The purpose of this project is to
train an Artificial Neural Network to control a vehicle around a racing
The track is defined by a few basic waypoints
which are interpolated using splines to create a smooth track.
The user drives the car around the track and the collected information
is used to train the neural network.
After the neural network is trained we let it control the vehicle.
The projects is written in C++ (MSVC6)
and uses DirectX 8 for the graphics.
(Open Dynamics Engine) is used for the car physics.
The track is rendered as a single textured triangle strip and the car model
is rendered as a D3DXMesh.
There's also a simple sky box and several camera angles to make things
Download the demo: NeuroDriver220.127.116.11.zip
- Windows 98/ME/XP/2000
- DirectX 8
- 600Mhz CPU
- 64MB RAM
- 32MB Graphics Accelerator
The main idea for this project is to develop
a generic, neural-network based, game AI for vehicle
command and control (cars, tanks, aircraft, mechs, etc.)
The 2 main classes of the project
are Driver and
class encapsulates the artificial neural-network (or networks) that
make up the "Brain"
That controls the Vehicle.
class is an abstract base class from which all vehicles are inherited
Each Vehicle class can be controlled
by several drivers (objects of the Driver
Each Driver is assigned a place
(station) in the Vehicle
With this architecture it is possible
to simulate a vehicle with several sets of controls.
For example, if we would like to simulate a tank - first, we inherit
a Tank class from the Vehicle
Then we create 3 different Driver
objects and assign each one to a different Tank
station (see diagram)
We can then train each Driver
to control the specific systems of its station according to
From the Tank object.
For the Driver
to control the Vehicle, the Driver
needs to perform the following actions:
1) Get data from the Vehicle
(e.g. speed, position of enemy targets, etc.)
2) Think (process
the data and decide how to manipulate the Vehicle's
3) Set the Vehicle
In code it looks something like
// Get data from the vehicle according to the Driver's assigned station
data = pVehicle->getData( stationID );
// Think (this is where the neural-network does its work)
controls = processData( data );
// Set the appropriate vehicle controls
pVehicle->setControls( controls, stationID );
The data retrieved
from the Vehicle is used as the
inputs to the neural-network.
These inputs are then propagated through the neural-network (forward
The outputs of the neural-network are used
for setting the controls of the Vehicle.
If we take the NeuroDriver
demo as an example, we use the car's speed,
heading factors and turn factor
as the inputs for the neural-network, and the outputs of the network
are used to set the
steering and throttle of the car (see drawing) :
With the NeuroDriver architecture
it is also possible to use more then a single
network as the Driver's
Each Driver class contains a Brain
class and each Brain class can
contain one or more BrainLobes.
Each BrainLobe class encapsulates
a single neural-network.
This gives us the ability to use more then a single-network for controlling
For example, we can use one neural-network to control the steering and
another neural-network to control the throttle.
The physics engine in NeuroDriver
consists of 2 main classes: PhysicsServer
The PhysicsServer class is responsible
for the world physics (e.g. gravity).
It is a singleton, only one instance of this class can exist in the
Every entity in the simulation that would like
to use physics is considered a PhysicsClient.
Each PhysicsClient must register
itself with the PhysicsServer
during initialization in order to become
Part of the simulation.
The PhysicsServer is responsible
for supplying the physics clients with the global world information
For updating each client every simulation frame.
The PhysicsClient class is an
abstract class, so we need to inherit a new class from it and implement
Inside this new class.
For example, the car physics in the NeuroDriver
demo inherit a CarPhysics
class from PhysicsClient.
class implements the actual car physics, it updates the car state according
to the forces
Applied on the car in each simulation frame.
The demo uses the ODE
(Open Dynamics Engine) physics library to simulate the car
Following is a diagram of the core
The graphics in the NeuroDriver
demo are very basic.
The entire scene is rendered using a static DX8 vertex-buffer (skybox,
And a single D3DXMesh (car) which uses its own vertex-buffer.
The text (fps counter, simulation data, etc.) is rendered using a D3DXFont.