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Testpyramidsrnd2

Developed by micheljperez
This is a PPO agent model trained using the Unity ML-Agents library, specifically designed to run the Pyramid game.
Object Detection
TensorBoard
#Unity Game AI#Deep Reinforcement Learning#3D Environment Training
Downloads 16
Release Time : 7/17/2022

Model Overview

This model is a deep reinforcement learning agent based on the PPO algorithm, trained to operate in the Pyramid game within Unity's ML-Agents environment.

Model Features

Based on PPO Algorithm
Trained using the Proximal Policy Optimization algorithm, an advanced reinforcement learning method.
Unity ML-Agents Integration
Developed specifically for the Unity ML-Agents environment, allowing seamless integration into Unity game projects.
Optimized for Pyramid Game
Trained and optimized for the Pyramid game scenario, effectively handling the game's environment and tasks.

Model Capabilities

Game Control
Reinforcement Learning Decision Making
Environment Perception
Action Execution

Use Cases

Game AI
Pyramid Game AI Player
Acts as an AI player for the Pyramid game, autonomously completing game tasks.
Capable of effectively achieving the game's objectives
Reinforcement Learning Research
PPO Algorithm Demonstration
Serves as a case study for applying the PPO algorithm in a gaming environment.
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