  Discounted reinforcement learning is fundamentally incompatible with function
approximation for control in continuing tasks. It is not an optimization
problem in its usual formulation, so when using function approximation there is
no optimal policy. We substantiate these claims, then go on to address some
misconceptions about discounting and its connection to the average reward
formulation. We encourage researchers to adopt rigorous optimization
approaches, such as maximizing average reward, for reinforcement learning in
continuing tasks.
