Title: Learning model-based planning from scratch,
Abstract: Conventional wisdom holds that model-based planning is a powerful approach to
sequential decision-making. It is often very challenging in practice, however,
because while a model can be used to evaluate a plan, it does not prescribe how
to construct a plan. Here we introduce the "Imagination-based Planner", the
first model-based, sequential decision-making agent that can learn to
construct, evaluate, and execute plans. Before any action, it can perform a
variable number of imagination steps, which involve proposing an imagined
action and evaluating it with its model-based imagination. All imagined actions
and outcomes are aggregated, iteratively, into a "plan context" which
conditions future real and imagined actions. The agent can even decide how to
imagine: testing out alternative imagined actions, chaining sequences of
actions together, or building a more complex "imagination tree" by navigating
flexibly among the previously imagined states using a learned policy. And our
agent can learn to plan economically, jointly optimizing for external rewards
and computational costs associated with using its imagination. We show that our
architecture can learn to solve a challenging continuous control problem, and
also learn elaborate planning strategies in a discrete maze-solving task. Our
work opens a new direction toward learning the components of a model-based
planning system and how to use them.