Hierarchical deep reinforcement learning for self-adaptive economic dispatch
HeliyonIt is challenging to accurately model the overall uncertainty of the power system when it is connected to large-scale intermittent generation sources such as wind and photovoltaic generation due to the inherent volatility, uncertainty, and indivisibility
Mengshi Li +3 more
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Hierarchical deep reinforcement learning reveals novel mechanism of cell movement. [PDF]
Nat Mach Intell, 2022 Time-lapse images of cells and tissues contain rich information of dynamic cell behaviors, which reflect the underlying processes of proliferation, differentiation and morphogenesis. However, we lack computational tools for effective inference. Here, we exploit Deep Reinforcement Learning (DRL) to infer cell-cell interactions and collective cell ...
Wang Z, Xu Y, Wang D, Yang J, Bao Z.
europepmc +3 more sources
Hierarchical Model-Based Deep Reinforcement Learning for Single-Asset Trading
Analytics, 2023 We present a hierarchical reinforcement learning (RL) architecture that employs various low-level agents to act in the trading environment, i.e., the market.
Adrian Millea
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Enhancing knowledge graph recommendations through deep reinforcement learning [PDF]
Scientific ReportsRecommendation systems are an important tool for information filtering, which have been widely applied in both industry and academia. Although recommendation methods that combine deep learning with collaborative filtering have improved recommendation ...
Jinlian Zhou +4 more
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LLMs augmented hierarchical reinforcement learning with action primitives for long-horizon manipulation tasks [PDF]
Scientific ReportsDeep reinforcement learning methods have shown promising results in learning specific tasks, but struggle to cope with the challenges of long horizon manipulation tasks.
Ning Zhang +3 more
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IEEE Access, 2018 In recent years, reinforcement learning (RL) has achieved remarkable success due to the growing adoption of deep learning techniques and the rapid growth of computing power.
Tuyen P. Le +2 more
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An adaptive coverage method for dynamic wireless sensor network deployment using deep reinforcement learning [PDF]
Scientific ReportsCoverage optimization stands as a foundational challenge in Wireless Sensor Networks (WSNs), exerting a critical influence on monitoring fidelity and holistic network efficacy.
Peng Zhou +4 more
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Using Deep Reinforcement Learning with Hierarchical Risk Parity for Portfolio Optimization
International Journal of Financial Studies, 2022 We devise a hierarchical decision-making architecture for portfolio optimization on multiple markets. At the highest level a Deep Reinforcement Learning (DRL) agent selects among a number of discrete actions, representing low-level agents.
Adrian Millea, Abbas Edalat
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An novel cloud task scheduling framework using hierarchical deep reinforcement learning for cloud computing. [PDF]
PLoS ONEWith the increasing popularity of cloud computing services, their large and dynamic load characteristics have rendered task scheduling an NP-complete problem.To address the problem of large-scale task scheduling in a cloud computing environment, this ...
Delong Cui +5 more
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Hierarchical Reinforcement Learning: A Survey and Open Research Challenges
Machine Learning and Knowledge Extraction, 2022 Reinforcement learning (RL) allows an agent to solve sequential decision-making problems by interacting with an environment in a trial-and-error fashion. When these environments are very complex, pure random exploration of possible solutions often fails,
Matthias Hutsebaut-Buysse +2 more
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