Methods › Reinforcement Learning › Q-Learning Networks › DQN › Papers, page 6

Deep Q-Network

DQN

Papers archive 2025-07-28

archive papers tagged: 519 · with a code link: 173 · where Syntology ran a sample: 47 (36 with a run with no instrument failure, 11 where every run was a failure of Syntology's instrument) Syntology

Show: all tagged papersonly where code ran (47 of 519 tagged: 36 with a run with no instrument failure, 11 where every run was a failure of Syntology's instrument)

Page 6 of 6: papers 501 to 519 of 519, newest first by the archive's date (ties by slug), in archive order.

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.

• Deep Reinforcement Learning for Multi-Domain Dialogue Systems
  26 Nov 2016 · 1 repository · arXiv:1611.08675
• Memory Lens: How Much Memory Does an Agent Use?
  21 Nov 2016 · 0 repositories · arXiv:1611.06928
• Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
  7 Nov 2016 · 0 repositories · arXiv:1611.01929
• Deep Reinforcement Learning From Raw Pixels in Doom
  7 Oct 2016 · 0 repositories · arXiv:1610.02164
• Opponent Modeling in Deep Reinforcement Learning
  18 Sep 2016 · 1 repository · arXiv:1609.05559
• Deep Reinforcement Learning Discovers Internal Models
  16 Jun 2016 · 0 repositories · arXiv:1606.05174
• Deep Reinforcement Learning With Macro-Actions
  15 Jun 2016 · 0 repositories · arXiv:1606.04615
• Classifying Options for Deep Reinforcement Learning
  27 Apr 2016 · 0 repositories · arXiv:1604.08153
• Deep Exploration via Bootstrapped DQN
  15 Feb 2016 · 6 repositories · arXiv:1602.04621
  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) · 1 unverified (of 2 harvested samples) · 2 pointer-only (licence)
• How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies
  7 Dec 2015 · 0 repositories · arXiv:1512.02011
• Deep Attention Recurrent Q-Network
  5 Dec 2015 · 3 repositories · arXiv:1512.01693
• State of the Art Control of Atari Games Using Shallow Reinforcement Learning
  4 Dec 2015 · 1 repository · arXiv:1512.01563
• Policy Distillation
  19 Nov 2015 · 1 repository · arXiv:1511.06295
• Prioritized Experience Replay
  18 Nov 2015 · 77 repositories · arXiv:1511.05952
  Syntology 80 ran (of which 62 constructed an object rather than computing a result; 72 with no instrument failure: 4 honoured, 0 violated, 68 with no contract checked; 8 where Syntology's instrument failed) · 31 unverified (of 111 harvested samples) · 43 pointer-only (licence)
• Generating Text with Deep Reinforcement Learning
  30 Oct 2015 · 0 repositories · arXiv:1510.09202
• Deep Reinforcement Learning with Double Q-learning
  22 Sep 2015 · 97 repositories · arXiv:1509.06461
  Syntology 56 ran (of which 38 constructed an object rather than computing a result; 55 with no instrument failure: 0 honoured, 0 violated, 55 with no contract checked; 1 where Syntology's instrument failed) · 50 unverified (of 106 harvested samples) · 57 pointer-only (licence)
• Massively Parallel Methods for Deep Reinforcement Learning
  15 Jul 2015 · 3 repositories · arXiv:1507.04296
• Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning
  1 Dec 2014 · 0 repositories
• Playing Atari with Deep Reinforcement Learning
  19 Dec 2013 · 112 repositories · arXiv:1312.5602
  Syntology 64 ran (of which 24 constructed an object rather than computing a result; 46 with no instrument failure: 5 honoured, 0 violated, 41 with no contract checked; 18 where Syntology's instrument failed) · 53 unverified (of 117 harvested samples) · 56 pointer-only (licence)