  Meta-Reinforcement learning approaches aim to develop learning procedures
that can adapt quickly to a distribution of tasks with the help of a few
examples. Developing efficient exploration strategies capable of finding the
most useful samples becomes critical in such settings. Existing approaches
towards finding efficient exploration strategies add auxiliary objectives to
promote exploration by the pre-update policy, however, this makes the
adaptation using a few gradient steps difficult as the pre-update (exploration)
and post-update (exploitation) policies are often quite different. Instead, we
propose to explicitly model a separate exploration policy for the task
distribution. Having two different policies gives more flexibility in training
the exploration policy and also makes adaptation to any specific task easier.
We show that using self-supervised or supervised learning objectives for
adaptation allows for more efficient inner-loop updates and also demonstrate
the superior performance of our model compared to prior works in this domain.
