  Many potential applications of reinforcement learning (RL) are stymied by the
large numbers of samples required to learn an effective policy. This is
especially true when applying RL to real-world control tasks, e.g. in the
sciences or robotics, where executing a policy in the environment is costly. In
popular RL algorithms, agents typically explore either by adding stochasticity
to a reward-maximizing policy or by attempting to gather maximal information
about environment dynamics without taking the given task into account. In this
work, we develop a method that allows us to plan for exploration while taking
both the task and the current knowledge about the dynamics into account. The
key insight to our approach is to plan an action sequence that maximizes the
expected information gain about the optimal trajectory for the task at hand. We
demonstrate that our method learns strong policies with 2x fewer samples than
strong exploration baselines and 200x fewer samples than model free methods on
a diverse set of low-to-medium dimensional control tasks in both the open-loop
and closed-loop control settings.
