Methods › Reinforcement Learning › Policy Gradient Methods › A2C › Papers where code ran, page 1
A2C
Papers archive 2025-07-28
archive papers tagged: 82 · with a code link: 27 · where Syntology ran a sample: 12 (11 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 (12 of 82 tagged: 11 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 12 of the 12 tagged papers where Syntology ran at least one harvested sample (11 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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Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments 14 May 2022 · 0 repositories · arXiv:2205.07015Syntology 2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified (of 4 harvested samples) · 1 pointer-only (licence)
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Augmenting Policy Learning with Routines Discovered from a Single Demonstration 23 Dec 2020 · 2 repositories · arXiv:2012.12469Syntology official (archive's flag): 1 ran · 1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample)
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Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking 15 Nov 2020 · 1 repository · arXiv:2011.07537Syntology official (archive's flag): 3 ran · 3 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; 3 where Syntology's instrument failed) · 1 unverified (of 4 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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Addressing Some Limitations of Transformers with Feedback Memory 21 Feb 2020 · 4 repositories · arXiv:2002.09402Syntology official: no sample here; runs from other or unrecorded repositories · 3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified (of 3 harvested samples) · 2 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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QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning 2 Oct 2019 · 1 repository · arXiv:1910.01055Syntology official (archive's flag): 1 ran · 1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample) · 1 pointer-only (licence)
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Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control 11 Mar 2019 · 1 repository · arXiv:1903.04527Syntology official (archive's flag): 7 ran · 7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified (of 9 harvested samples) · 1 pointer-only (licence)
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Representation Learning with Contrastive Predictive Coding 10 Jul 2018 · 28 repositories · arXiv:1807.03748Syntology 35 ran (of which 21 constructed an object rather than computing a result; 30 with no instrument failure: 1 honoured, 0 violated, 29 with no contract checked; 5 where Syntology's instrument failed) · 10 unverified (of 45 harvested samples) · 22 pointer-only (licence)
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An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution 9 Jul 2018 · 24 repositories · arXiv:1807.03247Syntology community repositories only · 5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified (of 5 harvested samples)
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TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning 31 Oct 2017 · 1 repository · arXiv:1710.11417Syntology official (archive's flag): 7 ran · 7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified (of 12 harvested samples) · 1 pointer-only (licence)
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Asynchronous Methods for Deep Reinforcement Learning 4 Feb 2016 · 70 repositories · arXiv:1602.01783Syntology 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)