Abstract
In standard neuro-evolution, a population of networks is evolved in
a task, and the network that best solves the task is found. This
network is then fixed and used to solve future instances of the problem.
Networks evolved in this way do not handle real-time interaction
very well. It is hard to evolve a solution ahead of time that can
cope effectively with all the possible environments that might arise
in the future and with all the possible ways someone may interact
with it. This paper proposes evolving feedforward neural networks
online to create agents that improve their performance through real-time
interaction. This approach is demonstrated in a game world where
neural-network-controlled individuals play against humans. Through
evolution, these individuals learn to react to varying opponents
while appropriately taking into account conflicting goals. After
initial evaluation offline, the population is allowed to evolve online,
and its performance improves considerably. The population not only
adapts to novel situations brought about by changing strategies in
the opponent and the game layout, but it also improves its performance
in situations that it has already seen in offline training. This
paper will describe an implementation of online evolution and shows
that it is a practical method that exceeds the performance of offline
evolution alone.
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