POGEMA: A Benchmark Platform for Cooperative Multi-Agent Navigation
Multi-agent reinforcement learning (MARL) has recently excelled in solving
challenging cooperative and competitive multi-agent problems in various
environments with, mostly, few agents and full observability. Moreover, a range
of crucial robotics-related tasks, such as multi-robot navigation and obstacle
avoidance, that have been conventionally approached with the classical
non-learnable methods (e.g., heuristic search) is currently suggested to be
solved by the learning-based or hybrid methods. Still, in this domain, it is
hard, not to say impossible, to conduct a fair comparison between classical,
learning-based, and hybrid approaches due to the lack of a unified framework
that supports both learning and evaluation. To this end, we introduce POGEMA, a
set of comprehensive tools that includes a fast environment for learning, a
generator of problem instances, the collection of pre-defined ones, a
visualization toolkit, and a benchmarking tool that allows automated
evaluation. We introduce and specify an evaluation protocol defining a range of
domain-related metrics computed on the basics of the primary evaluation
indicators (such as success rate and path length), allowing a fair multi-fold
comparison. The results of such a comparison, which involves a variety of
state-of-the-art MARL, search-based, and hybrid methods, are presented.
669f45cf19845ee054c3eb27
Alexey Skrynnik
admin_assigned
2024-07-23T07:59:32.661Z
652ced57756a15d750266362
/avatars/5ef33e4935ccc6e930ceb2475c270bb1.svg
Alexey Skrynnik
user
tviskaron
669f45cf19845ee054c3eb28
Anton Andreychuk
669f45cf19845ee054c3eb29
Anatolii Borzilov
669f45cf19845ee054c3eb2a
Alexander Chernyavskiy
669f45cf19845ee054c3eb2b
Konstantin Yakovlev
admin_assigned
2024-07-23T08:00:39.385Z
6616356e1788281d8fe2cc0c
https://cdn-avatars.huggingface.co/v1/production/uploads/6616356e1788281d8fe2cc0c/AK4gZYOMBXBcP4dNjZiib.jpeg
Konstantin Yakovlev
user
konstantin-yakovlev
669f45cf19845ee054c3eb2c
Aleksandr Panov
admin_assigned
2024-07-23T08:00:45.883Z
6633243f5ddb7702ad3ec216
/avatars/0abaf60f1a52e148c3f6fce57eb31eb4.svg
Aleksandr Panov
user
grafft
POGEMA: A Benchmark Platform for Cooperative Multi-Agent Navigation
Multi-agent reinforcement learning (MARL) has recently excelled in solving
challenging cooperative and competitive multi-agent problems in various
environments with, mostly, few agents and full observability. Moreover, a range
of crucial robotics-related tasks, such as multi-robot navigation and obstacle
avoidance, that have been conventionally approached with the classical
non-learnable methods (e.g., heuristic search) is currently suggested to be
solved by the learning-based or hybrid methods. Still, in this domain, it is
hard, not to say impossible, to conduct a fair comparison between classical,
learning-based, and hybrid approaches due to the lack of a unified framework
that supports both learning and evaluation. To this end, we introduce POGEMA, a
set of comprehensive tools that includes a fast environment for learning, a
generator of problem instances, the collection of pre-defined ones, a
visualization toolkit, and a benchmarking tool that allows automated
evaluation. We introduce and specify an evaluation protocol defining a range of
domain-related metrics computed on the basics of the primary evaluation
indicators (such as success rate and path length), allowing a fair multi-fold
comparison. The results of such a comparison, which involves a variety of
state-of-the-art MARL, search-based, and hybrid methods, are presented.
POGEMA provides a framework for learning and evaluating multi-agent reinforcement learning (MARL) methods, enabling fair comparisons with classical and hybrid approaches in tasks such as multi-robot navigation.
multi-agent reinforcement learning (MARL) multi-robot navigation obstacle avoidance classical methods learning-based methods hybrid methods fast environment problem instance generator visualization toolkit benchmarking tool evaluation protocol success rate path length