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================================================================================
NEURAL GUPPIES -v0.1 beta
Author: Paul T. Oliver (paul.t.oliver.design@gmail.com)
================================================================================
================================================================================
Index:
I. Disclaimer
II. Usage instructions
III. Parameters
IV. Controls
================================================================================
================================================================================
I. DISCLAIMER:
Neural Guppies is a NeuroEvolution experiment that evolves virtual creatures
into intelligent beings. It is a personal project aimed to test several
concepts that interest me, so I thought I should share it with the
community. However, it has no guaranty and has not been tested in any other
system but mine. Thus, if you run this on your system, you must do it at your
own risk. To see a brief introduction on Neural Guppies, visit the following
links:
http://www.youtube.com/watch?v=tCPzYM7B338
http://www.youtube.com/watch?v=N-HjfS4P6r4
http://www.youtube.com/watch?v=X7qUH8mSyUE
http://forum.codecall.net/topic/72637-goopies-evolving-neural-networks-wip/
Program is far from finished and can only begin Simulations from scratch
(loading and saving simulations will be implemented soon). If you close the
window the simulation will be lost. I've been able to successfully resume
simulations after putting the computer to Sleep or Hibernate, (without
closing the simulation window) but this may not work on all systems.
Main concepts tested on this program are:
- Artificial Neural Networks
- Genetic Algorithms
- Neuro-evolution
================================================================================
================================================================================
II. USAGE INSTRUCTIONS:
1) Extract NeuralGuppies-v0.1beta.zip to your preferred directory.
2) To begin a new simulation simply run Guppies.exe.
3) Click on NEW SIMULATION.
4) Set simulation parameters (see below to see what each parameter means).
5) Click on Begin new simulation.
================================================================================
================================================================================
III. PARAMETERS:
The program comes with default parameters built in. You should use those as a
starting point and experiment by tweaking them as much as you like. No control
is set to the inputed parameters, so you must take care that they make sense to
the program (for example, you should not make a world so small that objects
don't fit on it!).
What follows is a list of all the tweak-able parameters and what they mean.
After the name of each numerical parameter is the type of number that it
expects (positive integer, integer, positive floating point or floating point).
My recommendation is to change them in small gaps you you can see how each of
them affects the simulation.
1) NETWORK STRUCTURE:
Defines the main topology of the Neural Network (i.e. the way that Nodes
connect with each other). The four possible choices (in order of
complexity) are:
a) Single MultiLayer Perceptron:
A FeedForward network with 1 hidden layer. Information flows
unidirectionally from input nodes to output nodes (and not the
other way around).
b) Dual MultiLayer Perceptron:
Same as above, but with two hidden layers instead of one.
c) Simple Recurrent Network (Elman Network):
This type of recurrent network has a set of "context" units that
store the output of the (single) hidden layer and feeds it back
to the input layer on the next time-step, giving it a kind of
"temporal" perception.
d) Fully Recurrent Network:
All Nodes in this network are connected to each other. It's the
most complex (and processor intensive) network of the four.
2) NODE STRUCTURE:
Defines the structure of each network node. The two choices are:
a) Neuron:
Each neuron computes its outputs from a given set of inputs. Output
equals the weighted sum of all inputs.
b) Memory Cell:
This kind of node is based in the Long-Short Term Memory recurrent
network model. It contains 4 neurons, 3 of them act as "gates" that
allow it to block input, store it and output it, thus being able to
hold in information or "memories" for a long time span. It's the
most complex (and processor intensive) type of node.
3) NODES PER HIDDEN LAYER (expects a positive integer):
Number of nodes that reside on each hidden layer. SingleMLPs, SimpleRNs and
FullyRNs have one hidden layer. DualMLPs have two (thus, their number of
hidden nodes is "this value" x 2). The more nodes, the more complex the
Neural Networks of the Guppies are (and the more time it'll take to evolve
them) but the more "intelligent" they can become (theoretically).
4) SUB POPULATION SIZE (expects a positive integer):
Populations of Guppies are divided into sub-populations of individuals that
are placed on the Dish simultaneously. If you experience a slow frame rate,
you can make this sub-populations smaller in order to reduce processor
overhead. However, the bigger the sub-populations, the more chance the
Guppies have to interact with each other.
5) SUB POPULATION QUANTITY (expects a positive integer):
Number of sub-populations (or population divisions). Thus, total population
of Guppies equals "sub-population qtty" x "this number". Larger populations
give the genetic algorithm more information (genes) to mix, thus they will
likely result in more "intelligent" Guppies. However, the larger the
population, the more cycles (time) the GA will need in order to evolve
intelligence.
