  Reinforcement learning (RL) provides a theoretical framework for continuously
improving an agent's behavior via trial and error. However, efficiently
learning policies from scratch can be very difficult, particularly for tasks
with exploration challenges. In such settings, it might be desirable to
initialize RL with an existing policy, offline data, or demonstrations.
However, naively performing such initialization in RL often works poorly,
especially for value-based methods. In this paper, we present a meta algorithm
that can use offline data, demonstrations, or a pre-existing policy to
initialize an RL policy, and is compatible with any RL approach. In particular,
we propose Jump-Start Reinforcement Learning (JSRL), an algorithm that employs
two policies to solve tasks: a guide-policy, and an exploration-policy. By
using the guide-policy to form a curriculum of starting states for the
exploration-policy, we are able to efficiently improve performance on a set of
simulated robotic tasks. We show via experiments that JSRL is able to
significantly outperform existing imitation and reinforcement learning
algorithms, particularly in the small-data regime. In addition, we provide an
upper bound on the sample complexity of JSRL and show that with the help of a
guide-policy, one can improve the sample complexity for non-optimism
exploration methods from exponential in horizon to polynomial.
