scholarly journals Minimum Throughput Maximization for Multi-UAV Enabled WPCN: A Deep Reinforcement Learning Method

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 9124-9132 ◽  
Author(s):  
Jie Tang ◽  
Jingru Song ◽  
Junhui Ou ◽  
Jingci Luo ◽  
Xiuyin Zhang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Throughput Maximization ◽  
Learning Method ◽  
Multi Uav

A Plant Control Technology Using Reinforcement Learning Method with Automatic Reward Adjustment

2009 ◽  
Vol 129 (7) ◽  
pp. 1253-1263
Author(s):  
Toru Eguchi ◽  
Takaaki Sekiai ◽  
Akihiro Yamada ◽  
Satoru Shimizu ◽  
Masayuki Fukai
Keyword(s):  
Reinforcement Learning ◽  
Control Technology ◽  
Learning Method ◽  
Plant Control

QoE-driven Adaptive Deployment Strategy of Multi-UAV Networks Based on Hybrid Deep Reinforcement Learning

2021 ◽  
pp. 1-1
Author(s):  
Yi Zhou ◽  
Xiaoyong Ma ◽  
Shuting Hu ◽  
Danyang Zhou ◽  
Nan Cheng ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Deployment Strategy ◽  
Multi Uav

Control of an Inverted Pendulum by Reinforcement Learning Method in PLC Environment

2020 ◽  
Author(s):  
Gokhan Demirkiran ◽  
Ozcan Erdener ◽  
Onay Akpinar ◽  
Pelin Demirtas ◽  
M. Yagiz Arik ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Inverted Pendulum ◽  
Learning Method

scholarly journals Enhanced Reinforcement Learning Method Combining One-Hot Encoding-Based Vectors for CNN-Based Alternative High-Level Decisions

2021 ◽  
Vol 11 (3) ◽  
pp. 1291
Author(s):  
Bonwoo Gu ◽  
Yunsick Sung
Keyword(s):  
Reinforcement Learning ◽  
Search Algorithm ◽  
Classification Criteria ◽  
Tree Search ◽  
Learning Method ◽  
Board Game ◽  
Ancient China ◽  
Monte Carlo Tree Search ◽  
High Level ◽  
Tree Search Algorithm

Gomoku is a two-player board game that originated in ancient China. There are various cases of developing Gomoku using artificial intelligence, such as a genetic algorithm and a tree search algorithm. Alpha-Gomoku, Gomoku AI built with Alpha-Go’s algorithm, defines all possible situations in the Gomoku board using Monte-Carlo tree search (MCTS), and minimizes the probability of learning other correct answers in the duplicated Gomoku board situation. However, in the tree search algorithm, the accuracy drops, because the classification criteria are manually set. In this paper, we propose an improved reinforcement learning-based high-level decision approach using convolutional neural networks (CNN). The proposed algorithm expresses each state as One-Hot Encoding based vectors and determines the state of the Gomoku board by combining the similar state of One-Hot Encoding based vectors. Thus, in a case where a stone that is determined by CNN has already been placed or cannot be placed, we suggest a method for selecting an alternative. We verify the proposed method of Gomoku AI in GuPyEngine, a Python-based 3D simulation platform.

Collaborative Computation Offloading and Resource Allocation in Multi-UAV Assisted IoT Networks: A Deep Reinforcement Learning Approach

2021 ◽  
pp. 1-1
Author(s):  
Abegaz Mohammed Seid ◽  
Gordon Owusu Boateng ◽  
Stephen Anokye ◽  
Thomas Kwantwi ◽  
Guolin Sun ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Computation Offloading ◽  
Learning Approach ◽  
Multi Uav
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