<lmargin%:5><rmargin%:95><font:Arial Bold:20>Training Mode... continued...
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<spush><font:Arial Bold:16>How to Train<spop>
Note that in order to train an army, a factory must be created by selecting "Launch Team" from the context menu. Without a factory on the field, you will have no troops to train.

Your army's behavior is determined through the slider values you set in the slider window (the panel on the lower right of your screen). There are 6 different objectives (see appendix C) you can set. If you move your mouse over the icons near the top of the window, an info box will pop up on what slider you are currently looking at.  The area below the icons holds the sliders themselves. Moving the slider up (into the green) will reward the population for doing the current objective, while moving the slider down (into the red) will punish the population for doing the current objective. Two vertical white sliders to the left of the evolution objectives allow you to change the distance your robots will want to keep from enemies and their lifetime on the field.  For simpler tasks, you will want it shorter. Note that in early training it is not a good idea to start with numerous sliders all set at the same time.  Try starting by changing one or two sliders, and then gradually augment more sliders as your training objectives improve.

<spush><font:Arial Bold:16>Training Tips (Important)<spop>
The soldiers you are training are fresh recruits.  They do not initially know anything about the world around them, and thus wander aimlessly around. Although it may be tempting to design a complicated scenario, max out the slider values, and wait, such an approach is likely to fail.  The soldiers, like any students, need to learn simpler building blocks before more complicated tasks. The best way to train soldiers is to train them incrementally. For example, if you want to train your army to attack an enemy behind a wall, first place an enemy on the field and reward the population for hitting the target and approaching the enemy. After you are satisfied with those results, place a wall in between the spawn point and enemy so that the population has to learn to go around the wall to get to the enemy. (Note that the Tutorial mode guides you through such a learning scenario.)  When you are satisfied with those results, maybe switch out the enemy with a turret, and punish getting hit. Or you could add another wall. The choice is yours depending on your overall team strategy.

All scenarios and strategies you wish to train for should be set up in this incremental fashion in order to yield good armies. A good rule of thumb is to change one slider value and/or one environmental stimulus (enemy, wall, etc), and wait for your army to learn it before changing something else.  That way, training is gradual and realistic.  In this way, it is possible to gradually scale up to quite sophisticated behaviors.

Training takes time. Do not immediately expect the soldiers to learn the given task at hand. Wait a few minutes, and if the soldiers still are not giving the desired results, then review your slider settings and overall scenario. Perhaps try to learn something simpler that can serve as a foundation for more complex tasks.

<spush><font:Arial Bold:16>Stat Display and the Space Bar<spop>
It is possible to observe certain stats about your individual robots by pressing the space bar.  By default, the robots have no stats above their heads, but by pressing spacebar you can cycle through a number of different stats that appear above their heads. In addition, the HUD button (on the bottom panel) becomes active when stats are being displayed (see Appendix D for info on the HUD).  Space bar cycles through the following states (pressing spacebar in the last state cycles the stats back to being off):

<spush><font:Arial Bold:16>1. Brain ID's:<spop>  This is the current id of a unit's brain.  Every time a new brain is inserted into a robot body, it gets a new number, so the numbers give you an idea of how new the robots' brains are.  In general, older brains only stay around if they are still good compared to the newer ones.   You can use these brain ID's later to identify individuals in the Create Team mode.

<spush><font:Arial Bold:16>2. Species Number:<spop>  Tells which species each robot belongs to.  See Section VI for pointers to NEAT documentation, which explains how speciation works with the NEAT technology that is inside NERO.  Roughly, within each species are similar brains with similar styles, but the brains differ between different species.

<spush><font:Arial Bold:16>3. Current Fitness:<spop> Shows the internal number NERO is using to rate different individuals

<spush><font:Arial Bold:16>4. Fitness Rank:<spop>  Shows the current rank of every individual from 1 (the best) to n (the worst) where n is the number of robots on the field.

<spush><font:Arial Bold:16>5. Neural Net Complexity:<spop>  The rtNEAT method used by NERO actually evolves networks that increase in complexity as the game is played.  This display mode shows you the number of nodes (N), the number of links/connections (L), and also the species number (S) of each individual.  The first two numbers give you a sense how big their brains are getting.  The last number tells you how that correlates to different species. Note that complexity may vary at any given time among your troops.

<spush><font:Arial Bold: 16>6. Highlighted Champ:<spop> Shows the word "CHAMP" above the head of the robot that is currently doing highest above average in the fitness metrics you specified with the objective sliders. The performance and the network of this champ can be seen by opening the graphs in the upper left corner of the screen.

<spush><font:Arial Bold:16>Graphs<spop>
In order to see what is happening in your training, you can look at the fitness history and at the neural network of the champion robots. To do so, click on the "Graphs" button in the upper left of the training screen, and use the arrows to cycle through available graphs. There are a number of fitness graphs (one for each fitness metric in the slider control, and one that combines them all), and three different network graphs. 

The fitness graph has two lines. The bright line represents the current champion's score in the category. The darker line represents the overall trend averaged over the entire window.

The network graphs allow you to see the artificial neural network "brain" of the current champion. It is a visual representation of the actual computational controller that is driving the current champion. The first network graph shows the topology (the nodes and the connections) of the neural network. It also shows the weights of the connections by coloring them from highly negative (bright red) to highly positive (bright green), with black standing for low weight connections. The second graph shows just the topology without coloring the connections so you can clearly see even the dark lines. The third network graph shows just the weights on a matrix. A colored square (from red/negative to black/zero to green/positive) in the grid represents an active connection between row-indexed and column-indexed node in the network.

<spush><font:Arial Bold:16>Training Maps<spop>
The map you train on can have a significant effect on the kind of soldiers you produce.  In some cases, you may want to train soldiers in the same map in which they will go to battle.  New configurations of walls that you create yourself can be saved in your own custom training map files by pressing the "Save Map" button. You can reload maps later.  That way, if you for example created a maze and your robots are not done training but you need to turn off your computer, you can save your team, save the map, and exit safely.  When you restart training choose the new map that you saved.  NERO comes with a variety of maps for both training and battle.  It is possible to use any of the maps for both purposes.

<spush><font:Arial Bold:16>Saving your Army and Wall Configuration<spop>
You can save your current army by pressing the "Save" button on the bottom panel and choosing a name for your team in the resulting dialog box.

You can save your current wall configuration by pressing the "Save Map" button on the bottom panel and choosing a name for your map in the resulting dialog box.

<spush><font:Arial Bold:16>Tutorial<spop>
To learn more about training in a hands-on environment, select "Tutorial" from the main menu and follow the step-by-step beginner's guide to training your army to go around a wall. The advanced tutorial treats more high level topics.