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Testpyramidsrnd
T
Testpyramidsrnd
Developed by micheljperez
This is a PPO algorithm agent model trained using the Unity ML-Agents library, specifically designed for gaming in a pyramid environment.
Downloads 18
Release Time : 7/17/2022
Model Overview
The model is based on the PPO (Proximal Policy Optimization) algorithm and trained under Unity's ML-Agents framework to solve navigation and task completion problems in the pyramid environment.
Model Features
Based on PPO Algorithm
Trained using the Proximal Policy Optimization algorithm, an advanced reinforcement learning algorithm.
Unity ML-Agents Integration
Fully compatible with the Unity ML-Agents framework, making it easy to deploy and use in Unity environments.
Specialized for Pyramid Environment
Specifically trained for the pyramid environment, effectively solving navigation and task completion problems in this setting.
Model Capabilities
Pyramid Environment Navigation
Task Completion
Reinforcement Learning Decision Making
Use Cases
Game AI
Pyramid Environment Navigation
The agent can autonomously navigate the pyramid environment to find targets or complete tasks.
Capable of effectively completing specified tasks in the pyramid environment
Reinforcement Learning Research
PPO Algorithm Validation
Can be used to validate the performance of the PPO algorithm in 3D environments.
Demonstrates the effectiveness of the PPO algorithm in complex 3D environments
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