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Testpyramidsrnd

Developed by AdiKompella
This is a reinforcement learning agent model trained using the PPO algorithm in the Unity ML-Agents pyramid environment
Downloads 15
Release Time : 7/13/2022

Model Overview

This model is based on the PPO (Proximal Policy Optimization) algorithm, specifically trained for the Unity ML-Agents pyramid environment, capable of performing specific reinforcement learning tasks in this environment

Model Features

Unity ML-Agents Compatibility
This model is fully compatible with the Unity ML-Agents framework and can be directly deployed and run in the Unity environment
PPO Algorithm Implementation
Trained using the advanced reinforcement learning algorithm Proximal Policy Optimization (PPO)
Specialized for Pyramid Environment
Specifically trained for Unity's pyramid environment, excelling in this setting

Model Capabilities

Environment Navigation
Decision Making
Reinforcement Learning Task Execution

Use Cases

Game AI
Pyramid Environment Navigation
Autonomous navigation and decision-making in Unity's pyramid environment
Capable of completing specific tasks in the pyramid environment
Reinforcement Learning Research
PPO Algorithm Application
Can serve as a case study for the application of the PPO algorithm in Unity environments
Demonstrates the performance of the PPO algorithm in 3D environments
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