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Deepmind Technologies Ltd deterministic policy gradient ddpg
Deterministic Policy Gradient Ddpg, supplied by Deepmind Technologies Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deterministic/algorithm+ddpg+deterministic+gradient+policy/pm41820077-52-8-4
Average 86 stars, based on 1 article reviews
deterministic policy gradient ddpg - by Bioz Stars, 2026-09
86/100 stars

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Article Title: Robust Strategy Generation for Automatic Navigation of Unmanned Surface Vehicles through Improved DDPG Algorithm
Article Snippet: DeepMind presents the deep deterministic policy gradient (DDPG) algorithm [12], which integrates deterministic policy gradient algorithm [13] and empirical playback techniques to achieve superior performance in continuous action spaces, which expands DRL application scenarios and allows agents and robots to be applied to a wider range of real-world problems.

Article Title: SLP-Improved DDPG Path-Planning Algorithm for Mobile Robot in Large-Scale Dynamic Environment
Article Snippet: The deep deterministic policy gradient (DDPG) reinforcement learning algorithm was introduced by the Google DeepMind group [ ].

Article Title: Integrating model-driven and data-driven methods for under-frequency load shedding control
Article Snippet: With the access of a high percentage of new energy sources, power system frequency stability is challenged.. Under-frequency load shedding (UFLS) is one of the primary measures to maintain frequency stability.. Due to the mismatch between the amount of load shed by the traditional UFLS methods and the actual active power deficit, a new UFLS method needs to be designed.

Article Title: Optimizing Multi-Vessel Collision Avoidance Decision Making for Autonomous Surface Vessels: A COLREGs-Compliant Deep Reinforcement Learning Approach
Article Snippet: A number of algorithms based on this principle have garnered significant attention, including the deep deterministic policy gradient (DDPG) algorithm proposed by the DeepMind team in 2016 [22], the asynchronous advantage actor–critic (A3C) algorithm proposed by Mnih et al. in 2016 [23], the Proximal Policy Optimization (PPO) algorithm developed by Schulman et al. in 2017 [24], and the soft actor–critic (SAC) algorithm proposed by Haarnoja et al. in 2018 [25].

Article Title: SLP-Improved DDPG Path-Planning Algorithm for Mobile Robot in Large-Scale Dynamic Environment.
Article Snippet: The deep deterministic policy gradient (DDPG) reinforcement learning algorithm was introduced by the Google DeepMind group [23].

Derivative Assay:

Article Title: Autonomous Driving Control for Passing Unsignalized Intersections Using the Semantic Segmentation Technique
Article Snippet: .. Deep Deterministic Policy Gradient (DDPG) [31], introduced by DeepMind in 2016, is an algorithm derived from the Actor–Critic architecture, enabling learning through both the Q-function and policy methods, eff ctively addressing p blems with continuous action sp ces. ..

Article Title: Autonomous Driving Control for Passing Unsignalized Intersections Using the Semantic Segmentation Technique
Article Snippet: .. DDPG Deep Deterministic Policy Gradient (DDPG) [31], introduced by DeepMind in 2016, is an algorithm derived from the Actor–Critic architecture, enabling learning through both the Q-function and policy methods, effectively addressing problems with continuous action spaces. ..

Control:

Article Title: Trajectory planning for robotic manipulator based on improved DDPG algorithm.
Article Snippet: Deep reinforcement learning (DRL) algorithms are increasingly applied to robotic manipulator planning.. However, conventional DRL approaches suffer from slow learning convergence and low success rates in indus trial trajectory planning tasks.. To address these issues, this paper proposes an improved deep deterministic policy gradient (DDPG) algorithm that more effectively achieves time-optimal trajectory planning for robotic manipu lators.



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