Search Results for author: Aleksandra Malysheva

Found 5 papers, 2 papers with code

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

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

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.

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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