  The exploration--exploitation trade-off in reinforcement learning (RL) is a
well-known and much-studied problem that balances greedy action selection with
novel experience, and the study of exploration methods is usually only
considered in the context of learning the optimal policy for a single learning
task. However, in the context of online task transfer, where there is a change
to the task during online operation, we hypothesize that exploration strategies
that anticipate the need to adapt to future tasks can have a pronounced impact
on the efficiency of transfer. As such, we re-examine the
exploration--exploitation trade-off in the context of transfer learning. In
this work, we review reinforcement learning exploration methods, define a
taxonomy with which to organize them, analyze these methods' differences in the
context of task transfer, and suggest avenues for future investigation.
