  This work studies reinforcement learning in the Sim-to-Real setting, in which
an agent is first trained on a number of simulators before being deployed in
the real world, with the aim of decreasing the real-world sample complexity
requirement. Using a dynamic model known as a rich observation Markov decision
process (ROMDP), we formulate a theoretical framework for Sim-to-Real in the
situation where feedback in the real world is not available. We establish
real-world sample complexity guarantees that are smaller than what is currently
known for directly (i.e., without access to simulators) learning a ROMDP with
feedback.
