  Robotized warehouses are deployed to automatically distribute millions of
items brought by the massive logistic orders from e-commerce. A key to
automated item distribution is to plan paths for robots, also known as task
planning, where each task is to deliver racks with items to pickers for
processing and then return the rack back. Prior solutions are unfit for
large-scale robotized warehouses due to the inflexibility to time-varying item
arrivals and the low efficiency for high throughput. In this paper, we propose
a new task planning problem called TPRW, which aims to minimize the end-to-end
makespan that incorporates the entire item distribution pipeline, known as a
fulfilment cycle. Direct extensions from state-of-the-art path finding methods
are ineffective to solve the TPRW problem because they fail to adapt to the
bottleneck variations of fulfillment cycles. In response, we propose Efficient
Adaptive Task Planning, a framework for large-scale robotized warehouses with
time-varying item arrivals. It adaptively selects racks to fulfill at each
timestamp via reinforcement learning, accounting for the time-varying
bottleneck of the fulfillment cycles. Then it finds paths for robots to
transport the selected racks. The framework adopts a series of efficient
optimizations on both time and memory to handle large-scale item throughput.
Evaluations on both synthesized and real data show an improvement of $37.1\%$
in effectiveness and $75.5\%$ in efficiency over the state-of-the-arts.
