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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
33 lines
1.1 KiB
ReStructuredText
33 lines
1.1 KiB
ReStructuredText
Quantile Regression DQN
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=======================
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**Actions space:** Discrete
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**References:** `Distributional Reinforcement Learning with Quantile Regression <https://arxiv.org/abs/1710.10044>`_
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Network Structure
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-----------------
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.. image:: /_static/img/design_imgs/qr_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. First, the next state quantiles are predicted. These are used in order to calculate the targets for the network,
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by following the Bellman equation.
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Next, the current quantile locations for the current states are predicted, sorted, and used for calculating the
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quantile midpoints targets.
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3. The network is trained with the quantile regression loss between the resulting quantile locations and the target
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quantile locations. Only the targets of the actions that were actually taken are updated.
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4. Once in every few thousand steps, weights are copied from the online network to the target network.
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.. autoclass:: rl_coach.agents.qr_dqn_agent.QuantileRegressionDQNAlgorithmParameters |