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Unitypyramidsrnd
This is a reinforcement learning agent based on the PPO algorithm, specifically trained for Unity's ML-Agents pyramid environment.
Downloads 15
Release Time : 7/24/2022
Model Overview
The model is trained using the PPO (Proximal Policy Optimization) algorithm in Unity's ML-Agents pyramid environment and can perform specific tasks in this environment.
Model Features
Unity Environment Integration
Designed specifically for Unity ML-Agents pyramid environment, it can be directly deployed and used in Unity.
PPO Algorithm
Uses the Proximal Policy Optimization algorithm to balance exploration and exploitation for stable policy learning.
Visual Demonstration
Supports direct viewing of the agent's performance in the environment through Hugging Face Spaces.
Model Capabilities
Environment Navigation
Task Execution
Reinforcement Learning Decision-Making
Use Cases
Game AI
Pyramid Environment Navigation
The agent finds paths and completes tasks in the pyramid maze.
The agent's decision-making process in the environment can be observed.
Reinforcement Learning Research
PPO Algorithm Demonstration
Serves as a case study for the application of the PPO algorithm in 3D environments.
Can be used for teaching or algorithm comparison.
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