title: MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent
    Reinforcement Learning
summary: Many challenging tasks such as managing traffic systems, electricity grids,
  or supply chains involve complex decision-making processes that must balance
  multiple conflicting objectives and coordinate the actions of various
  independent decision-makers (DMs). One perspective for formalising and
  addressing such tasks is multi-objective multi-agent reinforcement learning
  (MOMARL). MOMARL broadens reinforcement learning (RL) to problems with multiple
  agents each needing to consider multiple objectives in their learning process.
  In reinforcement learning research, benchmarks are crucial in facilitating
  progress, evaluation, and reproducibility. The significance of benchmarks is
  underscored by the existence of numerous benchmark frameworks developed for
  various RL paradigms, including single-agent RL (e.g., Gymnasium), multi-agent
  RL (e.g., PettingZoo), and single-agent multi-objective RL (e.g.,
  MO-Gymnasium). To support the advancement of the MOMARL field, we introduce
  MOMAland, the first collection of standardised environments for multi-objective
  multi-agent reinforcement learning. MOMAland addresses the need for
  comprehensive benchmarking in this emerging field, offering over 10 diverse
  environments that vary in the number of agents, state representations, reward
  structures, and utility considerations. To provide strong baselines for future
  research, MOMAland also includes algorithms capable of learning policies in
  such settings.
paper_authors._id: 66a11c4a858a536116ce72a0
paper_authors.name: Florian Felten
paper_authors.status: claimed_verified
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paper_authors.fullname: Florian Felten
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paper_authors.name: Umut Ucak
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paper_authors.fullname: Umut Ucak
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paper_authors.user: jetimam
paper_authors._id: 66a11c4a858a536116ce72a2
paper_authors.name: Hicham Azmani
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paper_authors.fullname: HIcham Azmani
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paper_authors.user: hiazmani
paper_authors._id: 66a11c4a858a536116ce72a3
paper_authors.name: Gao Peng
paper_authors._id: 66a11c4a858a536116ce72a4
paper_authors.name: Willem Röpke
paper_authors.status: admin_assigned
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paper_authors.fullname: Willem Röpke
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paper_authors.user: WillemRopke
paper_authors._id: 66a11c4a858a536116ce72a5
paper_authors.name: Hendrik Baier
paper_authors._id: 66a11c4a858a536116ce72a6
paper_authors.name: Patrick Mannion
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-25T09:10:09.636Z
paper_authors._id: 64a17b672597e760a71d555c
paper_authors.avatarUrl: /avatars/9668806dfd7c3430a37575bf7a3346b2.svg
paper_authors.fullname: Patrick Mannion
paper_authors.type: user
paper_authors.user: pmannion
paper_authors._id: 66a11c4a858a536116ce72a7
paper_authors.name: Diederik M. Roijers
paper_authors._id: 66a11c4a858a536116ce72a8
paper_authors.name: Jordan K. Terry
paper_authors._id: 66a11c4a858a536116ce72a9
paper_authors.name: El-Ghazali Talbi
paper_authors._id: 66a11c4a858a536116ce72aa
paper_authors.name: Grégoire Danoy
paper_authors._id: 66a11c4a858a536116ce72ab
paper_authors.name: Ann Nowé
paper_authors._id: 66a11c4a858a536116ce72ac
paper_authors.name: Roxana Rădulescu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-25T07:38:48.878Z
paper_authors._id: 66a132d5e5b6ab831c8700a4
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paper_authors.fullname: Roxana Rădulescu
paper_authors.type: user
paper_authors.user: rradules
paper_title: MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent
    Reinforcement Learning
paper_summary: Many challenging tasks such as managing traffic systems, electricity grids,
  or supply chains involve complex decision-making processes that must balance
  multiple conflicting objectives and coordinate the actions of various
  independent decision-makers (DMs). One perspective for formalising and
  addressing such tasks is multi-objective multi-agent reinforcement learning
  (MOMARL). MOMARL broadens reinforcement learning (RL) to problems with multiple
  agents each needing to consider multiple objectives in their learning process.
  In reinforcement learning research, benchmarks are crucial in facilitating
  progress, evaluation, and reproducibility. The significance of benchmarks is
  underscored by the existence of numerous benchmark frameworks developed for
  various RL paradigms, including single-agent RL (e.g., Gymnasium), multi-agent
  RL (e.g., PettingZoo), and single-agent multi-objective RL (e.g.,
  MO-Gymnasium). To support the advancement of the MOMARL field, we introduce
  MOMAland, the first collection of standardised environments for multi-objective
  multi-agent reinforcement learning. MOMAland addresses the need for
  comprehensive benchmarking in this emerging field, offering over 10 diverse
  environments that vary in the number of agents, state representations, reward
  structures, and utility considerations. To provide strong baselines for future
  research, MOMAland also includes algorithms capable of learning policies in
  such settings.
paper_ai_summary: MOMAland introduces standardized environments for multi-objective multi-agent reinforcement learning, offering diverse settings and benchmarking algorithms.
paper_ai_keywords: multi-objective multi-agent reinforcement learning MOMARL reinforcement learning RL multi-agent RL single-agent RL MO-Gymnasium Gymnasium PettingZoo state representations reward structures utility considerations