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OA: Cluster analysis - updates, corrections, improvements (9)
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doc/rtd/content/03_machine_learning/mlpro_gt/sub/gettingstarted/02_dg.rst
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MLPro-GT-DG - Dynamic Games | ||
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Here is a concise series to introduce all users to the MLPro-GT-DG in a practical way, whether you are a first-timer or an experienced MLPro user. | ||
Here is a concise series designed to practically introduce all users to MLPro-GT-DG, whether you are new to it or an experienced MLPro user. | ||
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If you are a first-timer, then you can begin with **Section (1) What is MLPro?**. | ||
No experience with MLPro? To learn more about MLPro, please refer to the :ref:`Getting Started page of MLPro <target_mlpro_getstarted>`. | ||
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If you have understood MLPro but not the game theoretical approach in the engineering field, then you can jump to **Section (2) What is Game Theory?**. | ||
By following the step-by-step guidelines below, we expect you to gain a practical understanding of MLPro-GT and begin using MLPro-GT-DG. | ||
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If you have experience in both MLPro and game theory, then you can directly start with **Section (3) What is MLPro-GT?**. | ||
**1. What are Game Theory and Dynamic Games?** | ||
Game Theory is a field of mathematics that studies strategic interactions where the outcome for each participant depends on the choices of all involved. | ||
It models situations where individuals or entities make decisions to maximize their own payoffs, considering the potential responses of others. | ||
Dynamic Games extend this concept to situations where decisions are made sequentially over time, with each player’s strategy evolving based on previous actions and outcomes. | ||
These games often involve strategies that adapt as the game progresses, reflecting changing conditions and information. | ||
The study of dynamic games helps understand complex interactions in fields like economics, politics, and biology, where decisions are interdependent and evolve over time. | ||
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For a deeper understanding, we recommend reading the book by Dario Bauso, titled: `Game Theory with Engineering Applications <https://dl.acm.org/doi/10.5555/2948750>`_. | ||
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After following the below step-by-step guideline, we expect the user understands the MLPro-GT in practice and starts using MLPro-GT-DG. | ||
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**1. What is MLPro?** | ||
If you are a first-time user of MLPro, you might wonder what is MLPro. | ||
Therefore, we recommend initially starting with understanding MLPro by checking out the following steps: | ||
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(a) :ref:`MLPro: An Introduction <target_mlpro_introduction>` | ||
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(b) `introduction video of MLPro <https://ars.els-cdn.com/content/image/1-s2.0-S2665963822001051-mmc1.mp4>`_ | ||
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(c) :ref:`installing and getting started with MLPro <target_mlpro_getstarted>` | ||
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(d) `MLPro paper in Software Impact journal <https://doi.org/10.1016/j.simpa.2022.100421>`_ | ||
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**2. What is Game Theory?** | ||
If you have not dealt with game theory for engineering applications, we recommend starting to understand at least the basic concept of game theory. | ||
There are plenty of references, articles, papers, books, or videos on the internet that explains the game theory. | ||
But, for deep understanding, we recommend you to read the book from Dario Bauso, which is `Game Theory with Engineering Applications <https://dl.acm.org/doi/10.5555/2948750>`_. | ||
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**3. What is MLPro-GT?** | ||
We expect that you have a basic knowledge of MLPro and game theory. | ||
Therefore, you need to understand the overview of MLPro-GT by following the steps below: | ||
**2. What is MLPro-GT?** | ||
We assume you have a basic understanding of MLPro and game theory. | ||
Therefore, you should familiarize yourself with the overview of MLPro-GT by following these steps: | ||
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(a) :ref:`MLPro-GT introduction page <target_overview_GT>` | ||
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(b) `Section 5 of MLPro 1.0 paper <https://doi.org/10.1016/j.mlwa.2022.100341>`_ | ||
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**4. Understanding Game Board and Player in MLPro-GT-DG** | ||
First of all, it is important to understand the structure of a game board in MLPro-GT, which can be found on :ref:`this page <target_gb_gt>`. | ||
**3. Understanding Game Board and Player in MLPro-GT-DG** | ||
Firstly, it is important to understand the structure of a game board in MLPro-GT, which can be found on :ref:`this page <target_gb_gt>`. | ||
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In reinforcement learning, we have two types of agents, such as a single-agent RL or a multi-agent RL. Both of the types are covered by MLPro-RL. | ||
Meanwhile, in MLPro-GT, we focus on a multi-player GT because there are no significant advantages of using game theory for single-player. | ||
To understand a player in MLPro-GT, you can visit :ref:`this page <target_players_GT>`. | ||
MLPro-GT focuses on multi-player game theory, as game theory offers no significant advantages for single-player scenarios. | ||
To understand the concept of a player in MLPro-GT, you can visit :ref:`this page <target_players_GT>`. | ||
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Then, you can start following some of our howto files and a sample application that shows how to run and train multi-player with their own policy, as follows: | ||
Next, you can refer to our how-to files and a sample application that demonstrate how to run and train multi-player scenarios with their own policies, as outlined below: | ||
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(a) :ref:`Howto GT-001: Run Multi-Player with Own Policy <Howto GT 001>` | ||
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(b) :ref:`Howto GT-002: Train Multi-Player <Howto GT 002>` | ||
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(c) `Section 6.2 of MLPro 1.0 paper <https://doi.org/10.1016/j.mlwa.2022.100341>`_ | ||
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**5. Additional Guidance** | ||
After following the previous steps, we hope that you could practice MLPro-GT and start using this subpackage for your GT-related activities. | ||
For more advanced features, we highly recommend you to check out the following howto files: | ||
**4. Additional Guidance** | ||
After completing the previous steps, we hope you will be able to practice with MLPro-GT and begin using this subpackage for your game theory-related activities. | ||
For more advanced features, we strongly recommend reviewing the following how-to files: | ||
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(a) :ref:`Howto RL-HT-001: Hyperopt <Howto RL HT 001>` | ||
(a) `Howto RL-HT-001: Hyperparameter Tuning using Hyperopt <https://mlpro-int-hyperopt.readthedocs.io/en/latest/content/01_examples_pool/howto.rl.ht.001.html>`_ | ||
