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* updating the documentation website * adding the built docs * update of api docstrings across coach and tutorials 0-2 * added some missing api documentation * New Sphinx based documentation
34 lines
1.2 KiB
ReStructuredText
34 lines
1.2 KiB
ReStructuredText
Normalized Advantage Functions
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==============================
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**Actions space:** Continuous
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**References:** `Continuous Deep Q-Learning with Model-based Acceleration <https://arxiv.org/abs/1603.00748.pdf>`_
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Network Structure
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-----------------
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.. image:: /_static/img/design_imgs/naf.png
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:width: 600px
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:align: center
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Algorithm Description
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---------------------
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Choosing an action
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++++++++++++++++++
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The current state is used as an input to the network. The action mean :math:`\mu(s_t )` is extracted from the output head.
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It is then passed to the exploration policy which adds noise in order to encourage exploration.
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Training the network
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++++++++++++++++++++
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The network is trained by using the following targets:
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:math:`y_t=r(s_t,a_t )+\gamma\cdot V(s_{t+1})`
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Use the next states as the inputs to the target network and extract the :math:`V` value, from within the head,
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to get :math:`V(s_{t+1} )`. Then, update the online network using the current states and actions as inputs,
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and :math:`y_t` as the targets.
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After every training step, use a soft update in order to copy the weights from the online network to the target network.
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.. autoclass:: rl_coach.agents.naf_agent.NAFAlgorithmParameters
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