In this game the player controls the green agent, and evolved neural
networks control the NPCs, which are yellow (Fight mode) or red (Flight mode).
When the NPCs are yellow, they
try to attack the player while the player tries to hit them with a
bat. When the NPCs are red they must attack the player while
keeping it from escaping (the player no longer has a bat).
The challenge is to evolve multi-modal behavior for NPCs
so that they can accomplish both
In the movies below, the player is controlled by a static computer
strategy. In Fight mode, this bot constantly moves towards the nearest NPC in front
of it while swinging its bat. In Flight mode the bot moves backwards away from
the nearest NPC in front of it (away from what it can see
a. ModeMutation Result
ModeMutation is mutation operator for neuroevolution
that adds a new output mode to the output layer of a neural network
in order to encourage multi-modal behavior.
In the movie, the green bot plays the Fight or Flight game, and the NPCs try to kill the bot.
The NPCs have evolved several distinct output modes, and each number displayed on an NPC indicates
a different output mode. The NPCs use a baiting strategy against the bot. Notice that the output
mode of the NPCs chasing the bot is 0, but that the output mode of the bait alternates between
1 and 2.
When the NPCs become red, they are in the Flight task. Here the NPCs generalize the mode 0
behavior to work in the Flight task as well. They have to effectively corral the bot to keep
it from escaping.
b. 1Mode Result 1
This shows the comparatively poor performance of the 1Mode method (only one network mode).
Each network in the population tends to specialize in certain objectives,
but none does particularly well in all of them.
These NPCs develop a boring behavior for the Fight trial that avoids all contact with the bot.
They do no damage to it, though they also take no damage and manage to live through the whole trial.
However, the NPCs do well in the Flight trial that follows and exhibit corralling behavior.
Doing damage in the Fight trial is sacrificed for the sake of the other objectives,
thus putting this individual near the edge of the trade-off surface between objectives.
c. 1Mode Result 2
Another movie showing how the 1Mode method does not simultaneously optimize all objectives.
This network meets the goal of killing the bot once (50 damage),
but all NPCs are killed!
Also, in the Flight trial following the Fight trial, the NPCs perform very poorly and let the bot escape very quickly.
NPCs in this same 1Mode population that actually did well in the Flight task almost invariably did poorly in the Fight
task. This is how the population as a whole could reach the required goals in terms of average performance,
yet still fail to contain individuals that do well in all objectives.