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Deep Reinforcement Learning in VizDoom via DQN and Actor-Critic Agents
Ch. 12. P. 138–150.
Maria Bakhanova, Ilya Makarov
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.
В книге
* 1. Vol. 12861. , Springer, 2021.
Ибрахим А., Деркач Д. А., Каледин М. Л. и др., Computer Physics Communications 2026 Vol. 329 Article 110407
Мы представляем RLABC (Reinforcement Learning for Accelerator Beamline Control) — фреймворк с открытым исходным кодом на языке Python, который автоматически преобразует стандартные конфигурации пучковых линий, используемые в Elegant, в среды для обучения с подкреплением. RLABC интегрируется с широко используемым программным комплексом моделирования динамики пучка Elegant посредством интерфейсов на основе SDDS, что позволяет исследователям применять современные алгоритмы обучения с подкреплением для оптимизации ...
Добавлено: 20 сентября 2026 г.
Никулин А. М., Белоусов Ю. С., Свидченко О. А. и др., , in: Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track.: PMLR, 2022.
Добавлено: 8 октября 2024 г.
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.
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.
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.
IFAAMAS, 2021.
Добавлено: 29 мая 2021 г.
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.
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.
Шпильман А. А., Kidzinski L., Ong C. и др., , in: The NeurIPS '18 Competition: From Machine Learning to Intelligent Conversations.: Springer, 2020. P. 69–128.
Добавлено: 2 декабря 2019 г.
Dmitry Akimov, Макаров И. А., , in: Proceedings of the Fifth Workshop on Experimental Economics and Machine Learning at the National Research University Higher School of Economics co-located with the Seventh International Conference on Applied Research in Economics (iCare7).: Aachen: CEUR Workshop Proceedings, 2019. P. 3–17.
In this work, we study deep reinforcement algorithms for partially observable Markov decision processes (POMDP) combined with Deep Q-Networks. To our knowledge, we are the first to apply standard Markov decision process architectures to POMDP scenarios. We propose an extension of DQN with Dueling Networks and several other model-free policies to training agent using deep ...
Добавлено: 19 ноября 2019 г.
Ildar Kamaldinov, Макаров И. А., , in: Procedings of IEEE Conference on Games (COG'19).: NY: IEEE, 2019. P. 1–4.
An increasing number of algorithms in deep reinforcement learning area creates new challenges for environments, particularly, for their comprehensive analysis and searching application areas. The key purpose of this article is to provide an extensible environment for researches. We consider a Match-3 game, which has simple gameplay, but challenging game design for engaging players. The ...
Добавлено: 30 июля 2019 г.
Dmitry Akimov, Макаров И. А., , in: Proceedings of 11th International Conference on Advances in Multimedia (MMEDIA'19).: Lansing: ThinkMind, 2019. P. 59–64.
In this work, we study the effect of combining existent improvements for Deep Q-Networks (DQN) in Markov Decision Processes (MDP) and Partially Observable MDP (POMDP) settings. Combinations of several heuristics, such as Distributional Learning and Dueling architectures improvements, for MDP are well-studied. We propose a new combination method of simple DQN extensions and develop a ...
Добавлено: 29 июля 2019 г.
Шпильман А. А., Malysheva A., Kudenko D., , in: Adaptive and Learning Agents Workshop at International Joint Conference on Autonomous Agents and Multiagent Systems.: [б.и.], 2019. P. 1–8.
Добавлено: 13 июня 2019 г.
Шпильман А. А., Malysheva A., Sung T. T. и др., , in: Deep RL Workshop NeurIPS 2018.: [б.и.], 2018. P. 1–10.
Добавлено: 18 января 2019 г.