As an alternative to highly pessimistic worst-case evaluations and overly optimistic single example empirical demonstrations  we advocate evaluating reinforcement-learning algorithms relative to a distribution over target environments We formalize the problem of optimizing a learner based on environments sampled from this distribution as "meta-reinforcement learning"