Hierarchical Reinforcement Learning Framework in Geographic Coordination for Air Combat Tactical Pursuit [PDF]
Entropy, 2023 This paper proposes an air combat training framework based on hierarchical reinforcement learning to address the problem of non-convergence in training due to the curse of dimensionality caused by the large state space during air combat tactical pursuit.
Ruihai Chen +4 more
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A neural model of hierarchical reinforcement learning. [PDF]
PLoS ONE, 2017 We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences ...
Daniel Rasmussen +2 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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HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control [PDF]
Inf Sci (N Y), 2023 Infectious diseases, such as Black Death, Spanish Flu, and COVID-19, have accompanied human history and threatened public health, resulting in enormous infections and even deaths among citizens.
Xinqi Du +4 more
semanticscholar +2 more sources
Hierarchical Reinforcement Learning
ACM Computing Surveys, 2021 Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches.
Shubham Pateria +3 more
semanticscholar +3 more sources
Hierarchical reinforcement learning-based traffic signal control [PDF]
Scientific ReportsEfficient traffic light control is a critical issue in urban transportation systems. Recently, deep reinforcement learning (DRL) has gained popularity as a method for real-time traffic light control.
Jiajing Shen
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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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DT-HRL: Mastering Long-Sequence Manipulation with Reimagined Hierarchical Reinforcement Learning [PDF]
BiomimeticsRobotic manipulators in warehousing and logistics often face complex tasks that involve multiple steps, frequent task switching, and long-term dependencies. Inspired by the hierarchical structure of human motor control, this paper proposes a Hierarchical
Junyang Zhang +4 more
doaj +2 more sources
Vision-Based Robot Navigation through Combining Unsupervised Learning and Hierarchical Reinforcement Learning [PDF]
Sensors, 2019 Extensive studies have shown that many animals’ capability of forming spatial representations for self-localization, path planning, and navigation relies on the functionalities of place and head-direction (HD) cells in the hippocampus.
Xiaomao Zhou +3 more
doaj +2 more sources
Int-HRL: towards intention-based hierarchical reinforcement learning [PDF]
Neural Computing and ApplicationsAbstract While deep reinforcement learning (RL) agents outperform humans on an increasing number of tasks, training them requires data equivalent to decades of human gameplay. Recent hierarchical RL methods have increased sample efficiency by incorporating information inherent to the structure of the decision problem but at the cost of having
Penzkofer, Anna +5 more
openaire +7 more sources