Chapter 27. Learning to Assess Weapons
Now that we've investigated different varieties of decision trees (DTs), we can use them to improve the weapon-selection behaviors. The main problem with the voting system is that it was tedious to set up and used theoretical assumptions to derive the weapon choice.
In this chapter, we focus the power of DT learning algorithms to resolve these problems. Notably, we'll be able to learn about the weapons by experience during the game, which prevents making unfounded assumptions. The learning may also reduce the effort needed to get a working behavior. Because we already have a voting system, however, we could take advantage of it.
This chapter covers the following topics:
At the end of the chapter, we'll have a fully working deathmatch bot that's not only capable of moving and shooting, but capable of making tactical decisions about what weapon to use.