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Double DQN

45 papers tagged archive 2025-07-28

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

A Double Deep Q-Network, or Double DQN utilises Double Q-learning to reduce overestimation by decomposing the max operation in the target into action selection and action evaluation. We evaluate the greedy policy according to the online network, but we use the target network to estimate its value. The update is the same as for DQN, but replacing the target Y^(DQN)ₜ with:

Y^(DoubleDQN)ₜ = Rₜ₊₁+γQ(Sₜ₊₁, maxₐQ(Sₜ₊₁, a; θₜ);θₜ⁻)

Compared to the original formulation of Double Q-Learning, in Double DQN the weights of the second network θ^′ₜ are replaced with the weights of the target network θₜ⁻ for the evaluation of the current greedy policy.

Source: Deep Reinforcement Learning with Double Q-learning

Papers archive 2025-07-28

30 shown of 45, 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

20 shown of 43 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 Learning27
Reinforcement Learning (RL)26
reinforcement-learning25
Deep Reinforcement Learning16
Q-Learning15
Atari Games8
OpenAI Gym5
Decision Making3
General Reinforcement Learning2
Model-based Reinforcement Learning2
Scheduling2
Traffic Signal Control2
Algorithmic Trading1
BIG-bench Machine Learning1
Cloud Computing1
Collision Avoidance1
Combinatorial Optimization1
Conformal Prediction1
Diversity1
Efficient Exploration1

Usage over time archive 2025-07-28

Papers per year tagged with Double DQN: 2015 to 2025, peak 10 10 0 2015: 1 paper 2015 2016: 0 papers 2016 2017: 0 papers 2017 2018: 4 papers 2018 2019: 4 papers 2019 2020: 10 papers 2020 2021: 8 papers 2021 2022: 8 papers 2022 2023: 7 papers 2023 2024: 2 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (45 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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