Results 21 to 30 of about 387,064 (296)

Hierarchical Reinforcement Learning With Guidance for Multi-Domain Dialogue Policy

IEEE/ACM Transactions on Audio Speech and Language Processing, 2023
Achieving high performance in a multi-domain dialogue system with low computation is undoubtedly challenging. Previous works applying an end-to-end approach have been very successful.
Mahdin Rohmatillah, Jen-Tzung Chien
semanticscholar   +1 more source

A Hierarchical Framework for Quadruped Robots Gait Planning Based on DDPG

Biomimetics, 2023
In recent years, significant progress has been made in employing reinforcement learning for controlling legged robots. However, a major challenge arises with quadruped robots due to their continuous states and vast action space, making optimal control ...
Yanbiao Li, Zhao Chen, Chentao Wu, Haoyu Mao, Peng Sun   +4 more
doaj   +1 more source

A Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes

IEEE Access, 2018
In recent years, reinforcement learning (RL) has achieved remarkable success due to the growing adoption of deep learning techniques and the rapid growth of computing power.
Tuyen P. Le, Ngo Anh Vien, TaeChoong Chung   +2 more
doaj   +1 more source

Hierarchical Reinforcement Learning [PDF]

, 1993
A response generating system can be seen as a mapping from a set of external states (inputs) to a set of actions (outputs). This mapping can be done in principally different ways. One method is to divide the state space into a set of discrete states and store the optimal response for each state. This is denominated a memory mapping system.
openaire   +3 more sources

Intelligent Attack Path Discovery Based on Hierarchical Reinforcement Learning [PDF]

Jisuanji kexue, 2023
Intelligent attack path discovery is a key technology for automated penetration testing,but existing methods face the problems of exponential growth of state and action space and sparse rewards,which make the algorithm difficult to converge.To this end ...
ZENG Qingwei, ZHANG Guomin, XING Changyou, SONG Lihua
doaj   +1 more source

LLM-Based Intent Processing and Network Optimization Using Attention-Based Hierarchical Reinforcement Learning [PDF]

IEEE Wireless Communications and Networking Conference
Intent-based network automation is a promising tool that enables easier network management; however, certain challenges must be addressed effectively. These are: 1) processing intents, i.e., identification of logic and necessary parameters to fulfill an ...
Md Arafat Habib, Pedro Enrique Iturria-Rivera, Yigit Ozcan, Medhat H. M. Elsayed, Majid Bavand, Raimundas Gaigalas, Melike Erol-Kantarci   +6 more
semanticscholar   +1 more source

Adjacency Constraint for Efficient Hierarchical Reinforcement Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Goal-conditioned Hierarchical Reinforcement Learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i.e., the goal space, is large.
Tianren Zhang, Shangqi Guo, Tian Tan, Xiaolin Hu, Feng Chen   +4 more
openaire   +3 more sources

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks [PDF]

AIChE Journal, 2022
Process synthesis experiences a disruptive transformation accelerated by digitization and artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state-of-the-art actor-critic logic.
Laura Stops, Roel Leenhouts, Qitong Gao, Artur M. Schweidtmann   +3 more
semanticscholar   +1 more source

Hierarchical reinforcement learning for automatic disease diagnosis

Bioinformatics, 2022
AbstractMotivationDisease diagnosis-oriented dialog system models the interactive consultation procedure as the Markov decision process, and reinforcement learning algorithms are used to solve the problem. Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making.
Cheng Zhong, Kangenbei Liao, Wei Chen, Qianlong Liu, Baolin Peng, Xuanjing Huang, Jiajie Peng, Zhongyu Wei   +7 more
openaire   +3 more sources

Hierarchical Reinforcement Learning for Air-to-Air Combat [PDF]

International Conference on Unmanned Aircraft Systems, 2021
Artificial Intelligence (AI) is becoming a critical component in the defense industry, as recently demonstrated by DARPA's AlphaDogfight Trials (ADT). ADT sought to vet the feasibility of AI algorithms capable of piloting an F-16 in simulated air-to-air ...
Adrian P. Pope, J. Ide, Daria Mićović, Henry Diaz, D. Rosenbluth, Lee Ritholtz, Jason C. Twedt, Thayne T. Walker, K. Alcedo, D. Javorsek   +9 more
semanticscholar   +1 more source