Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas

Key points

• We ask how such patterns emerge when agents learn where to move and how learning rates shape their consequences for collective welfare.1
• Fixed populations of cooperators and defectors independently learn movement policies using tabular Q-learning and local observations.1
• Cooperator learning generates clusters around resource peaks, while co-adaptation changes their strength and motion.1
• These results connect learning rates to the emergence and welfare costs of spatial organization driven by individual rewards.1

Sources (1)

• [1]Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 05:03 PM

  Spatial public goods models show that prescribed movement toward richer locations can generate spatial patterns.

  We ask how such patterns emerge when agents learn where to move and how learning rates shape their consequences for collective welfare.

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