  We study how $N$ intelligent buses serving a loop of $M$ bus stops learn a
\emph{no-boarding strategy} and a \emph{holding strategy} by reinforcement
learning. The high level no-boarding and holding strategies emerge from the low
level actions of \emph{stay} or \emph{leave} when a bus is at a bus stop and
everyone who wishes to alight has done so. A reward that encourages the buses
to strive towards a staggered phase difference amongst them whilst picking up
people allows the reinforcement learning process to converge to an optimal
Q-table within a reasonable amount of simulation time. It is remarkable that
this emergent behaviour of intelligent buses turns out to minimise the average
waiting time of commuters, in various setups where buses have identical natural
frequency, or different natural frequencies during busy as well as lull
periods. Cooperative actions are also observed, e.g. the buses learn to
\emph{unbunch}.

Physics and Society