pop-forum Poporacer -- and apologies for spam forwarded by mistake
Aaron Sloman
pressing the wrong button, causing a spam advertisement for summer
vacations to be forwarded a few minutes ago.
While I am writing, some news: A few years ago a group of
Undergraduates developed a program using Pop-11 called 'popracer'
for their second year project. It is described by Mark Rowan, one of
the participnats, in this article:
http://www.tamias.co.uk/popracer
Here is an extract:
"The brief was simple: can embodied agents in a simulation teach
themselves to drive a car?
The agents are modelled by neural networks, each in control of a
simulated vehicle on a pre-drawn racetrack. There exists a basic
modelling of Newtonian physics, to control such aspects as
acceleration and friction, and a series of waypoints are drawn along
the course of the track. The inputs to the neural networks consist
of the speeds of the left and right wheels of the car, the car's
current heading, a choice of which waypoint to observe, and the
bearing to the chosen waypoint; essentially, these are equivalent
input sources to those you could derive from real-world GPS
co-ordinates on a waypointed track.
At this initial level, running the simulation causes a variety of
different behaviours: spinning, looping, driving in reverse,
crashing into barriers -- and invariably not progressing very far
along the track at all. The neural networks obviously require some
training.
Instead of performing supervised training by a human driver via
backpropagation, the cars are given a set amount of time in which
they can do whatever they rather obliviously choose to do. After a
certain time has elapsed, the grim reaper visits and culls all the
agents, with the exception of the lucky two which have strayed by
chance further along the track than their peers. The neural networks
of these two agents are then combined using a genetic algorithm, and
the resulting offspring replace the other cars which were less
successful.
Within a few generations it can be seen that the cars are learning a
preference for driving along the general direction of the track,
even taking account of the bends in the track and adjusting their
turning speeds accordingly. By this time the race is on, and the
fastest two cars of each epoch are selected. The agents learn to cut
the corners in order to better their race times -- but not by too
much, or they will crash off the track and get stuck -- and before
long, intelligent acceleration and deceleration through corners is
observed. After approximately 15 epochs the cars are not only
optimising their driving styles, but are taking advantage of the
slipperiness of the road and actually power-sliding through tight
corners."
The source code, user guide and full project report, including
pictures of the race tracks (which can be generated using a pop-11
graphical tool developed in the project) are all available online
here:
http://www.cs.bham.ac.uk/research/projects/poplog/popracer/
or in a tar ball
http://www.cs.bham.ac.uk/research/projects/poplog/popracer.tar.gz