  This paper considers the problem of reward design for autonomous driving
(AD), with insights that are also applicable to the design of cost functions
and performance metrics more generally. Herein we develop 8 simple sanity
checks for identifying flaws in reward functions. The sanity checks are applied
to reward functions from past work on reinforcement learning (RL) for
autonomous driving, revealing near-universal flaws in reward design for AD that
might also exist pervasively across reward design for other tasks. Lastly, we
explore promising directions that may help future researchers design reward
functions for AD.
