Maze statistics

After running an example integration, say acs2_in_maze.py, here’s what the output tells you:

Agent stats

See lcs.agents.acs2.ACS2

  • population: number of classifiers in the population
  • numerosity: sum of numerosities of all classifiers in the population
  • reliable: number of reliable classifiers in the population
  • fitness: average classifier fitness in the population
  • trial: trial number
  • steps: number of steps in this trial
  • total_steps: number of steps in all trials so far

Environment stats

There are currently no environment statistics for maze environment.

Performance stats

  • knowledge: As defined in examples.acs2.maze.utils.calculate_performance(): If any of the reliable classifiers successfully predicts a transition, we say that the transition is anticipated correctly. This is a percentage of correctly anticipated transitions among all possible transitions.