With sparse reward (kills, health, etc), scored a better 80 and learned much faster.
Normally, "reward engineering" uses human knowledge to give more continuous, richer rewards. This was not used here.
The "dense orange graph" - uses more dense rewards - kills, health - and learns better. I referred to this as a "sparse reward" - since it is still a fairly lean and sparse function.
But this is just my opinion. Also note this is for the older 1v1 agent.
The current reward function is even more detailed, and they blend and anneal the 5 agent score, so i dunno...
https://gist.github.com/dfarhi/66ec9d760ae0c49a5c492c9fae939...