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Testpyramidsrnd

Developed by Aitor
This is a reinforcement learning agent based on the PPO algorithm, specifically trained for navigation and task-solving in Unity's ML-Agents pyramid environment.
Protein Model
TensorBoard
#Unity Reinforcement Learning#3D Environment Navigation#PPO Algorithm Implementation
Downloads 15
Release Time : 10/27/2022

Model Overview

This model is trained using the Proximal Policy Optimization (PPO) algorithm and can autonomously learn and perform tasks in Unity ML-Agents' pyramid environment.

Model Features

Unity Environment Integration
Designed specifically for Unity ML-Agents pyramid environment, it can run directly in a 3D virtual environment
PPO Algorithm
Employs the Proximal Policy Optimization algorithm to balance exploration and exploitation for stable learning
Real-time Visualization
Supports real-time viewing of the agent's performance in the environment via a browser

Model Capabilities

3D Environment Navigation
Obstacle Avoidance
Goal-Oriented Behavior
Reinforcement Learning Decision Making

Use Cases

Game AI
Automatic Pathfinding
The agent can autonomously find paths in complex 3D environments
Task Solving
Capable of completing specific tasks in the pyramid environment
Educational Demonstration
Reinforcement Learning Teaching
Can serve as a teaching case for reinforcement learning algorithms
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