Methods › Reinforcement Learning › Policy Gradient Methods › A3C › Papers where code ran, page 1

A3C

Papers archive 2025-07-28

archive papers tagged: 57 · with a code link: 20 · where Syntology ran a sample: 4 (3 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 (4 of 57 tagged: 3 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 4 of the 4 tagged papers where Syntology ran at least one harvested sample (3 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.

• Unsupervised Traffic Accident Detection in First-Person Videos
  2 Mar 2019 · 2 repositories · arXiv:1903.00618
  Syntology official (archive's flag): 5 ran · 5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 5 harvested samples)
• Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
  18 Dec 2017 · 12 repositories · arXiv:1712.06567
  Syntology 4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified (of 5 harvested samples) · 4 pointer-only (licence)
• Noisy Networks for Exploration
  30 Jun 2017 · 15 repositories · arXiv:1706.10295
  Syntology 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) · 2 unverified (of 3 harvested samples) · 3 pointer-only (licence)
• Asynchronous Methods for Deep Reinforcement Learning
  4 Feb 2016 · 70 repositories · arXiv:1602.01783
  Syntology 60 ran (of which 20 constructed an object rather than computing a result; 51 with no instrument failure: 2 honoured, 1 violated, 48 with no contract checked; 9 where Syntology's instrument failed) · 35 unverified (of 95 harvested samples) · 20 pointer-only (licence)