.. _sphx_glr_auto_examples_gradient-based-onpolicy:

Gradient-based on-policy learning agents
----------------------------------------

The following examples showcase how to use gradient-based Reinforcement Learning
techniques (in particular, Q-learning and Deterministic Policy Gradient) to train a
Model Predictive Controller (MPC) scheme for a simple task in an on-policy fashion.



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    <div class="sphx-glr-thumbnails">

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    <div class="sphx-glr-thumbcontainer" tooltip="This example tries to reproduce the results from the linear MPC numerical experiment in gros_datadriven_2020. We are given an RL environment whose cost function is">

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  .. image:: /auto_examples/gradient-based-onpolicy/images/thumb/sphx_glr_q_learning_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_gradient-based-onpolicy_q_learning.py`

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      <div class="sphx-glr-thumbnail-title">On-policy Q-learning</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example takes inspiration from the linear MPC numerical experiment in gros_datadriven_2020, but applies DPG to it instead of Q-learning. We are given an RL environment whose cost function is">

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  .. image:: /auto_examples/gradient-based-onpolicy/images/thumb/sphx_glr_dpg_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_gradient-based-onpolicy_dpg.py`

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      <div class="sphx-glr-thumbnail-title">On-policy Deterministic Policy Gradient</div>
    </div>


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    </div>


.. toctree::
   :hidden:

   /auto_examples/gradient-based-onpolicy/q_learning
   /auto_examples/gradient-based-onpolicy/dpg