refactor: change the file layout of omnisafe #35
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Gaiejj
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friedmainfunction
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| self.lagrangian_multiplier = 2.0 | ||
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| def compute_loss_pi(self, data: dict): | ||
| # Policy loss |
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| # Policy loss | |
| """compute loss for policy""" |
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| """ | ||
| Update actor, critic, running statistics | ||
| """ |
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| """ | |
| Update actor, critic, running statistics | |
| """ | |
| """Update actor, critic, running statistics""" |
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| """ | ||
| Pre-process data, e.g. standardize observations, rescale rewards if | ||
| enabled by arguments. | ||
| """ |
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| """ | |
| Pre-process data, e.g. standardize observations, rescale rewards if | |
| enabled by arguments. | |
| """ | |
| """Pre-process data, e.g. standardize observations, rescale rewards if enabled by arguments.""" |
Gaiejj
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docs/source/BaseRL/TRPO.rst
Outdated
| @@ -313,7 +313,7 @@ TRPO describes an approximate policy iteration scheme based on the policy improv | |||
| Note that for now, we assume exact evaluation of the advantage values :math:`A^R_{\pi}`. | |||
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| It follows from Equation :ref:`(11) <trpo-eq-11>` that TRPO is guaranteed to generate a monotonically improving sequence of policies :math:`J\left(\pi_0\right) \leq J\left(\pi_1\right) \leq J\left(\pi_2\right) \leq \cdots`. | |||
| To see this, let :math:`M_i(\pi)=L_{\pi_i}(\pi)-C D_{\mathrm{KL}}^{\max }\left(\pi_i, \pi\right)`. | |||
| To see this, let :math:`M_i(\pi)=L_{\pi_i}(\pi)-C D_{\mathrm{}}^{\max }\left(\pi_i, \pi\right)`. | |||
Gaiejj
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