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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
44 lines
1.9 KiB
ReStructuredText
44 lines
1.9 KiB
ReStructuredText
Clipped Proximal Policy Optimization
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====================================
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**Actions space:** Discrete | Continuous
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**References:** `Proximal Policy Optimization Algorithms <https://arxiv.org/pdf/1707.06347.pdf>`_
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Network Structure
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-----------------
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.. image:: /_static/img/design_imgs/ppo.png
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:align: center
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Algorithm Description
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---------------------
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Choosing an action - Continuous action
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++++++++++++++++++++++++++++++++++++++
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Same as in PPO.
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Training the network
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++++++++++++++++++++
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Very similar to PPO, with several small (but very simplifying) changes:
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1. Train both the value and policy networks, simultaneously, by defining a single loss function,
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which is the sum of each of the networks loss functions. Then, back propagate gradients only once from this unified loss function.
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2. The unified network's optimizer is set to Adam (instead of L-BFGS for the value network as in PPO).
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3. Value targets are now also calculated based on the GAE advantages.
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In this method, the :math:`V` values are predicted from the critic network, and then added to the GAE based advantages,
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in order to get a :math:`Q` value for each action. Now, since our critic network is predicting a :math:`V` value for
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each state, setting the :math:`Q` calculated action-values as a target, will on average serve as a :math:`V` state-value target.
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4. Instead of adapting the penalizing KL divergence coefficient used in PPO, the likelihood ratio
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:math:`r_t(\theta) =\frac{\pi_{\theta}(a|s)}{\pi_{\theta_{old}}(a|s)}` is clipped, to achieve a similar effect.
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This is done by defining the policy's loss function to be the minimum between the standard surrogate loss and an epsilon
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clipped surrogate loss:
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:math:`L^{CLIP}(\theta)=E_{t}[min(r_t(\theta)\cdot \hat{A}_t, clip(r_t(\theta), 1-\epsilon, 1+\epsilon) \cdot \hat{A}_t)]`
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.. autoclass:: rl_coach.agents.clipped_ppo_agent.ClippedPPOAlgorithmParameters |