AIbase
EN
Home
Mlagents PushBlock
M

Mlagents PushBlock

Developed by kingabzpro
This is a PPO agent model trained using the Unity ML-Agents library, specifically designed for the PushBlock game environment.
Molecular Model
TensorBoard
#Unity Reinforcement Learning#Push Block Game AI#PPO Algorithm
Downloads 53
Release Time : 7/4/2022

Model Overview

This model is a reinforcement learning agent based on the PPO (Proximal Policy Optimization) algorithm, trained to perform block-pushing tasks in Unity's PushBlock environment.

Model Features

Based on PPO Algorithm
Trained using the Proximal Policy Optimization algorithm, an advanced reinforcement learning method.
Unity Environment Integration
Designed for Unity's ML-Agents framework, allowing direct deployment and execution in Unity environments.
Visual Demonstration
Supports viewing the agent's performance directly in a browser via Hugging Face Spaces.

Model Capabilities

Block-Pushing Task Execution
Reinforcement Learning Decision-Making
Unity Environment Interaction

Use Cases

Game AI
PushBlock Game AI
Acts as an intelligent agent in the PushBlock game, capable of learning and executing block-pushing tasks.
The trained agent can effectively achieve the goal of pushing blocks.
Reinforcement Learning Research
PPO Algorithm Application
Can serve as a case study for researching the performance of the PPO algorithm in Unity environments.
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
English简体中文繁體中文にほんご
© 2025AIbase