6) FITNESS BONUSES (expects a positive floating point value):
Bonus fitness points each Guppy receives for eating a food pellet (remember
that the more fitness points a Guppy makes during its life, the more
chance it has to reproduce).
7) FITNESS BONUS PER GUPPY (expects a positive floating point value):
Bonus fitness points each Guppy receives for attacking another Guppy.
8) FITNESS BONUS PER CORPSE max (expects a positive floating point value):
Bonus fitness points each Guppy receives for eating a corpse. Since corpses
"decay" this is the MAX value of a "fresh" corpse.
9) DISH SIZE (expects a positive floating point value):
Size of the world.
10) ZAPPER RANDOM FORCE (expects a positive floating point value):
Random movement force applied to Zappers each frame.
11) ZAPPER RANDOM TORQUE (expects a positive floating point value):
Random rotation force applied to Zappers each frame.
12) ZAPPER QUANTITY (expects a positive integer):
Number of Zappers (does not change).
13) PELLET QUANTITY (expects a positive integer):
Max number of food pellets that can exist on any given time.
14) PELLET CREATION DELAY (expects a positive integer):
Time it takes (in frames) for a new Pellet to be spawned in the world.
15) START SCARCE:
Begin simulation with no Pellets in the world.
16) CORPSE DECAY:
Corpses have a limited lifetime after they are created, after which they
disappear.
17) CORPSE DECAY RATE (expects a positive integer):
Time it takes (in frames) for a corpse to decay.
18) COLORS:
Colors of the entities in the world. Remember that Guppies "see" in three
colors (RED, GREEN and BLUE). Entities with similar colors would be
difficult to distinguish for a Guppy.
19) ACTIVATION DELAY (expects a positive integer):
Time it takes for a Guppy to pass from being an "Egg" to being an adult.
It's important to leave this value a bit high (above 100) so to prevent a
Guppy from being born too near to another entity.
20) THRUST FORCE (expects a positive floating point value):
Strength of Guppy thrust force. The higher the force, the higher the speed.
21) THRUST RADIUS (expects a positive floating point value):
The higher this value, the faster the Guppies can "turn". Too high they
might not be able to control themselves. Default value provides a good
balance.
22) INITIAL ENERGY (expects a positive floating point value):
Initial energy Guppies begin their life with. Each "energy point"
represents at most one second (60 frames) of life.
23) MAX ENERGY (expects a positive floating point value):
Maximum energy points a Guppy can have at any given time.
24) AGING RATE (expects a positive integer):
Rate at which Guppies "age". Aging for a Guppy means that it consumes
energy points faster (thus, it gets harder to stay alive the longer a Guppy
lives). This value being "x", each Guppy consumes 1 additional energy point
per second every "x" seconds it has lived. In other words, the smaller this
value is, the faster Guppies age.
25) LEAVE CORPSE:
Guppy leaves a corpse behind when it dies.
26) ENERGY TRANSFERS (expects floating point values):
Quantity of energy points transfered from entity to Guppy during collision.
Zappers should have a negative value so that they "steal" energy from the
Guppies whenever they touch. Other entities transfer their energy whenever
a Guppy attacks them head on (i.e. when they hit them with their frontal
"beak"). This should be made positive, so that they give the Guppy
additional lifetime.
================================================================================
================================================================================
IV. CONTROLS:
Once you hit the "Begin new simulation" button the parameter window will close
and the simulation window will pop up.
You can resize, minimize or maximize this window at any time, but if you close
it the simulation will be lost! Next is a list of controls so you can navigate
around the Dish and take a close look at th Guppies:
I) Zoom in.
O) Zoom out.
Z) Zoom out completely.
W) Shift camera up.
A) Shift camera left.
S) Shift camera down.
D) Shift camera right.
F11) Switch to Fullscreen.
SPACE) Pause simulation.
V) Turn on/off vSync (when turned off, rendering will be done at max speed).
G) Turn on/off graphics (simulation goes faster when graphics are turned off).
T) Turn on/off text display.
MOUSE WHEEL UP/DOWN) Zoom in/out on cursor position.
MOUSE CLICK) Shift camera to cursor position. If you click on an entity, that
will cause the camera to "follow" it around.
* To accelerate the simulation to the max you should disable vSync, Graphics
and text rendering all at once. That way evolution can happen faster while
you're not directly looking at it.
================================================================================
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