Results 261 to 270 of about 387,064 (296)

Hierarchical Bayesian Inverse Reinforcement Learning

IEEE Transactions on Cybernetics, 2015
Inverse reinforcement learning (IRL) is the problem of inferring the underlying reward function from the expert's behavior data. The difficulty in IRL mainly arises in choosing the best reward function since there are typically an infinite number of reward functions that yield the given behavior data as optimal.
Choi, JD Choi, Jae-Deug, Kim, KE Kim, Kee-Eung   +1 more
openaire   +3 more sources

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

Vision-Based Autonomous Driving: A Hierarchical Reinforcement Learning Approach

IEEE Transactions on Vehicular Technology, 2023
Human drivers have excellent perception and reaction abilities in complex environments such as dangerous highways, busy intersections, and harsh weather conditions.
Jiao Wang, Haoyi Sun, Can Zhu
semanticscholar   +1 more source

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

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

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

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

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