/ML-Tracker

A simple but powerful tool which tracks ML-Agent training sessions and clients.

ML-Tracker

ML-Tracker is a project for creating an app that keeps track and progress of ML-Agents training sessions across your local machine and networked machines. ML-Agents is a powerful framework for creating intelligent agents in games and simulations using reinforcement learning, imitation learning, and other methods. ML-Tracker allows you to monitor and manage multiple ML-Agents training sessions and their outputs from a single interface.

Features

  • Monitor and manage multiple ML-Agents training sessions and their outputs from a single interface
  • Connect and communicate with other ML-Trackers in a swarm or group or cluster
  • Share and synchronize data and metrics among ML-Trackers
  • Control and configure ML-Agents training sessions remotely
  • Compare and evaluate ML-Agents results across different machines and environments

Installation

To use ML-Tracker, you need to have the following installed:

  • [Microsoft Edge] browser
  • [Unity Hub] and [Unity Editor] with ML-Agents package
  • [Python] and [mlagents] package
  • [ML-Explorer] project

To install ML-Tracker, you can clone this repository or download the zip file. Then, open the ML-Tracker folder in your preferred code editor.

Usage

To use ML-Tracker, you need to follow these steps:

  1. Launch the Microsoft Edge browser and open the ML-Tracker web page.
  2. Launch the Unity Hub and open the ML-Agents project that you want to track.
  3. Launch the Python terminal and run the mlagents-learn command with the appropriate configuration file and environment name.
  4. On the ML-Tracker web page, enter the name of your training session and the port number that you are using for communication between Unity and Python.
  5. Click on the "Start Tracking" button and watch the graphs and charts update in real time as your ML-Agents train.
  6. To connect with other ML-Trackers, click on the "Join Swarm" button and enter the IP address and port number of the ML-Tracker that you want to join.
  7. To share and synchronize data and metrics with other ML-Trackers, click on the "Sync Data" button and select the ML-Trackers that you want to sync with.
  8. To control and configure ML-Agents training sessions remotely, click on the "Remote Control" button and select the ML-Tracker that you want to control.
  9. To compare and evaluate ML-Agents results across different machines and environments, click on the "Compare Results" button and select the ML-Trackers that you want to compare with.
  10. To stop tracking, click on the "Stop Tracking" button and close the Unity and Python terminals.

License

ML-Tracker is licensed under the [MIT License].