ArticleHeliyon2024
Deep deterministic policy gradient algorithm: A systematic review.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Adaptive Control of Lower-Limb Assistive Exoskeleton for Rehabilitation Using Deep Reinforcement Learning.Sensors (Basel, Switzerland) · 2026Article
- PPO-GPR: A Custom Proximal Policy Optimization Tool for Active Reinforcement Learning.ACS engineering Au · 2026Article
- Adaptive PID-based deep reinforcement learning for load frequency control in islanded microgrids with heterogeneous resources and energy storage.Scientific reports · 2026Article
- Prediction of Composite Supercapacitor Performance Through Combining Machine Learning with Novel Binder-Related Features.Nanomaterials (Basel, Switzerland) · 2026Article
- Integrating self-attention and LSTM into TD3 for robust mobile robot navigation in dynamic environments.Scientific reports · 2026Article
- Deep Reinforcement Learning-Based Intelligent Water Level Control: From Simulation to Embedded Implementation.Sensors (Basel, Switzerland) · 2025Article
- LSTM-Enhanced TD3 and Behavior Cloning for UAV Trajectory Tracking Control.Biomimetics (Basel, Switzerland) · 2025Article
- Next-Generation Hydrogel Design: Computational Advances in Synthesis, Characterization, and Biomedical Applications.Polymers · 2025Review
- The innovation path of VR technology integration into music classroom teaching in colleges and universities.Scientific reports · 2025Article
- Combining meta reinforcement learning with neural plasticity mechanisms for improved AI performance.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep Reinforcement Learning (DRL) has gained significant adoption in diverse fields and applications, mainly due to its proficiency in resolving complicated decision-making problems in spaces with high-dimensional states and actions. Deep Deterministic Policy Gradient (DDPG) is a well-known DRL algorithm that adopts an actor-critic approach, synthesizing the advantages of value-based and policy-based reinforcement learning methods. The aim of this study is to provide a thorough examination of the latest developments, patterns, obstacles, and potential opportunities related to DDPG. A systematic search was conducted using relevant academic databases (Scopus, Web of Science, and ScienceDirect) to identify 85 relevant studies published in the last five years (2018-2023). We provide a comprehensive overview of the key concepts and components of DDPG, including its formulation, implementation, and training. Then, we highlight the various applications and domains of DDPG, including Autonomous Driving, Unmanned Aerial Vehicles, Resource Allocation, Communications and the Internet of Things, Robotics, and Finance. Additionally, we provide an in-depth comparison of DDPG with other DRL algorithms and traditional RL methods, highlighting its strengths and weaknesses. We believe that this review will be an essential resource for researchers, offering them valuable insights into the methods and techniques utilized in the field of DRL and DDPG.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.