  Exploration in reinforcement learning is a challenging problem: in the worst
case, the agent must search for high-reward states that could be hidden
anywhere in the state space. Can we define a more tractable class of RL
problems, where the agent is provided with examples of successful outcomes? In
this problem setting, the reward function can be obtained automatically by
training a classifier to categorize states as successful or not. If trained
properly, such a classifier can provide a well-shaped objective landscape that
both promotes progress toward good states and provides a calibrated exploration
bonus. In this work, we show that an uncertainty aware classifier can solve
challenging reinforcement learning problems by both encouraging exploration and
provided directed guidance towards positive outcomes. We propose a novel
mechanism for obtaining these calibrated, uncertainty-aware classifiers based
on an amortized technique for computing the normalized maximum likelihood (NML)
distribution. To make this tractable, we propose a novel method for computing
the NML distribution by using meta-learning. We show that the resulting
algorithm has a number of intriguing connections to both count-based
exploration methods and prior algorithms for learning reward functions, while
also providing more effective guidance towards the goal. We demonstrate that
our algorithm solves a number of challenging navigation and robotic
manipulation tasks which prove difficult or impossible for prior methods.
