Methods › Reinforcement Learning › Policy Gradient Methods › DPG

Deterministic Policy Gradient

DPG

introduced 2014 20 papers tagged archive 2025-07-28

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

Deterministic Policy Gradient, or DPG, is a policy gradient method for reinforcement learning. Instead of the policy function π(.|s) being modeled as a probability distribution, DPG considers and calculates gradients for a deterministic policy a = μₜₕₑₜₐ(s).

Papers archive 2025-07-28

20 shown of 20, 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 28 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 Learning4
Reinforcement Learning (RL)4
reinforcement-learning3
Continuous Control2
Image Generation2
Language Modeling2
Language Modelling2
Q-Learning2
continuous-control2
Abstractive Text Summarization1
Adversarial Attack1
Code Generation1
Decoder1
Deep Reinforcement Learning1
Face Recognition1
Game Design1
Model Predictive Control1
Motion Planning1
MuJoCo1
Object Detection1

Usage over time archive 2025-07-28

Papers per year tagged with DPG: 2017 to 2025, peak 5 5 0 2017: 1 paper 2017 2018: 3 papers 2018 2019: 1 paper 2019 2020: 1 paper 2020 2021: 5 papers 2021 2022: 2 papers 2022 2023: 0 papers 2023 2024: 4 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (20 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

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