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MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned
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Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more broadly applicable methods, organizers need to enforce the use of general techniques, the use of sample-efficient methods, and the reproducibility of the results. While beneficial for the research community, these restrictions come at a cost—increased difficulty. If the barrier for entry is too high, many potential participants are demoralized. With this in mind, we hosted the third edition of the MineRL ObtainDiamond competition, MineRL Diamond 2021, with a separate track in which we permitted any solution to promote the participation of newcomers. With this track and more extensive tutorials and support, we saw an increased number of submissions. The participants of this easier track were able to obtain a diamond, and the participants of the harder track progressed the generalizable solutions in the same task.
Al-Gaashani M. S., Xu W., Obsie E. Y., Applied Soft Computing Journal 2025
The recent monkeypox disease outbreak and its serious implications emphasize the urgent necessity for advanced diagnostic tools capable of rapid and accurate disease detection. In response, this study introduces a novel architecture for monkeypox disease identification, leveraging the capabilities of MobileNetV2 combined with progressive transfer learning and innovative model enhancements. The research began by analyzing ...
Added: October 22, 2026
Ibrahim A., Ratnikov F., Kaledin M. et al., Computer Physics Communications 2026 Vol. 329 Article 110407
We present RLABC (Reinforcement Learning for Accelerator Beamline Control), an open-source Python framework that automatically transforms standard Elegant beamline configurations into reinforcement learning environments. RLABC integrates with the widely-used Elegant beam dynamics simulation code via SDDS-based interfaces, enabling researchers to apply modern RL algorithms to beamline optimization with minimal RL-specific development. Validation on a test ...
Added: September 20, 2026
Emelyanov A., Sergei Kudriashov, Alena Fenogenova, , in: Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)Vol. 3: System Demonstrations.: Rabat: Association for Computational Linguistics, 2026. Ch. 11 P. 139–153.
Membership Inference Attacks (MIAs) aim to determine whether a specific data point was included in the training set of a target model. Although there are have been numerous methods developed for detecting data contamination in large language models (LLMs), their performance on multimodal LLMs (MLLMs) falls short due to the instabilities introduced through multimodal component ...
Added: May 19, 2026
Rome: Springer, 2025.
This book constitutes the refereed proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023, held in Rome, Italy, during November 13-15, 2023.
The 9 full papers and 8 short papers included in this book were carefully reviewed and selected from 166 submissions. They were organized in topical sections ...
Added: May 2, 2025
Соловьёв Р. А., Габрушева Т., Ванг В., Image and Vision Computing 2021 Vol. 107 P. 104117–0
We present a novel method for combining predictions in ensembles of different object detection models: weighted boxes fusion. This method significantly improves the quality of the fused predicted rectangles for an ensemble.
We tested the method on several datasets and evaluated it in the context of the Open Images and COCO Object Detection challenges. It helped ...
Added: January 15, 2025
Белокопытов А. С., Kleeva D., Ossadtchi A., , in: Advances in Neural Computation, Machine Learning, and Cognitive Research VIII.: Springer, 2024. P. 317–326.
Added: October 23, 2024
Rogachev A., Ratnikov F., 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 ...
Added: July 2, 2024
Menshchikov A., Shadrin D., Prutyanov V. et al., IEEE Transactions on Computers 2021 Vol. 70 No. 8 P. 1175–1188
The Hogweed of Sosnowskyi (lat. Heracleum sosnowskyi) is poisonous for humans, dangerous for farming crops, and local ecosystems. This plant is fast-growing and has already spread all over Eurasia: from Germany to the Siberian part of Russia, and its distribution expands year-by-year. In-situ detection of this harmful plant is a tremendous challenge for many countries. ...
Added: May 11, 2024
Cham: Springer, 2024.
This book constitutes revised selected papers from the thoroughly refereed proceedings of the 11th International Conference on Analysis of Images, Social Networks and Texts, AIST 2023, held in Yerevan, Armenia, during September 28-30, 2023.
The 24 full papers included in this book were carefully reviewed and selected from 93 submissions. They were organized in topical sections ...
Added: March 25, 2024
Nikita O. Starodubcev, Nikitin N., Andronova E. et al., Engineering Applications of Artificial Intelligence 2023 Vol. 119 Article 105715
In recent years generative design techniques have become firmly established in numerous applied fields, especially in engineering. These methods are crucial for automating the initial stages of the engineering design of various structures, which reduces the amount of routine work. However, existing approaches are limited by the specificity of the problem under consideration. In addition, ...
Added: March 5, 2024
Zhevnenko D., Kazantsev M., Makarov I., Journal of Industrial Information Integration 2023 Vol. 33 Article 100444
The paper deals with the problem of controlling the state of industrial devices according to the readings of their sensors. The current methods are based on an approach to feature extraction in which the prediction occurs. We propose an interaction method of multiple blocks of different complexity, which aggregate information differently over time, to create ...
Added: February 15, 2024
Kotelnikov A., Baranchuk D., Ivan Rubachev et al., , 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.
Denoising diffusion probabilistic models are becoming the leading generative modeling paradigm for many important data modalities. Being the most prevalent in the computer vision community, diffusion models have recently gained some attention in other domains, including speech, NLP, and graph-like data. In this work, we investigate if the framework of diffusion models can be advantageous ...
Added: February 11, 2024
Zhang P., Du J., Wang L. et al., Applied Soft Computing Journal 2022 Vol. 122 Article 108816
Human Learning Optimization (HLO) is a simple yet powerful meta-heuristic developed based on a simplified human learning model. Many cognitive activities of humans contain an element of reasoning, and with reasoning, humans can gain deeper information on problems to boost learning performance. Inspired by this fact, this paper proposes a novel human learning optimization algorithm ...
Added: April 11, 2022
Anton Zakharenkov, Makarov I., , in: Proceedings of IEEE 21st International Symposium on Computational Intelligence and Informatics (CINTI'21), 18-20 Nov. 2021.: NY: IEEE, 2021. P. 000131–000136.
Added: January 19, 2022
Laurent F., Schneider M., Scheller C. et al., , in: Proceedings of Machine Learning ResearchVol. 133: Proceedings of the NeurIPS 2020: Competition and Demonstration Track.: PMLR, 2021. P. 275–301.
The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP). The VRSP is concerned with scheduling trips in traffic networks and the re-scheduling of vehicles when disruptions occur, for example the breakdown of a vehicle. While solving the VRSP in various settings has been an active area in operations research ...
Added: September 6, 2021
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. ...
Added: September 1, 2021
Ivanov D., Egorov V., Shpilman A., , in: AAMAS'2021: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems.: IFAAMAS, 2021. P. 1536–1538.
Recent reinforcement learning studies extensively explore the interplay between cooperative and competitive behaviour in mixed environments. Unlike cooperative environments where agents strive towards a common goal, mixed environments are notorious for the conflicts of selfish and social interests. As a consequence, purely rational agents often struggle to maintain cooperation. A prevalent approach to induce cooperative ...
Added: May 29, 2021