  We study the problem of representational transfer in RL, where an agent first
pretrains in a number of source tasks to discover a shared representation,
which is subsequently used to learn a good policy in a \emph{target task}. We
propose a new notion of task relatedness between source and target tasks, and
develop a novel approach for representational transfer under this assumption.
Concretely, we show that given generative access to source tasks, we can
discover a representation, using which subsequent linear RL techniques quickly
converge to a near-optimal policy in the target task.
  The sample complexity is close to knowing the ground truth features in the
target task, and comparable to prior representation learning results in the
source tasks. We complement our positive results with lower bounds without
generative access, and validate our findings with empirical evaluation on rich
observation MDPs that require deep exploration. In our experiments, we observe
a speed up in learning in the target by pre-training, and also validate the
need for generative access in source tasks.
