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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
1.2 KiB
ReStructuredText
36 lines
1.2 KiB
ReStructuredText
N-Step Q Learning
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=================
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**Actions space:** Discrete
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**References:** `Asynchronous Methods for Deep Reinforcement Learning <https://arxiv.org/abs/1602.01783>`_
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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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The :math:`N`-step Q learning algorithm works in similar manner to DQN except for the following changes:
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1. No replay buffer is used. Instead of sampling random batches of transitions, the network is trained every
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:math:`N` steps using the latest :math:`N` steps played by the agent.
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2. In order to stabilize the learning, multiple workers work together to update the network.
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This creates the same effect as uncorrelating the samples used for training.
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3. Instead of using single-step Q targets for the network, the rewards from $N$ consequent steps are accumulated
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to form the :math:`N`-step Q targets, according to the following equation:
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:math:`R(s_t, a_t) = \sum_{i=t}^{i=t + k - 1} \gamma^{i-t}r_i +\gamma^{k} V(s_{t+k})`
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where :math:`k` is :math:`T_{max} - State\_Index` for each state in the batch
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.. autoclass:: rl_coach.agents.n_step_q_agent.NStepQAlgorithmParameters
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