Search Results for author: Aleksei Shpilman

Found 14 papers, 6 papers with code

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

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.

Robot Navigation

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

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