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Mlagents Pyramids
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Mlagents Pyramids

Developed by danielcfho
This is a reinforcement learning agent based on the PPO algorithm, trained in the pyramid environment using Unity ML-Agents.
Physics Model
TensorBoard
#Unity Reinforcement Learning#3D Environment Navigation#PPO Algorithm
Downloads 40
Release Time : 6/26/2022

Model Overview

This model is a deep reinforcement learning agent specifically trained for Unity's ML-Agents pyramid environment, capable of performing specific tasks in that environment.

Model Features

Based on PPO Algorithm
Trained using the Proximal Policy Optimization (PPO) algorithm, an efficient reinforcement learning algorithm.
Unity ML-Agents Integration
Fully compatible with the Unity ML-Agents framework, deployable and operable in Unity environments.
Pyramid Environment Adaptation
Specifically trained for the pyramid 3D environment, capable of effective navigation and task completion in that environment.

Model Capabilities

3D Environment Navigation
Reinforcement Learning Decision-Making
Task Completion

Use Cases

Game AI
3D Game NPC Control
Can be used to control non-player characters (NPCs) in 3D games for navigation in complex environments.
Game Testing Automation
Can be used to automate testing of playability and path design in 3D game environments.
Robot Simulation
Robot Navigation Training
Can serve as a foundational model for training robot navigation algorithms.
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