[docs] Fix typo. (#2260)

docs/Background-Machine-Learning.md

 [Learning a policy](https://blogs.unity3d.com/2017/08/22/unity-ai-reinforcement-learning-with-q-learning/)
 usually requires many trials and iterative policy updates. More specifically,
 the robot is placed in several fire situations and over time learns an optimal
-policy which allows it to put our fires more effectively. Obviously, we cannot
+policy which allows it to put out fires more effectively. Obviously, we cannot
 expect to train a robot repeatedly in the real world, particularly when fires
 are involved. This is precisely why the use of
 [Unity as a simulator](https://blogs.unity3d.com/2018/01/23/designing-safer-cities-through-simulations/)