  In this paper, we consider the source of Deep Reinforcement Learning (DRL)'s
sample complexity, asking how much derives from the requirement of learning
useful representations of environment states and how much is due to the sample
complexity of learning a policy. While for DRL agents, the distinction between
representation and policy may not be clear, we seek new insight through a set
of transfer learning experiments. In each experiment, we retain some fraction
of layers trained on either the same game or a related game, comparing the
benefits of transfer learning to learning a policy from scratch. Interestingly,
we find that benefits due to transfer are highly variable in general and
non-symmetric across pairs of tasks. Our experiments suggest that perhaps
transfer from simpler environments can boost performance on more complex
downstream tasks and that the requirements of learning a useful representation
can range from negligible to the majority of the sample complexity, based on
the environment. Furthermore, we find that fine-tuning generally outperforms
training with the transferred layers frozen, confirming an insight first noted
in the classification setting.
