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Ball Test

Developed by osanseviero
A reinforcement learning agent based on the PPO algorithm, designed to control the balancing ball task in the Unity 3DBall environment
3D Vision
TensorBoard
#Deep Reinforcement Learning#Unity Environment Control#3D Balance Control
Downloads 29
Release Time : 4/19/2022

Model Overview

This model is trained using the Unity ML-Agents framework and employs the PPO algorithm to learn control strategies for balancing a ball in a 3D environment. Suitable for reinforcement learning research and robotic control applications.

Model Features

Based on PPO Algorithm
Utilizes the Proximal Policy Optimization algorithm to achieve stable policy learning in continuous action spaces
Multi-layer Perceptron Architecture
Employs a neural network structure with 2 layers of 128 units to process environmental observations
Linear Learning Rate Scheduling
Uses a linear learning rate scheduling strategy to optimize the training process

Model Capabilities

Balance control in 3D environment
Decision-making in continuous action spaces
Reinforcement learning policy optimization

Use Cases

Educational Research
Reinforcement Learning Teaching Example
Serves as a standard teaching case for the PPO algorithm
Helps understand the application of reinforcement learning in continuous control problems
Robotic Control
Balance Control System
Can be transferred to actual robotic balance control tasks
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