Search Results for author: Aleksei Shpilman

Found 22 papers, 11 papers with code

Scalable Multi-Agent Model-Based Reinforcement Learning

1 code implementation25 May 2022 Vladimir Egorov, Aleksei Shpilman

While in mixed environments full autonomy of the agents can be a desirable outcome, cooperative environments allow agents to share information to facilitate coordination.

Model-based Reinforcement Learning reinforcement-learning +1

Self-Imitation Learning from Demonstrations

no code implementations21 Mar 2022 Georgiy Pshikhachev, Dmitry Ivanov, Vladimir Egorov, Aleksei Shpilman

Modern LfD algorithms require meticulous tuning of hyperparameters that control the influence of demonstrations and, as we show in the paper, struggle with learning from suboptimal demonstrations.

Imitation Learning

Improving State-of-the-Art in One-Class Classification by Leveraging Unlabeled Data

1 code implementation14 Mar 2022 Farid Bagirov, Dmitry Ivanov, Aleksei Shpilman

The former only learns from labeled positive data, whereas the latter also utilizes unlabeled data to improve the overall performance.

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

no code implementations17 Feb 2022 Anssi Kanervisto, Stephanie Milani, Karolis Ramanauskas, Nicholay Topin, Zichuan Lin, Junyou Li, Jianing Shi, Deheng Ye, Qiang Fu, Wei Yang, Weijun Hong, Zhongyue Huang, Haicheng Chen, Guangjun Zeng, Yue Lin, Vincent Micheli, Eloi Alonso, François Fleuret, Alexander Nikulin, Yury Belousov, Oleg Svidchenko, Aleksei Shpilman

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.

Maximum Entropy Model-based Reinforcement Learning

no code implementations2 Dec 2021 Oleg Svidchenko, Aleksei Shpilman

Recent advances in reinforcement learning have demonstrated its ability to solve hard agent-environment interaction tasks on a super-human level.

Dota 2 Model-based Reinforcement Learning +1

Simple End-to-end Deep Learning Model for CDR-H3 Loop Structure Prediction

1 code implementation20 Nov 2021 Natalia Zenkova, Ekaterina Sedykh, Tatiana Shugaeva, Vladislav Strashko, Timofei Ermak, Aleksei Shpilman

In this work, we present an end-to-end model to predict CDR H3 loop structure, that performs on par with state-of-the-art methods in terms of accuracy but an order of magnitude faster.

Solving Traffic4Cast Competition with U-Net and Temporal Domain Adaptation

1 code implementation5 Nov 2021 Vsevolod Konyakhin, Nina Lukashina, Aleksei Shpilman

In this technical report, we present our solution to the Traffic4Cast 2021 Core Challenge, in which participants were asked to develop algorithms for predicting a traffic state 60 minutes ahead, based on the information from the previous hour, in 4 different cities.

Domain Adaptation

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

1 code implementation24 Feb 2021 Dmitry Ivanov, Vladimir Egorov, Aleksei Shpilman

Recent reinforcement learning studies extensively explore the interplay between cooperative and competitive behaviour in mixed environments.

Multi-agent Reinforcement Learning Q-Learning

Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization

1 code implementation18 Dec 2020 Mikita Sazanovich, Anastasiya Nikolskaya, Yury Belousov, Aleksei Shpilman

Black-box optimization is one of the vital tasks in machine learning, since it approximates real-world conditions, in that we do not always know all the properties of a given system, up to knowing almost nothing but the results.

End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box

no code implementations17 Dec 2020 Vladislav Belyaev, Aleksandra Malysheva, Aleksei Shpilman

The task object tracking is vital in numerous applications such as autonomous driving, intelligent surveillance, robotics, etc.

Autonomous Driving Video Object Tracking

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

no code implementations17 Dec 2020 Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman

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.

Multi-agent Reinforcement Learning reinforcement-learning

Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data

no code implementations16 Dec 2020 Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman

In this paper, we demonstrate how data from videos of human running (e. g. taken from YouTube) can be used to shape the reward of the humanoid learning agent to speed up the learning and produce a better result.

A comparative evaluation of machine learning methods for robot navigation through human crowds

no code implementations16 Dec 2020 Anastasia Gaydashenko, Daniel Kudenko, Aleksei Shpilman

Robot navigation through crowds poses a difficult challenge to AI systems, since the methods should result in fast and efficient movement but at the same time are not allowed to compromise safety.

BIG-bench Machine Learning reinforcement-learning +1

Automatic generation of reviews of scientific papers

1 code implementation8 Oct 2020 Anna Nikiforovskaya, Nikolai Kapralov, Anna Vlasova, Oleg Shpynov, Aleksei Shpilman

In this paper, we present a method for the automatic generation of a review paper corresponding to a user-defined query.

Extractive Summarization

Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously

no code implementations7 Jul 2020 Mikita Sazanovich, Konstantin Chaika, Kirill Krinkin, Aleksei Shpilman

In this paper, we describe our winning approach to solving the Lane Following Challenge at the AI Driving Olympics Competition through imitation learning on a mixed set of simulation and real-world data.

Imitation Learning

Deep Multi-Agent Reinforcement Learning with Relevance Graphs

1 code implementation30 Nov 2018 Aleksandra Malysheva, Tegg Taekyong Sung, Chae-Bong Sohn, Daniel Kudenko, Aleksei Shpilman

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.

Multi-agent Reinforcement Learning reinforcement-learning

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