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* initial ACER commit * Code cleanup + several fixes * Q-retrace bug fix + small clean-ups * added documentation for acer * ACER benchmarks * update benchmarks table * Add nightly running of golden and trace tests. (#202) Resolves #200 * comment out nightly trace tests until values reset. * remove redundant observe ignore (#168) * ensure nightly test env containers exist. (#205) Also bump integration test timeout * wxPython removal (#207) Replacing wxPython with Python's Tkinter. Also removing the option to choose multiple files as it is unused and causes errors, and fixing the load file/directory spinner. * Create CONTRIBUTING.md (#210) * Create CONTRIBUTING.md. Resolves #188 * run nightly golden tests sequentially. (#217) Should reduce resource requirements and potential CPU contention but increases overall execution time. * tests: added new setup configuration + test args (#211) - added utils for future tests and conftest - added test args * new docs build * golden test update
38 lines
1.4 KiB
ReStructuredText
38 lines
1.4 KiB
ReStructuredText
Deep Q Networks
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===============
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**Actions space:** Discrete
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**References:** `Playing Atari with Deep Reinforcement Learning <https://www.cs.toronto.edu/~vmnih/docs/dqn.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 target network to calculate the :math:`Q` values for each of
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the actions :math:`Q(s_{t+1},a)`, and keep only the maximum value for each state.
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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 max_a Q(s_{t+1})`
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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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.. autoclass:: rl_coach.agents.dqn_agent.DQNAlgorithmParameters
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