On Proximal Policy Optimization's Heavy-tailed Gradients
On Proximal Policy Optimization's Heavy-tailed Gradients
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Modern policy gradient algorithms such as Proximal Policy Optimization (PPO) rely on an arsenal of heuristics, including loss clipping and gradient clipping, to ensure successful learning. These heuristics are reminiscent of techniques from robust statistics, commonly used for estimation in outlier-rich (``heavy-tailed'') regimes. In this paper, we...
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On Proximal Policy Optimization's Heavy-tailed Gradients
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TN_cdi_proquest_journals_2492481799
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2492481799
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2331-8422
