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Ball

Developed by ThomasSimonini
This is a reinforcement learning agent trained with the PPO algorithm, designed to control the balancing ball task in the Unity 3DBall game.
3D Vision
TensorBoard
#Deep Reinforcement Learning#Unity3D Control#Continuous Action Space
Downloads 23
Release Time : 4/19/2022

Model Overview

This model is trained using the Unity ML-Agents framework with the PPO algorithm to learn how to balance a sphere in a 3D environment. Suitable for reinforcement learning education and game AI development.

Model Features

Based on PPO Algorithm
Utilizes the Proximal Policy Optimization algorithm for stable policy optimization
Unity Environment Integration
Designed specifically for the Unity 3DBall game environment and can be directly deployed in Unity
Configurable Network Architecture
Supports customization of neural network layers and hidden unit count

Model Capabilities

Balance control in 3D environment
Real-time decision making
Reinforcement learning policy optimization

Use Cases

Game AI
3DBall Game AI
Acts as an AI opponent or demo character in the 3DBall game
Capable of stably controlling the ball's balance
Educational Demonstration
Reinforcement Learning Teaching
Demonstrates the application of the PPO algorithm in continuous control tasks
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