Methods › Reinforcement Learning › Policy Gradient Methods › MDPO

Mirror Descent Policy Optimization

MDPO

4 papers tagged archive 2025-07-28

Introduced by Manan Tomar et al. in Mirror Descent Policy Optimization

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

Mirror Descent Policy Optimization (MDPO) is a policy gradient algorithm based on the idea of iteratively solving a trust-region problem that minimizes a sum of two terms: a linearization of the standard RL objective function and a proximity term that restricts two consecutive updates to be close to each other. It is based on Mirror Descent, which is a general trust region method that attempts to keep consecutive iterates close to each other.

PaperSource

Papers archive 2025-07-28

4 shown of 4, 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

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.

TaskPapers
Continuous Control1
Hallucination1
Language Modeling1
Language Modelling1
Large Language Model1
MuJoCo1
Reinforcement Learning (RL)1
continuous-control1
model1

Usage over time archive 2025-07-28

Papers per year tagged with MDPO: 2020 to 2024, peak 2 2 0 2020: 1 paper 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 1 paper 2023 2024: 2 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (4 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

Policy Gradient Methods

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