MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned

Белокопытов А. С., Клеева Д. Ф., Осадчий А. Е., , in: Advances in Neural Computation, Machine Learning, and Cognitive Research VIII.: Springer, 2024. P. 317–326.

Добавлено: 23 октября 2024 г.

Soft Margin Spectral Normalization for GANs

Рогачев А. И., Ратников Ф. Д., Computing and Software for Big Science 2024 Vol. 8 No. 1 Article 12

In this paper, we explore the use of Generative Adversarial Networks (GANs) to speed up the simulation process while ensuring that the generated results are consistent in terms of physics metrics. Our main focus is the application of spectral normalization for GANs to generate electromagnetic calorimeter (ECAL) response data, which is a crucial component of ...

Добавлено: 2 июля 2024 г.

Real-time detection of hogweed: UAV platform empowered by deep learning

Menshchikov A., Shadrin D., Прутьянов В. В. и др., IEEE Transactions on Computers 2021 Vol. 70 No. 8 P. 1175–1188

Добавлено: 11 мая 2024 г.

11th International Conference, AIST 2023, Yerevan, Armenia, September 28–30, 2023, Revised Selected Papers. Analysis of Images, Social Networks and Texts. Lecture Notes in Computer Science (LNCS, volume 14486)

Cham: Springer, 2024.

Добавлено: 25 марта 2024 г.

Generative design of physical objects using modular framework

Nikita O. Starodubcev, Nikitin N., Andronova E. и др., Engineering Applications of Artificial Intelligence 2023 Vol. 119 Article 105715

Добавлено: 5 марта 2024 г.

Interaction models for remaining useful lifetime estimation

Zhevnenko D., Kazantsev M., Макаров И. А., Journal of Industrial Information Integration 2023 Vol. 33 Article 100444

Статья посвящена проблеме контроля состояния промышленных устройств по показаниям их датчиков. Существующие методы основаны на подходе к извлечению признаков, в котором происходит предсказание. Мы предлагаем метод взаимодействия нескольких блоков различной сложности, которые по-разному агрегируют информацию во времени, для создания общего скрытого пространства для предсказания оставшегося срока службы (RUL), и обучаем полученную архитектуру за один проход ...

Добавлено: 15 февраля 2024 г.

TabDDPM: Modelling Tabular Data with Diffusion Models

Котельников А. К., Baranchuk D., Ivan Rubachev и др., , in: Proceedings of the 40th International Conference on Machine Learning: Volume 202: International Conference on Machine Learning, 23-29 July 2023, Honolulu, Hawaii, USAVol. 202: International Conference on Machine Learning, 23-29 July 2023, Honolulu, Hawaii, USA.: PMLR, 2023. P. 17564–17579.

Добавлено: 11 февраля 2024 г.

A human learning optimization algorithm with reasoning learning

Zhang P., Du J., Wang L. и др., Applied Soft Computing Journal 2022 Vol. 122 Article 108816

Добавлено: 11 апреля 2022 г.

Deep Reinforcement Learning with DQN vs. PPO in VizDoom

Anton Zakharenkov, Макаров И. А., , in: Proceedings of IEEE 21st International Symposium on Computational Intelligence and Informatics (CINTI'21), 18-20 Nov. 2021.: NY: IEEE, 2021. P. 000131–000136.

Добавлено: 19 января 2022 г.

Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World

Laurent F., Schneider M., Scheller C. и др., , in: Proceedings of Machine Learning ResearchVol. 133: Proceedings of the NeurIPS 2020: Competition and Demonstration Track.: PMLR, 2021. P. 275–301.

Добавлено: 6 сентября 2021 г.

Deep Reinforcement Learning in VizDoom via DQN and Actor-Critic Agents

Maria Bakhanova, Ilya Makarov, , in: Advances in Computational Intelligence: 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I* 1. Vol. 12861.: Springer, 2021. Ch. 12 P. 138–150.

In this work, we study the problem of learning reinforcement learning-based agents in a first-person shooter environment VizDoom. We compare several well-known architectures, such as DQN, DDQN, A3C, and Curiosity-driven model, while highlighting the main differences in learned policies of agents trained via these models. ...

Добавлено: 1 сентября 2021 г.

Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments

Иванов Д. И., Egorov V., Шпильман А. А., , in: AAMAS'2021: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems.: IFAAMAS, 2021. P. 1536–1538.

Добавлено: 29 мая 2021 г.

AAMAS'2021: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems

IFAAMAS, 2021.

Добавлено: 29 мая 2021 г.

Workshop on AI for Autonomous Driving (AIAD)

[б.и.], 2020.

Добавлено: 28 декабря 2020 г.

MAGNet: Multi-Agent Graph Network for Deep Multi-Agent Reinforcement Learning

Шпильман А. А., Malysheva A., Kudenko D., , in: Proceedings of 2019 XVI International Symposium "Problems of Redundancy in Information and Control Systems" (REDUNDANCY).: IEEE, 2019. P. 171–176.

Добавлено: 15 июля 2020 г.

Learning to Route in Similarity Graphs

Баранчук Д. А., Persiyanov D., Sinitsin A. и др., , in: International Conference on Machine Learning (ICML 2019).: PMLR, 2019. P. 475–484.

Добавлено: 16 марта 2020 г.

Deep Reinforcement Learning Methods in Match-3 Game

Ildar Kamaldinov, Макаров И. А., , in: Analysis of Images, Social Networks and Texts. 8th International Conference AIST 2019.: Springer, 2019. P. 51–62.

A large number of methods are being developed in the deep reinforcement learning area recently, but the scope of their application is limited. The number of environments does not always allow for a comprehensive assessment of a new agent training algorithm. The main purpose of this article is to present another environment for Match-3 game ...

Добавлено: 4 февраля 2020 г.