Results 31 to 40 of about 387,064 (296)
Hierarchical Reinforcement Learning Under Mixed Observability
, 2022 Accepted at the 15th International Workshop on the Algorithmic Foundations of Robotics (WAFR) 2022, University of Maryland, College Park.
Nguyen, Hai +5 more
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Option-Critic Algorithm Based on Mutual Information Optimization [PDF]
Jisuanji kexueAs an important research content of hierarchical reinforcement learning,temporal abstraction allows hierarchical reinforcement learning agents to learn policies at different time scales,which can effectively solve the sparse reward problem that is ...
LI Junwei, LIU Quan, XU Yapeng
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Optimal Hierarchical Learning Path Design With Reinforcement Learning
Applied Psychological Measurement, 2020 E-learning systems are capable of providing more adaptive and efficient learning experiences for learners than traditional classroom settings. A key component of such systems is the learning policy. The learning policy is an algorithm that designs the learning paths or rather it selects learning materials for learners based on information such as the ...
Xiao Li +3 more
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Planning Irregular Object Packing via Hierarchical Reinforcement Learning [PDF]
IEEE Robotics and Automation Letters, 2022 Object packing by autonomous robots is an important challenge in warehouses and logistics industry. Most conventional data-driven packing planning approaches focus on regular cuboid packing, which are usually heuristic and limit the practical use in ...
Sichao Huang +3 more
semanticscholar +1 more source
A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning
The Scientific World Journal, 2014 Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are “trial and error” and “related reward.” A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of ...
Yuchen Fu +3 more
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A Hierarchical Goal-Biased Curriculum for Training Reinforcement Learning
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Hierarchy and curricula are two techniques commonly used to improve training for Reinforcement Learning (RL) agents. Yet few works have examined how to leverage hierarchical planning to generate a curriculum for training RL Options.
Sunandita Patra +6 more
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UAV Swarm Confrontation Using Hierarchical Multiagent Reinforcement Learning
International Journal of Aerospace Engineering, 2021 With the development of unmanned aerial vehicle (UAV) technology, UAV swarm confrontation has attracted many researchers’ attention. However, the situation faced by the UAV swarm has substantial uncertainty and dynamic variability.
Baolai Wang +3 more
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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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IEEE Access, 2020 Reinforcement learning (RL) techniques, while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces or in environments with sparse rewards.
Behzad Ghazanfari +2 more
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Int-HRL: towards intention-based hierarchical reinforcement learning. [PDF]
Neural Comput ApplAbstract 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 A +5 more
europepmc +7 more sources