Algorithm Introduction
==========================

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            <button onclick="switchLanguage('zh')">中文</button>
            <button onclick="switchLanguage('en')">English</button>
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      <div style="background-color: #FDF5E6; padding: 15px; border-radius: 5px; border: 1px solid #EEC591;">
            This project provides <b>3 types of environment implementations, and 3 baseline algorithms</b> for users to design algorithms and compare their performance.
            For detailed documentation on environment classes, see: <a href="Environment.html">Environment Wrapping</a>; for detailed documentation on algorithm classes, see: <a href="Model.html">Model Construction</a>
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Quick Start (Running baseline algorithms)
-----------------------------------------------

Baseline1: :code:`BenchEnv_Multi` environment +  :code:`A3CLSTM-E2E` algorithm 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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    <div style="background-color: #e5f1fe; padding: 15px; border-radius: 5px; border: 1px solid #1874CD;">
        Environment code is located at <code>Alg_Base/DAT_Benchmark/envs/environment.py</code>
        Algorithm code is located at <code>Alg_Base/DAT_Benchmark/models/A3CLSTM_E2E/</code>
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You can run the following code to quickly start the algorithm:

.. code:: bash

    cd Alg_Base/DAT_Benchmark/
    # Test mode
    # Test using Cumulative Reward (CR)
    python ./models/A3CLSTM_E2E/main.py --Mode 0 --Scene "citystreet" --Weather "day" --delay 20 --Test_Param "CityStreet-d" --Test_Mode AR
    # Test using Tracking Success Rate (TSR)
    python ./models/A3CLSTM_E2E/main.py --Mode 0 --Scene "citystreet" --Weather "day" --delay 20 --Test_Param "CityStreet-d" --Test_Mode TSR
    # New training mode
    python ./models/A3CLSTM_E2E/main.py --Mode 1 --workers 35 --Scene "citystreet" --Weather "day" --delay 20 --Freq 125 --New_Train
    # Resuming training mode
    python ./models/A3CLSTM_E2E/main.py --Mode 1 --workers 35 --Scene "citystreet" --Weather "day" --delay 20 --Freq 125

Baseline2: :code:`UAV_VAT_Gymnasium` environment +  :code:`D-VAT` algorithm
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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    <div style="background-color: #e5f1fe; padding: 15px; border-radius: 5px; border: 1px solid #1874CD;">
        Environment code can be found at <code>Alg_Base/DAT_Benchmark/models/D_VAT/DVAT_envs.py</code>
        Algorithm code can be found at <code>Alg_Base/DAT_Benchmark/models/D_VAT/</code>
    </div>
    </br>

You can run the following code to quickly start the algorithm:

.. code:: bash

    cd Alg_Base/DAT_Benchmark/
    # Test mode
    # Test using Cumulative Reward (CR)
    python ./models/D_VAT/DVAT_main.py -w 1 -m citystreet-day.wbt --train_mode 0 --Test_Mode CR
    # Test using Tracking Success Rate (TSR)
    python ./models/D_VAT/DVAT_main.py -w 1 -m citystreet-day.wbt --train_mode 0 --Test_Mode TSR
    # New training mode
    python ./models/D_VAT/DVAT_main.py -w 35 -m citystreet-day.wbt --train_mode 1 --New_Train
    # Resume training mode
    python ./models/D_VAT/DVAT_main.py -w 35 -m citystreet-day.wbt --train_mode 1

Baseline3(Ours): :code:`Envs` Environment +  :code:`R-VAT` Algorithm
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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    <div style="background-color: #e5f1fe; padding: 15px; border-radius: 5px; border: 1px solid #1874CD;">
        Environment code can be found at <code>Alg_Base/DAT_Benchmark/envs/envs_parallel.py</code>
        Algorithm code can be found at <code>Alg_Base/DAT_Benchmark/models/R_VAT/</code>
    </div>
    </br>


You can run the following code to quickly start the algorithm:

.. code:: bash

    cd Alg_Base/DAT_Benchmark/
    # Test mode
    # Test using Cumulative Reward (CR)
    python ./models/R_VAT/RVAT.py -w 1 -m citystreet-day.wbt --train_mode 0 --Test_Mode AR
    # Test using Tracking Success Rate (TSR)
    python ./models/R_VAT/RVAT.py -w 1 -m citystreet-day.wbt --train_mode 0 --Test_Mode TSR
    # New training mode
    python ./models/R_VAT/RVAT.py -w 35 -m citystreet-day.wbt --train_mode 1 --New_Train
    # Resume training mode
    python ./models/R_VAT/RVAT.py -w 35 -m citystreet-day.wbt --train_mode 1