Mlagents PushBlock
This is a PPO agent model trained using the Unity ML-Agents library, specifically designed for the PushBlock game environment.
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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.
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