congested: learning in congestion games

what are the features of the package that are most important?

• the many implemented environments specific to game theory
• the implementation of Q-learning with easy extensions of its variants
• internal knowledge of expected game Nash Equilibria
• standard parameters implemented as defaults of the games

steps to make this a package

• use pip requirements, and make sure only the necessary requirements are present
• use dataclasses to achieve modularity, where possible, needed for:
  • run simulation code to know which parameters it needs, e.g.
    • duopoly requires states but congestion does not
    • public goods game takes a multiplier
    • network congestion games take parameters for each edge
  • multiprocessing sweeps to know necessary parameters
  • plotting of relevant variables and parameters after simulation
• documentation of important functions
• plotting functions
  • welfare over time
  • action distribution over time
  • vector field plot, simplex
  • q values plots
• setuptools toml file
• package structure