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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
36 lines
1.4 KiB
ReStructuredText
36 lines
1.4 KiB
ReStructuredText
Double DQN
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==========
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**Actions space:** Discrete
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**References:** `Deep Reinforcement Learning with Double Q-learning <https://arxiv.org/abs/1509.06461.pdf>`_
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Network Structure
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-----------------
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.. image:: /_static/img/design_imgs/dqn.png
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:align: center
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Algorithm Description
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---------------------
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Training the network
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++++++++++++++++++++
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1. Sample a batch of transitions from the replay buffer.
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2. Using the next states from the sampled batch, run the online network in order to find the $Q$ maximizing
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action :math:`argmax_a Q(s_{t+1},a)`. For these actions, use the corresponding next states and run the target
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network to calculate :math:`Q(s_{t+1},argmax_a Q(s_{t+1},a))`.
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3. In order to zero out the updates for the actions that were not played (resulting from zeroing the MSE loss),
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use the current states from the sampled batch, and run the online network to get the current Q values predictions.
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Set those values as the targets for the actions that were not actually played.
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4. For each action that was played, use the following equation for calculating the targets of the network:
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:math:`y_t=r(s_t,a_t )+\gamma \cdot Q(s_{t+1},argmax_a Q(s_{t+1},a))`
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5. Finally, train the online network using the current states as inputs, and with the aforementioned targets.
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6. Once in every few thousand steps, copy the weights from the online network to the target network.
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