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(b) :ref:`Howto RL-HT-002: Optuna <Howto RL HT 002>` | ||
(b) `Howto RL-HT-001: Hyperparameter Tuning using Optuna <https://mlpro-int-optuna.readthedocs.io/en/latest/content/01_examples_pool/howto.rl.ht.002.html>`_ | ||
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(c) :ref:`Howto RL-ATT-001: Stagnation Detection <Howto RL ATT 001>` | ||
(c) `Howto RL-ATT-001: Train and Reload Single Agent using Stagnation Detection (Gymnasium) <https://mlpro-int-sb3.readthedocs.io/en/latest/content/01_example_pool/03_howtos_att/howto_rl_att_001_train_and_reload_single_agent_gym_sd.html>`_ |
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...nt/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/01_overview.rst
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.. _target_oa_stream_overview: | ||
Overview | ||
======== | ||
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Combines the functionalities of MLPro's stream task and ML model... | ||
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**Learn more** | ||
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.. toctree:: | ||
:maxdepth: 2 | ||
:glob: | ||
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01_overview/* | ||
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**Cross Reference** | ||
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- BF: Stream Processing | ||
- Howto section | ||
- API | ||
- MLPro-Int-River |
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...ning/mlpro_oa/sub/layer0_oa_stream_processing/01_overview/01_oa_stream_task.rst
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.. _target_oa_stream_tasks: | ||
Online Adaptive Stream Tasks | ||
============================ | ||
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... | ||
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.../mlpro_oa/sub/layer0_oa_stream_processing/01_overview/02_oa_stream_workflow.rst
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.. _target_oa_stream_workflows: | ||
Online Adaptive Stream Workflows | ||
================================ | ||
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... |
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...e_learning/mlpro_oa/sub/layer0_oa_stream_processing/02_oa_boundary_detector.rst
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.. _target_oa_boundary_detector: | ||
Boundary Detection | ||
================== | ||
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Further descriptions coming soon... | ||
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**Cross Reference** | ||
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- Related Howtos | ||
- API |
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...chine_learning/mlpro_oa/sub/layer0_oa_stream_processing/03_oa_normalization.rst
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.. _target_oa_normalizers: | ||
Normalization | ||
============= | ||
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Further descriptions coming soon... | ||
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**Learn more** | ||
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.. toctree:: | ||
:maxdepth: 2 | ||
:glob: | ||
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03_oa_normalization/* |
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...g/mlpro_oa/sub/layer0_oa_stream_processing/03_oa_normalization/01_oa_minmax.rst
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.. _target_oa_norm_minmax: | ||
MinMax-Normalization | ||
==================== | ||
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Further descriptions coming soon... | ||
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**Cross Reference** | ||
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- BF-Math: MinMax-Normalization | ||
- Howtos |
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...mlpro_oa/sub/layer0_oa_stream_processing/03_oa_normalization/02_oa_zscaling.rst
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.. _target_oa_norm_ztrans: | ||
Z-Scaling | ||
========= | ||
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Further descriptions coming soon... | ||
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**Cross Reference** | ||
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- BF-Math: Z-Scaling | ||
- Howtos |
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...chine_learning/mlpro_oa/sub/layer0_oa_stream_processing/10_cluster_analysis.rst
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.. _target_oa_cluster_analyzer: | ||
Cluster Analysis | ||
================ | ||
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Further descriptions coming soon... | ||
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**Learn more** | ||
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.. toctree:: | ||
:maxdepth: 2 | ||
:glob: | ||
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10_cluster_analysis/* | ||
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**Cross Reference** | ||
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- Selected open access papers | ||
- Howtos | ||
- API | ||
- MLPro-OA-River |
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...hine_learning/mlpro_oa/sub/layer0_oa_stream_processing/20_anomaly_detection.rst
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.. _target_oa_anomaly_detection: | ||
Anomaly Detection | ||
================= | ||
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Further descriptions coming soon... | ||
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**Learn more** | ||
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.. toctree:: | ||
:maxdepth: 2 | ||
:glob: | ||
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20_anomaly_detection/* | ||
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**Cross Reference** | ||
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- Selected open access papers | ||
- Howtos | ||
- API | ||
- MLPro-OA-River |
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...rning/mlpro_oa/sub/layer0_oa_stream_processing/20_anomaly_detection/01_cbad.rst
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.. _target_oa_cbad: | ||
Cluster-based Anomaly Detection | ||
=============================== | ||
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Further descriptions coming soon... | ||
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- Motivation, potential, advantages | ||
- New types of anomalies | ||
- Special dependencies to cluster algorithms | ||
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**Cross Reference** | ||
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- Howtos | ||
- API |
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...ine_learning/mlpro_oa/sub/layer0_oa_stream_processing/30_anomaly_prediction.rst
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.. _target_oa_anomaly_prediction: | ||
Anomaly Prediction | ||
================== | ||
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Further descriptions coming soon... | ||
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**Learn more** | ||
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.. toctree:: | ||
:maxdepth: 2 | ||
:glob: | ||
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20_anomaly_prediction/* | ||
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**Cross Reference** | ||
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- Selected open access papers | ||
- Howtos | ||
- API | ||
- MLPro-OA-River |
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