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Relabeling and policy distillation of hierarchical reinforcement learning
Hierarchical reinforcement learning (HRL) is a promising method to extend traditional reinforcement learning to solve more complex tasks. HRL can...
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Learning Multiple-Gait Quadrupedal Locomotion via Hierarchical Reinforcement Learning
Over long periods of evolution, legged animals have developed the capability to use a variety of gaits to move efficiently and flexibly at different...
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Hierarchical Goal-Guided Learning for the Evasive Maneuver of Fixed-Wing UAVs based on Deep Reinforcement Learning
Fixed-wing unmanned aerial vehicles (UAVs) will play a vital role in forthcoming military conflicts. Effectively avoiding threats and improving the...
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Robot Subgoal-guided Navigation in Dynamic Crowded Environments with Hierarchical Deep Reinforcement Learning
Although deep reinforcement learning has recently achieved some successes in robot navigation, there are still unsolved problems. Particularly, a...
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Offline Hierarchical Reinforcement Learning: Enable Large-Scale Training in HRL
Large-scale trained models have shown significant success across various machine learning domains, leading researchers to explore their application...
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Fitness-Based Hierarchical Reinforcement Learning for Multi-human-robot Task Allocation in Complex Terrain Conditions
A fitness-based hierarchical reinforcement learning method is proposed in this study for multi-human-robot task allocation in complex terrain...
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Intelligent problem-solving as integrated hierarchical reinforcement learning
According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on...
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Node selection for model quality optimization in hierarchical federated learning based on deep reinforcement learning
In Hierarchical Federated Learning (HFL), data sample sizes and distribution of different clients vary greatly. Due to the heterogeneity of the data,...
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Hierarchical Reinforcement Learning Under Mixed Observability
The framework of mixed observable Markov decision processes (MOMDP) models many robotic domains in which some state variables are fully observable...
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Efficient relation extraction via quantum reinforcement learning
Most existing relation extraction methods only determine the relation type after identifying all entities, thus not fully modeling the interaction...
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Hierarchical traffic light-aware routing via fuzzy reinforcement learning in software-defined vehicular networks
Lack of a fully vehicular topology view and restricted vehicles' movement to streets with the time-varying traffic light conditions have caused...
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Sensor-Based Navigation Using Hierarchical Reinforcement Learning
Robotic systems are nowadays capable of solving complex navigation tasks. However, their capabilities are limited to the knowledge of the designer...
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A digital twin-driven flexible scheduling method in a human–machine collaborative workshop based on hierarchical reinforcement learning
Under the influence of the global COVID-19 pandemic, the demand for medical equipment and epidemic prevention materials has increased significantly,...
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Uncertainty-Aware Hierarchical Reinforcement Learning Robust to Noisy Observations
This work proposes UA-HRL, an uncertainty-aware hierarchical reinforcement learning framework for mitigating the problems caused by noisy sensor...
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Quadrupedal Locomotion in an Energy-efficient Way Based on Reinforcement Learning
Achieving energy-efficient motion is important for the application of quadruped robots in a wide range. In this paper, we propose a hierarchical...
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Hierarchical Reinforcement Learning Adversarial Algorithm Against Opponent with Fixed Offensive Strategy
Based on option-critic algorithm, a new adversarial algorithm named deterministic policy network with option architecture is proposed to improve...
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State-Dependent Maximum Entropy Reinforcement Learning for Robot Long-Horizon Task Learning
Task-oriented robot learning has shown significant potential with the development of Reinforcement Learning (RL) algorithms. However, the learning of...
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A hierarchical multi-agent allocation-action learning framework for multi-subtask games
Great progress has been made in the domain of multi-agent reinforcement learning in recent years. Most work concentrates on solving a single task by...
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Hierarchical deep reinforcement learning reveals a modular mechanism of cell movement
Time-lapse images of cells and tissues contain rich information about dynamic cell behaviours, which reflect the underlying processes of...
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Safe Reinforcement Learning-based Driving Policy Design for Autonomous Vehicles on Highways
Safe decision-making strategy of autonomous vehicles (AVs) plays a critical role in avoiding accidents. This study develops a safe reinforcement...