Optimistic Agents are Asymptotically Optimal
arXiv:1210.0077
Abstract
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.
13 LaTeX pages