Methods › Reinforcement Learning › Policy Gradient Methods › DDPG

Deep Deterministic Policy Gradient

DDPG

218 papers tagged archive 2025-07-28

Introduced by Timothy P. Lillicrap et al. in Continuous control with deep reinforcement learning

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

DDPG, or Deep Deterministic Policy Gradient, is an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. It combines the actor-critic approach with insights from DQNs: in particular, the insights that 1) the network is trained off-policy with samples from a replay buffer to minimize correlations between samples, and 2) the network is trained with a target Q network to give consistent targets during temporal difference backups. DDPG makes use of the same ideas along with batch normalization.

PaperSource

Papers archive 2025-07-28

30 shown of 218, 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 88 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)131
reinforcement-learning114
Deep Reinforcement Learning105
Reinforcement Learning102
Continuous Control36
continuous-control34
Q-Learning17
MuJoCo16
OpenAI Gym15
Decision Making14
Management13
Autonomous Driving8
Multi-agent Reinforcement Learning6
Scheduling6
energy management6
Diversity5
Federated Learning5
Benchmarking4
Data Augmentation4
Edge-computing4

Usage over time archive 2025-07-28

Papers per year tagged with DDPG: 2015 to 2025, peak 34 34 0 2015: 1 paper 2015 2016: 1 paper 2016 2017: 6 papers 2017 2018: 21 papers 2018 2019: 22 papers 2019 2020: 34 papers 2020 2021: 31 papers 2021 2022: 32 papers 2022 2023: 33 papers 2023 2024: 26 papers 2024 2025: 11 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (218 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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