Methods › Reinforcement Learning › Q-Learning Networks › Rainbow DQN

Rainbow DQN

9 papers tagged archive 2025-07-28

Introduced by Matteo Hessel et al. in Rainbow: Combining Improvements in Deep Reinforcement Learning

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Rainbow DQN is an extended DQN that combines several improvements into a single learner. Specifically:

PaperSource

Papers archive 2025-07-28

9 shown of 9, 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.

Tasks archive 2025-07-28

13 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Reinforcement Learning (RL)7
Deep Reinforcement Learning6
reinforcement-learning4
Q-Learning3
Reinforcement Learning3
Atari Games2
Computational Efficiency1
Decision Making1
Diversity1
Efficient Exploration1
Ensemble Learning1
Game of Go1
Montezuma's Revenge1

Usage over time archive 2025-07-28

Papers per year tagged with Rainbow DQN: 2017 to 2024, peak 2 2 0 2017: 1 paper 2017 2018: 0 papers 2018 2019: 1 paper 2019 2020: 2 papers 2020 2021: 2 papers 2021 2022: 2 papers 2022 2023: 0 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (9 dated). Bars are counts, not a trend claim.

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

Q-Learning Networks

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