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25 lines
730 B
ReStructuredText
25 lines
730 B
ReStructuredText
Hierarchical Actor Critic
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=========================
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**Actions space:** Continuous
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**References:** `Hierarchical Reinforcement Learning with Hindsight <https://arxiv.org/abs/1805.08180>`_
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Network Structure
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-----------------
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.. image:: /_static/img/design_imgs/ddpg.png
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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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Pass the current states through the actor network, and get an action mean vector :math:`\mu`.
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While in training phase, use a continuous exploration policy, such as the Ornstein-Uhlenbeck process,
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to add exploration noise to the action. When testing, use the mean vector :math:`\mu` as-is.
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Training the network
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++++++++++++++++++++
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