title: POGEMA: A Benchmark Platform for Cooperative Multi-Agent Navigation
summary: 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.
paper_authors._id: 669f45cf19845ee054c3eb27
paper_authors.name: Alexey Skrynnik
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-23T07:59:32.661Z
paper_authors._id: 652ced57756a15d750266362
paper_authors.avatarUrl: /avatars/5ef33e4935ccc6e930ceb2475c270bb1.svg
paper_authors.fullname: Alexey Skrynnik
paper_authors.type: user
paper_authors.user: tviskaron
paper_authors._id: 669f45cf19845ee054c3eb28
paper_authors.name: Anton Andreychuk
paper_authors._id: 669f45cf19845ee054c3eb29
paper_authors.name: Anatolii Borzilov
paper_authors._id: 669f45cf19845ee054c3eb2a
paper_authors.name: Alexander Chernyavskiy
paper_authors._id: 669f45cf19845ee054c3eb2b
paper_authors.name: Konstantin Yakovlev
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-23T08:00:39.385Z
paper_authors._id: 6616356e1788281d8fe2cc0c
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paper_authors.fullname: Konstantin Yakovlev
paper_authors.type: user
paper_authors.user: konstantin-yakovlev
paper_authors._id: 669f45cf19845ee054c3eb2c
paper_authors.name: Aleksandr Panov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-23T08:00:45.883Z
paper_authors._id: 6633243f5ddb7702ad3ec216
paper_authors.avatarUrl: /avatars/0abaf60f1a52e148c3f6fce57eb31eb4.svg
paper_authors.fullname: Aleksandr Panov
paper_authors.type: user
paper_authors.user: grafft
paper_title: POGEMA: A Benchmark Platform for Cooperative Multi-Agent Navigation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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