in a complex 3D environment.
Reinforcement Learning Education
PPO Algorithm Demonstration
Demonstrates the practical application of the PPO algorithm in a 3D environment.
Intuitively understand the reinforcement learning training process.
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