Methods › Reinforcement Learning › Policy Gradient Methods › Soft Actor Critic › Papers where code ran, page 1
Soft Actor Critic
Papers archive 2025-07-28
archive papers tagged: 58 · with a code link: 22 · where Syntology ran a sample: 9 (8 with a run with no instrument failure, 1 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (9 of 58 tagged: 8 with a run with no instrument failure, 1 where every run was a failure of Syntology's instrument)
Syntology We ran code from the paper's repository; we did not isolate this method inside it.
Page 1 of 1: papers 1 to 9 of the 9 tagged papers where Syntology ran at least one harvested sample (8 with a run with no instrument failure, 1 where every run was a failure of Syntology's instrument), newest first by the archive's date (ties by arXiv id). This is a filter on Syntology's record ordered by date only, not a ranking; a run is not a correctness claim. A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.
Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code, as “N ran (of which C constructed an object rather than computing a result; K with no instrument failure: H honoured, V violated, P with no contract checked; I where Syntology's instrument failed) · U unverified”; the instrument figure counts failures of Syntology's instrument, not of the code. It is per sample and not a correctness claim. When the archive marks a repository official for the paper, the line starts with that repository's state (the archive's flag, not a verdict on who wrote the code; “community repositories only” when every sample that ran came from a community repository, “official: no sample here; runs from other or unrecorded repositories” when some came from a repository the paper names or has in its text, or from none recorded); hover it for the repositories the samples that ran came from.
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Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning 29 Jan 2025 · 1 repository · arXiv:2501.17827Syntology official (archive's flag): 6 ran · 6 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified (of 11 harvested samples)
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Imitation Learning by State-Only Distribution Matching 9 Feb 2022 · 1 repository · arXiv:2202.04332Syntology official (archive's flag): 1 ran · 1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample)
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Experience Replay with Likelihood-free Importance Weights 23 Jun 2020 · 1 repository · arXiv:2006.13169Syntology 2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 2 harvested samples)
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Smooth Exploration for Robotic Reinforcement Learning 12 May 2020 · 4 repositories · arXiv:2005.05719Syntology community repositories only · 1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample)
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Reinforcement Learning with Augmented Data 30 Apr 2020 · 2 repositories · arXiv:2004.14990Syntology 21 ran (of which 8 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 12 where Syntology's instrument failed) · 3 unverified (of 24 harvested samples) · 21 pointer-only (licence)
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SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement Learning 28 Dec 2019 · 1 repository · arXiv:1912.12482Syntology official (archive's flag): 10 ran · 10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified (of 13 harvested samples)
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Improving Exploration in Soft-Actor-Critic with Normalizing Flows Policies 6 Jun 2019 · 1 repository · arXiv:1906.02771Syntology official (archive's flag): 4 ran · 4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified (of 4 harvested samples) · 3 pointer-only (licence)
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Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models 30 May 2018 · 9 repositories · arXiv:1805.12114Syntology community repositories only · 12 ran (of which 5 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 7 unverified (of 19 harvested samples) · 17 pointer-only (licence)
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Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 · 86 repositories · arXiv:1801.01290Syntology community repositories only · 91 ran (of which 61 constructed an object rather than computing a result; 73 with no instrument failure: 3 honoured, 1 violated, 69 with no contract checked; 18 where Syntology's instrument failed) · 57 unverified (of 148 harvested samples) · 66 pointer-only (licence)