Title: Accelerating Imitation Learning with Predictive Models,
Abstract: Sample efficiency is critical in solving real-world reinforcement learning
problems, where agent-environment interactions can be costly. Imitation
learning from expert advice has proved to be an effective strategy for reducing
the number of interactions required to train a policy. Online imitation
learning, which interleaves policy evaluation and policy optimization, is a
particularly effective technique with provable performance guarantees. In this
work, we seek to further accelerate the convergence rate of online imitation
learning, thereby making it more sample efficient. We propose two model-based
algorithms inspired by Follow-the-Leader (FTL) with prediction: MoBIL-VI based
on solving variational inequalities and MoBIL-Prox based on stochastic
first-order updates. These two methods leverage a model to predict future
gradients to speed up policy learning. When the model oracle is learned online,
these algorithms can provably accelerate the best known convergence rate up to
an order. Our algorithms can be viewed as a generalization of stochastic
Mirror-Prox (Juditsky et al., 2011), and admit a simple constructive FTL-style
analysis of performance.