Optimistic Agents are Asymptotically Optimal
  We use optimism to introduce generic asymptotically optimal reinforcement learning agents. They achieve, with an arbitrary finite or compact class of environments, asymptotically optimal behavior. Furthermore, in the finite deterministic case we provide finite error bounds. 
Peter Sunehag and Marcus Hutter
cs.AI cs.LG