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 populationnumerosity
: sum of numerosities of all classifiers in the populationreliable
: number of reliable classifiers in the populationfitness
: average classifier fitness in the populationtrial
: trial numbersteps
: number of steps in this trialtotal_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 inexamples.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.