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Testpyramidsrnd

Developed by croumegous
This is a reinforcement learning agent based on the PPO algorithm, specifically trained for the Unity ML-Agents pyramid environment
Physics Model
TensorBoard
#Unity Reinforcement Learning#3D Environment Navigation#PPO Algorithm Optimization
Downloads 23
Release Time : 7/10/2022

Model Overview

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

Model Features

Unity Environment Integration
Designed specifically for the Unity ML-Agents pyramid environment, it can be seamlessly integrated into Unity projects
PPO Algorithm Implementation
Uses the Proximal Policy Optimization algorithm to balance exploration and exploitation, achieving stable policy learning
3D Navigation Capability
Capable of navigation and task-solving in complex 3D environments

Model Capabilities

3D Environment Navigation
Obstacle Avoidance
Goal-Oriented Behavior
Reinforcement Learning Policy Execution

Use Cases

Game AI
NPC Intelligent Navigation
Provides intelligent navigation capabilities for NPC characters in games
NPCs can autonomously move in complex 3D environments
Training Environment Testing
Used to test and validate the effectiveness of ML-Agents training environments
Validates the trainability of the pyramid environment and agent performance
Educational Demonstration
Reinforcement Learning Teaching
Serves as a teaching example for the PPO algorithm
Visually demonstrates the application of reinforcement learning in 3D environments
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