title: Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning
summary: Offline multi-agent reinforcement learning (MARL) is an exciting direction of
  research that uses static datasets to find optimal control policies for
  multi-agent systems. Though the field is by definition data-driven, efforts
  have thus far neglected data in their drive to achieve state-of-the-art
  results. We first substantiate this claim by surveying the literature, showing
  how the majority of works generate their own datasets without consistent
  methodology and provide sparse information about the characteristics of these
  datasets. We then show why neglecting the nature of the data is problematic,
  through salient examples of how tightly algorithmic performance is coupled to
  the dataset used, necessitating a common foundation for experiments in the
  field. In response, we take a big step towards improving data usage and data
  awareness in offline MARL, with three key contributions: (1) a clear guideline
  for generating novel datasets; (2) a standardisation of over 80 existing
  datasets, hosted in a publicly available repository, using a consistent storage
  format and easy-to-use API; and (3) a suite of analysis tools that allow us to
  understand these datasets better, aiding further development.
paper_authors._id: 66ec6edce65d94c88ebb0543
paper_authors.name: Claude Formanek
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Claude Formanek
paper_authors.type: user
paper_authors.user: claudef
paper_authors._id: 66ec6edce65d94c88ebb0544
paper_authors.name: Louise Beyers
paper_authors.status: extracted_pending
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paper_authors._id: 6638bbea8bd9205c326e8553
paper_authors.avatarUrl: /avatars/4bb695a8f51e6ba17448c748280ebf14.svg
paper_authors.fullname: Louise Beyers
paper_authors.type: user
paper_authors.user: LBey
paper_authors._id: 66ec6edce65d94c88ebb0545
paper_authors.name: Callum Rhys Tilbury
paper_authors.status: extracted_pending
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paper_authors.fullname: Callum Rhys Tilbury
paper_authors.type: user
paper_authors.user: callumtilbury
paper_authors._id: 66ec6edce65d94c88ebb0546
paper_authors.name: Jonathan P. Shock
paper_authors._id: 66ec6edce65d94c88ebb0547
paper_authors.name: Arnu Pretorius
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-20T07:46:03.083Z
paper_authors._id: 63c03b1fdd793d5a62836bca
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paper_authors.fullname: Arnu Pretorius
paper_authors.type: user
paper_authors.user: Arnu
paper_title: Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning
paper_summary: Offline multi-agent reinforcement learning (MARL) is an exciting direction of
  research that uses static datasets to find optimal control policies for
  multi-agent systems. Though the field is by definition data-driven, efforts
  have thus far neglected data in their drive to achieve state-of-the-art
  results. We first substantiate this claim by surveying the literature, showing
  how the majority of works generate their own datasets without consistent
  methodology and provide sparse information about the characteristics of these
  datasets. We then show why neglecting the nature of the data is problematic,
  through salient examples of how tightly algorithmic performance is coupled to
  the dataset used, necessitating a common foundation for experiments in the
  field. In response, we take a big step towards improving data usage and data
  awareness in offline MARL, with three key contributions: (1) a clear guideline
  for generating novel datasets; (2) a standardisation of over 80 existing
  datasets, hosted in a publicly available repository, using a consistent storage
  format and easy-to-use API; and (3) a suite of analysis tools that allow us to
  understand these datasets better, aiding further development.
paper_ai_summary: Research in offline multi-agent reinforcement learning lacks a standardized approach to data generation and usage, which is addressed by providing a guideline, standardization, and analysis tools for datasets.
paper_ai_keywords: multi-agent reinforcement learning offline MARL static datasets optimal control policies data-driven data methodology dataset characteristics algorithmic performance dataset usage data awareness dataset generation dataset repository storage format API analysis tools