  Artificial general intelligence aims to create agents capable of learning to
solve arbitrary interesting problems. We define two versions of asymptotic
optimality and prove that no agent can satisfy the strong version while in some
cases, depending on discounting, there does exist a non-computable weak
asymptotically optimal agent.
