Methods › Reinforcement Learning › Efficient Planning › Prioritized Sweeping

Prioritized Sweeping

6 papers tagged archive 2025-07-28

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

Prioritized Sweeping is a reinforcement learning technique for model-based algorithms that prioritizes updates according to a measure of urgency, and performs these updates first. A queue is maintained of every state-action pair whose estimated value would change nontrivially if updated, prioritized by the size of the change. When the top pair in the queue is updated, the effect on each of its predecessor pairs is computed. If the effect is greater than some threshold, then the pair is inserted in the queue with the new priority.

Source: Sutton and Barto, Reinforcement Learning, 2nd Edition

Papers archive 2025-07-28

6 shown of 6, 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.

Investigating the Interplay of Prioritized Replay and Generalization
12 Jul 2024 · 0 repositories · arXiv:2407.09702
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping
15 Jan 2020 · 0 repositories · arXiv:2001.07527
Reachability and Differential based Heuristics for Solving Markov Decision Processes
3 Jan 2019 · 0 repositories · arXiv:1901.00921
Prioritized Sweeping Neural DynaQ with Multiple Predecessors, and Hippocampal Replays
15 Feb 2018 · 0 repositories · arXiv:1802.05594
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
12 Feb 2018 · 1 repository · arXiv:1802.04325
Is prioritized sweeping the better episodic control?
20 Nov 2017 · 1 repository · arXiv:1711.06677

Tasks archive 2025-07-28

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

Task	Papers
Reinforcement Learning	4
Reinforcement Learning (RL)	3
reinforcement-learning	3
Q-Learning	2
Deep Reinforcement Learning	1
Hippocampus	1
Model-based Reinforcement Learning	1
Multi-agent Reinforcement Learning	1

Usage over time archive 2025-07-28

Papers per year the archive tags with this method, by the paper's archive date (6 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

Efficient Planning

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