4 research outputs found
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations
Modeling of real-world biological multi-agents is a fundamental problem in
various scientific and engineering fields. Reinforcement learning (RL) is a
powerful framework to generate flexible and diverse behaviors in cyberspace;
however, when modeling real-world biological multi-agents, there is a domain
gap between behaviors in the source (i.e., real-world data) and the target
(i.e., cyberspace for RL), and the source environment parameters are usually
unknown. In this paper, we propose a method for adaptive action supervision in
RL from real-world demonstrations in multi-agent scenarios. We adopt an
approach that combines RL and supervised learning by selecting actions of
demonstrations in RL based on the minimum distance of dynamic time warping for
utilizing the information of the unknown source dynamics. This approach can be
easily applied to many existing neural network architectures and provide us
with an RL model balanced between reproducibility as imitation and
generalization ability to obtain rewards in cyberspace. In the experiments,
using chase-and-escape and football tasks with the different dynamics between
the unknown source and target environments, we show that our approach achieved
a balance between the reproducibility and the generalization ability compared
with the baselines. In particular, we used the tracking data of professional
football players as expert demonstrations in football and show successful
performances despite the larger gap between behaviors in the source and target
environments than the chase-and-escape task.Comment: 14 pages, 5 figure
SoccerNet 2023 Challenges Results
peer reviewedThe SoccerNet 2023 challenges were the third annual video understanding
challenges organized by the SoccerNet team. For this third edition, the
challenges were composed of seven vision-based tasks split into three main
themes. The first theme, broadcast video understanding, is composed of three
high-level tasks related to describing events occurring in the video
broadcasts: (1) action spotting, focusing on retrieving all timestamps related
to global actions in soccer, (2) ball action spotting, focusing on retrieving
all timestamps related to the soccer ball change of state, and (3) dense video
captioning, focusing on describing the broadcast with natural language and
anchored timestamps. The second theme, field understanding, relates to the
single task of (4) camera calibration, focusing on retrieving the intrinsic and
extrinsic camera parameters from images. The third and last theme, player
understanding, is composed of three low-level tasks related to extracting
information about the players: (5) re-identification, focusing on retrieving
the same players across multiple views, (6) multiple object tracking, focusing
on tracking players and the ball through unedited video streams, and (7) jersey
number recognition, focusing on recognizing the jersey number of players from
tracklets. Compared to the previous editions of the SoccerNet challenges, tasks
(2-3-7) are novel, including new annotations and data, task (4) was enhanced
with more data and annotations, and task (6) now focuses on end-to-end
approaches. More information on the tasks, challenges, and leaderboards are
available on https://www.soccer-net.org. Baselines and development kits can be
found on https://github.com/SoccerNet
SoccerNet 2023 challenges results
SoccerNet 2023 Challenges ResultsThe SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet