Methods › Reinforcement Learning › Q-Learning Networks › Ape-X DQN
Ape-X DQN
Introduced by Dan Horgan et al. in Distributed Prioritized Experience Replay
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Ape-X DQN is a variant of a DQN with some components of Rainbow-DQN that utilizes distributed prioritized experience replay through the Ape-X architecture.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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A coevolutionary approach to deep multi-agent reinforcement learning 12 Apr 2021 · 1 repository · arXiv:2104.05610
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Macro action selection with deep reinforcement learning in StarCraft 2 Dec 2018 · 1 repository · arXiv:1812.00336
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Distributed Prioritized Experience Replay 2 Mar 2018 · 15 repositories · arXiv:1803.00933Syntology ran 0 of 15 samples · 15 unverified
Tasks archive 2025-07-28
9 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections