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Deep Reinforcement Learning Methods in Match-3 Game
P. 51-62.
Ildar Kamaldinov, Makarov I.
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 that could be expanded, which would have a connection with the real business. The results for the most popular deep reinforcement learning algorithms are presented as a baseline.
In book
Springer, 2019
Ildar Kamaldinov, Makarov I., , 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 ...
Added: July 30, 2019
Shpilman A., 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.
Over recent years, deep reinforcement learning has shown strong successes in complex single-Agent tasks, and more recently this approach has also been applied to multi-Agent domains. In this paper, we propose a novel approach, called MAGNet, to multi-Agent reinforcement learning that utilizes a relevance graph representation of the environment obtained by a self-Attention mechanism, and ...
Added: July 15, 2020
Shpilman A., Kidzinski L., Ong C. et al., , in : The NeurIPS '18 Competition: From Machine Learning to Intelligent Conversations. : Springer, 2020. P. 69-128.
Added: December 2, 2019
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
Dmitry Akimov, Makarov I., , 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 ...
Added: November 19, 2019
Dmitry Akimov, Makarov I., , 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 ...
Added: July 29, 2019
[б.и.], 2020
Self-driving cars and advanced safety features present one of today’s greatest challenges and opportunities for Artificial Intelligence (AI). Despite billions of dollars of investments and encouraging progress under certain operational constraints, there are no driverless cars on public roads today without human safety drivers. Autonomous Driving research spans a wide spectrum, from modular architectures -- ...
Added: December 28, 2020
Laurent F., Schneider M., Scheller C. et al., , in : Proceedings of Machine Learning Research. Vol. 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
Shpilman A., Malysheva A., Sung T. T. et al., , in : Deep RL Workshop NeurIPS 2018. : [б.и.], 2018. P. 1-10.
Over recent years, deep reinforcement learning has shown strong successes in
complex single-agent tasks, and more recently this approach has also been applied
to multi-agent domains. In this paper, we propose a novel approach, called MAGnet,
to multi-agent reinforcement learning (MARL) that utilizes a relevance graph
representation of the environment obtained by a self-attention mechanism, and
a message-generation technique inspired ...
Added: January 18, 2019
Kotov F., Ivanov F., Timokhin I., , in : 2023 XVIII International Symposium Problems of Redundancy in Information and Control Systems (REDUNDANCY). : IEEE, 2023. P. 64-69.
The Successive Cancellation List (SCL) algorithm is a widely used decoding technique in communication systems. However, constructing the critical set for SCL decoding is a challenging task, as it requires a large number of computations and can lead to significant decoding delays. In this paper, a new approach to critical set construction for SCL decoding ...
Added: December 9, 2023
Berezovskiy V., Morozov N., , in : The 2nd Workshop and Challenges for Out-of-Distribution Generalization in Computer Vision. ICCV 2023. : [б.и.], 2023.
Knowledge distillation (KD) is a powerful model compression technique broadly used in practical deep learning applications. It is focused on training a small student network to mimic a larger teacher network. While it is widely known that KD can offer an improvement to student generalization in i.i.d setting, its performance under domain shift, i.e. the ...
Added: November 20, 2023
Hollandi R., Moshkov N., Paavolainen L. et al., Trends in Cell Biology 2022
Single nucleus segmentation is a frequent challenge of microscopy image processing, since it is the first step of many quantitative data analysis pipelines. The quality of tracking single cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought ...
Added: January 21, 2022
Chistyakov A., Lobacheva E., Kuznetsov A. et al., , in : Workshop of the 5th International Conference on Learning Representations (ICLR). : [б.и.], 2017. P. 1-4.
In this paper, we propose a new feature extraction technique for program execution logs. First, we automatically extract complex patterns from a program's behavior graph. Then, we embed these patterns into a continuous space by training an autoencoder. We evaluate the proposed features on a real-world malicious software detection task. We also find that the ...
Added: October 31, 2018
Umerenkov D., Herbert A., Konovalov Dmitrii et al., Life Science Alliance 2023 Vol. 6 No. 7 Article e202301962
Identifying roles for Z-DNA remains challenging given their dynamic nature. Here, we perform genome-wide interrogation with the DNABERT transformer algorithm trained on experimentally identified Z-DNA forming sequences (Z-flipons). The algorithm yields large performance enhancements (F1 = 0.83) over existing approaches and implements computational mutagenesis to assess the effects of base substitution on Z-DNA formation. We ...
Added: June 9, 2023
Gorishniy Y., Ivan Rubachev, Babenko A., , in : Thirty-Sixth Conference on Neural Information Processing Systems : NeurIPS 2022. : Curran Associates, Inc., 2022. Ch. 1. P. 24991-25004.
Added: January 28, 2023
Lobacheva E., Chirkova N., Vetrov D., / International Conference on Machine Learning. Series 1 "Workshop on Learning to Generate Natural Language". 2017.
Recurrent neural networks show state-of-the-art results in many text analysis tasks but often require a lot of memory to store their weights. Recently proposed Sparse Variational Dropout (Molchanov et al., 2017) eliminates the majority of the weights in a feed-forward neural network without significant loss of quality. We apply this technique to sparsify recurrent neural ...
Added: October 19, 2017
Malafeev A., Nikolaev K., , in : Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Kazan, Russia, July 17–19, 2019, Revised Selected Papers. Communications in Computer and Information Science. Vol. 1086.: Springer, 2020. P. 154-159.
In this paper, a deep learning method study is conducted to solve a new multiclass text classification problem, identifying user interests by text messages. We used an original dataset of almost 90 thousand forum text messages, labeled for ten interests. We experimented with different modern neural network architectures: recurrent and convolutional, as well as simpler ...
Added: November 7, 2019
Alanov A., Kochurov M., Volkhonskiy D. et al., , in : Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2020). Vol. 4.: SciTePress, 2020. P. 214-221.
We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism. The user control ability allows to explicitly specify the texture which should be generated by the model. This property follows from using an encoder part which learns a latent representation for each texture from the dataset. To ensure ...
Added: November 8, 2020
[б.и.], 2017
The performance of machine learning methods is heavily dependent on the choice of data representation (or features) on which they are applied. The rapidly developing field of representation learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data. We take a broad view of the field and include ...
Added: October 31, 2018
Koch S., Matveev A., Jiang Z. et al., , in : Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019). : IEEE, 2019. P. 9601-9611.
We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows ...
Added: November 26, 2019
Atanov A., Ashukha A., Struminsky K. et al., , in : Proceedings of the 7th International Conference on Learning Representations (ICLR 2019). : ICLR, 2019. P. 1-17.
Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution. In this work, we propose a new type of prior distributions for convolutional neural networks, deep weight prior (DWP), that exploit generative models to encourage a specific structure of ...
Added: September 2, 2019
Morozov N., Rakitin D., Oleg Desheulin et al., , in : Neural Fields across Fields: Methods and Applications of Implicit Neural Representations. ICLR 2023 Workshop. : [б.и.], 2023. Ch. 8.
In view synthesis, a neural radiance field approximates underlying density and radiance fields based on a sparse set of scene pictures. To generate a pixel of a novel view, it marches a ray through the pixel and computes a weighted sum of radiance emitted from a dense set of ray points. This rendering algorithm is ...
Added: July 18, 2023
Терещенко С. Н., Perov A., Осипов А. Л., Siberian Journal of Life Sciences and Agriculture 2021 Т. 13 № 1 С. 144-155
Background.
Development of a convolutional neural network model for detecting cassava diseases from a mobile phone photo.
Materials and methods. The material for the research was taken images with various types of cassava diseases, published in open access of the Kaggle platform.
Research methods: theory of design and development of information systems, programming, methods of augmentation and extension ...
Added: November 17, 2021
Pavlov F., Poptsova M., , in : 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). : Seul : IEEE, 2020. P. 2800-2805.
Added: March 29, 2021