  Leveraging learned strategies in unfamiliar scenarios is fundamental to human
intelligence. In reinforcement learning, rationally reusing the policies
acquired from other tasks or human experts is critical for tackling problems
that are difficult to learn from scratch. In this work, we present a framework
called Selective Myopic bEhavior Control~(SMEC), which results from the insight
that the short-term behaviors of prior policies are sharable across tasks. By
evaluating the behaviors of prior policies via a hybrid value function
architecture, SMEC adaptively aggregates the sharable short-term behaviors of
prior policies and the long-term behaviors of the task policy, leading to
coordinated decisions. Empirical results on a collection of manipulation and
locomotion tasks demonstrate that SMEC outperforms existing methods, and
validate the ability of SMEC to leverage related prior policies.
