  Reinforcement learning (RL) methods have been shown to be capable of learning
intelligent behavior in rich domains. However, this has largely been done in
simulated domains without adequate focus on the process of building the
simulator. In this paper, we consider a setting where we have access to an
ensemble of pre-trained and possibly inaccurate simulators (models). We
approximate the real environment using a state-dependent linear combination of
the ensemble, where the coefficients are determined by the given state features
and some unknown parameters. Our proposed algorithm provably learns a
near-optimal policy with a sample complexity polynomial in the number of
unknown parameters, and incurs no dependence on the size of the state (or
action) space. As an extension, we also consider the more challenging problem
of model selection, where the state features are unknown and can be chosen from
a large candidate set. We provide exponential lower bounds that illustrate the
fundamental hardness of this problem, and develop a provably efficient
algorithm under additional natural assumptions.
