Project NERO: An Experiment in Technology Transfer
The University of Texas at Austin
Digital Media Collaboratory/Innovation Creativity and Capital Institute
Neuroevolution Group/Department of Computer Sciences
In August 2003 the Digital Media Collaboratory (DMC) of the
Innovation Creativity and Capital Institute (IC2) at the
University of Texas held its second annual GameDev conference.
The focus of the 2003 conference was artificial intelligence, and
consequently the DMC invited several Ph.D. students from the
Neuroevolution Group at the University's Department of
Computer Science (UTCS) to make presentations on state-of-the-art
academic AI research with potential game applications.
The GameDev conference also held break-out sessions where
groups brainstormed ideas for innovative games, and in one of the
sessions Ken Stanley proposed an idea for a game based on a real-time
variant of his previously published NEAT learning algorithm.
On the basis of Ken's proposal the DMC/IC2 resolved to staff and
fund a project to create a professional-quality demo of the game.
(See production credits.)
The resulting NERO project started in October 2003
and has continued through the present, generating several spin-off
research projects in its wake.
As a result of the project we have imported the latest in AI research
from the UTCS Neuroevolution Group into a commercial game engine,
providing the DMC with a case study in technology transfer and a polished
demo of an entertaining game.
The NERO Game
Our novel experimental game is called NERO, which stands for
Neuro-Evolving Robotic Operatives.
It is set in a fictional post-apocalyptic world, where robots struggle
over the relics of human civilization.
Although it resembles some RTS games,
unlike most RTS games NERO consists of two distinct phases of play.
In the first phase individual players deploy robots in a 'sandbox' and
train them to the desired tactical doctrine.
Once a collection of robots has been trained, a second phase of play
allows players to pit their robots in a battle against robots
trained by some other player, to see how well their training regimens
prepared their robots for battle.
The training phase is the most innovative aspect of game play in NERO,
and is also the most interesting from the perspective of AI research.
Screenshot in training mode.
Here bipedal Enforcer-style NEROs are being trained in our Mountain Pass arena.
When a brain is installed in an Enforcer for training,
that robot enters play at a drop point near the crowded area at
the right, and the brain is scored on how well the robot performs
before the brain's timer expires.
These Enforcers have already been trained to approach
enemies; several can be seen crowded around the target labeled
E1 to the left, and many more are on their way.
Now the player is using the reward controls (lower right) to set up
for additional training that will keep this team from approaching
the enemy too closely.
Many other options are also available on the control panel.
(A similar object-placement control panel at the lower left has
been retracted to improve visibility.)
Training for complex tactical behaviors will require a player to think
out and implement a shaping plan, leading the robots through a series
of sandbox scenarios that guide them stepwise to the desired battlefield
doctrine. (See figure below.)
Example training regimen for a team of robots.
The player starts the naive robots in a simple environment
and progressively adds more powerful enemies and more complex
obstacles to the environment until the robots are experienced
veterans, ready to be deployed against another player's
In battle, you put your trained team to the test. You can play a battle
against the computer or against a human opponent on a local area network
or the internet. On some servers, the player can place a flag
interactively during the battle.
Screenshot in battle mode (detail).
Two trained teams are engaged in our Orchard arena,
where a long wall down the center separates the two teams'
starting positions but leaves room to go around either end.
The Red Team (right) was trained to face the enemy and fire
from a safe distance, and the Blue Team (left) was trained to
chase down enemies while navigating obstacles such as the wall.
Here the Blue Team has formed into single file while negotiating
the turn at the end of the wall, and now assaults the Red Team,
which has assumed a defensive posture.
The smoke is color-coded to indicate which team is firing;
the image shows that the Red Team's training has given it
the advantage of a concentration of fire in this situation.
Real-Time Neuroevolution with
The robots in NERO use artificial neural networks for their
and they learn by means of neuroevolution.
Neuroevolution is a genetic algorithm, a type of
reinforcement learning algorithm that operates by rewarding
the agents in a population that perform the best and punishing those
that perform the worst.
In NERO the rewards and punishments are specified by the players,
by means of the slider controls shown in the screenshot above, on the
The genetic algorithm decides which robots' "brains" are the most
and least fit on the basis of the robots' behavior and the current
settings of the sliders.
For the NERO project we are using a specific neuroevolutionary algorithm
called NEAT, Neuro-Evolution of Augmenting Topologies.
Unlike most neuroevolutionary algorithms, NEAT starts with an
artificial neural network of minimal connectivity and adds complexity
only when it helps solve a problem.
This helps ensure that the algorithm does not produce unnecessarily
In NERO we are introducing a new real-time variant
of NEAT, called rtNEAT, in which a small population evolves while you
(Most genetic algorithms use generation-based off-line processing, and
only provide a result at the end of some pre-specified amount of
rtNEAT solves several technical challenges.
For example, in order to allow continual adaptation, rtNEAT discards
the traditional notion of generations for the genetic algorithm, and
instead keeps a small population that is evaluated continuously, with
regular replacement of the poorest performers.
rtNEAT is powerful enough that we are able to work with a population as
small as 30 even for non-trivial learning tasks.
This allows the entire population to be replaced quickly enough for
human viewers to see the population's behavior adapt while they
We break up the regimented schedule of generation-based
evolution by imposing an artificial lifetime on each
"brain" being evaluated. In this example the brains control
individual agents in a simulation.
Whenever a brain's evaluation lifetime is up, we compare
its fitness (based on the agent's performance while
"wearing" that brain) to the fitness of other brains
in the population.
If it is among the least fit brains it is immediately
discarded and replaced by breeding two high-fitness brains
together using NEAT.
Otherwise the brain is put on the shelf, ready to be
inserted into another robot for further evaluation when
its turn comes up again.
To date we have worked through several layers of experiments to
validate the feasibility of rtNEAT and the NERO game concept, and
to iteratively enhance the learning power and resulting intelligence
of the NERO agents.
We are currently polishing up the game for a public release, and
after that we will start a new layer of AI experiments to enhance the
power of rtNEAT and the NERO agents' capabilities, and pursue other
research projects based on the NERO game.
Production of the NERO project is funded by the Digital Media
Collaboratory and the IC2 Institute of the University of Texas at
The original NEAT research was supported in part by the National Science
Foundation and the Texas Higher Education Coordinating Board.
NERO is built on the Torque game engine, licensed from