Temat: DQN - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A compact DQN model for mobile agents with collision avoidance
Autorzy:: Kamola, Mariusz
Powiązania:: https://bibliotekanauki.pl/articles/27314243.pdf
Data publikacji:: 2023
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: Q‐learning
DQN
reinforcement learning
Opis:: This paper presents a complete simulation and reinforce‐ ment learning solution to train mobile agents’ strategy of route tracking and avoiding mutual collisions. The aim was to achieve such functionality with limited resources, w.r.t. model input and model size itself. The designed models prove to keep agents safely on the track. Colli‐ sion avoidance agent’s skills developed in the course of model training are primitive but rational. Small size of the model allows fast training with limited computational resources.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2023, 17, 2; 28--35
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Goal - oriented conversational bot for employment domain
Autorzy:: Drozda, Paweł
Żmijewski, Tomasz
Osowski, Maciej
Krasnodębska, Aleksandra
Talun, Arkadiusz
Powiązania:: https://bibliotekanauki.pl/articles/22615524.pdf
Data publikacji:: 2023
Wydawca:: Uniwersytet Warmińsko-Mazurski w Olsztynie
Tematy:: chatbot
Deep Q Network
DQN
goal
oriented bot
Natural Language Processing
NLP
Opis:: This paper focuses of the implementation of the goal – oriented chatbot in order to prepare virtual resumes of candidates for job position. In particular the study was devoted to testing the feasibility of using Deep Q Networks (DQN) to prepare an effective chatbot conversation flow with the final system user. The results of the research confirmed that the use of the DQN model in the training of the conversational system allowed to increase the level of success, measured as the acceptance of the resume by the recruiter and the finalization of the conversation with the bot. The success rate increased from 10% to 64% in experimental environment and from 15% to 45% in production environment. Moreover, DQN model allowed the conversation to be shortened by an average of 4 questions from 11 to 7.
Źródło:: Technical Sciences / University of Warmia and Mazury in Olsztyn; 2023, 26(1); 111--123
1505-4675
2083-4527
Pojawia się w:: Technical Sciences / University of Warmia and Mazury in Olsztyn
Dostawca treści:: Biblioteka Nauki

Artykuł

Informacja