Results 261 to 270 of about 404,230 (315)

Budgeted Hierarchical Reinforcement Learning

2018 International Joint Conference on Neural Networks (IJCNN), 2018
In hierarchical reinforcement learning, the framework of options models sub-policies over a set of primitive actions. In this paper, we address the problem of discovering and learning options from scratch. Inspired by recent works in cognitive science, our approach is based on a new budgeted learning approach in which options naturally arise as a way ...
Aurelia Leon, Ludovic Denoyer
openaire   +1 more source

Meta-Hierarchical Reinforcement Learning (MHRL)-Based Dynamic Resource Allocation for Dynamic Vehicular Networks

IEEE Transactions on Vehicular Technology, 2022
With the rapid development of vehicular networks, there is an increasing demand for extensive networking, computting, and caching resources. How to allocate multiple resources effectively and efficiently for dynamic vehicular networks is extremely ...
Ying He, Yuhang Wang, Qiuzhen Lin, Jian-qiang Li   +3 more
semanticscholar   +1 more source

Planning-Augmented Hierarchical Reinforcement Learning

IEEE Robotics and Automation Letters, 2021
Planning algorithms are powerful at solving long-horizon decision-making problems but require that environment dynamics are known. Model-free reinforcement learning has recently been merged with graph-based planning to increase the robustness of trained policies in state-space navigation problems.
Robert Gieselmann, Florian T. Pokorny
openaire   +1 more source

Large-Scale Dynamic Scheduling for Flexible Job-Shop With Random Arrivals of New Jobs by Hierarchical Reinforcement Learning

IEEE Transactions on Industrial Informatics
As the intelligent manufacturing paradigm evolves, it is urgent to design a near real-time decision-making framework for handling the uncertainty and complexity of production line control.
Kun Lei, Peng Guo, Yi Wang, Jian Zhang, Xiangyin Meng, Linmao Qian   +5 more
semanticscholar   +1 more source

Hierarchical Reinforcement Learning Integrating With Human Knowledge for Practical Robot Skill Learning in Complex Multi-Stage Manipulation

IEEE Transactions on Automation Science and Engineering
This paper proposes a novel hierarchical reinforcement learning (HRL) framework of complex manipulation tasks which integrates the human prior knowledge.
Xing Liu, Gaozhao Wang, Zihao Liu, Y. Liu, Zhengxiong Liu, Panfeng Huang   +5 more
semanticscholar   +1 more source

Price-Matching-Based Regional Energy Market With Hierarchical Reinforcement Learning Algorithm

IEEE Transactions on Industrial Informatics
This article proposes a multienergy trading market model based on price matching, aiming to foster multienergy collaboration and enhance energy utilization through individual participation.
Ning Zhang, Juan Yan, Cungang Hu, Qiuye Sun, Lingxiao Yang, David Wenzhong Gao, Josep M. Guerrero, Yushuai Li   +7 more
semanticscholar   +1 more source

Hierarchical Reinforcement Learning

2009
Reinforcement learning (RL) deals with the problem of an agent that has to learn how to behave to maximize its utility by its interactions with an environment (Sutton & Barto, 1998; Kaelbling, Littman & Moore, 1996). Reinforcement learning problems are usually formalized as Markov Decision Processes (MDP), which consist of a finite set of ...
Carlos Diuk, Michael Littman
openaire   +1 more source

Autonomous reinforcement learning with hierarchical REPS

The 2013 International Joint Conference on Neural Networks (IJCNN), 2013
Future intelligent robots will need to interact with uncertain and changing environments. One key aspect to allow robotic agents to adapt to such situations is to enable them to learn multiple solution strategies to one problem, such that the agent can remain flexible and employ alternative solutions even if the preferred solution is no longer viable ...
Daniel, C., Neumann, G., Peters, J.
openaire   +2 more sources

Initial Task Allocation in Multi-Human Multi-Robot Teams: An Attention-Enhanced Hierarchical Reinforcement Learning Approach

IEEE Robotics and Automation Letters
Multi-human multi-robot teams (MH-MR) obtain tremendous potential in tackling intricate and massive missions by merging distinct strengths and expertise of individual members.
Ruiqi Wang, Dezhong Zhao, Arjun Gupte, Byung-Cheol Min   +3 more
semanticscholar   +1 more source

Goal-Conditioned Hierarchical Reinforcement Learning With High-Level Model Approximation

IEEE Transactions on Neural Networks and Learning Systems
Hierarchical reinforcement learning (HRL) exhibits remarkable potential in addressing large-scale and long-horizon complex tasks. However, a fundamental challenge, which arises from the inherently entangled nature of hierarchical policies, has not been ...
Yu Luo, Tianying Ji, Fuchun Sun, Huaping Liu, Jianwei Zhang, Mingxuan Jing, Wenbing Huang   +6 more
semanticscholar   +1 more source