  In complex tasks, such as those with large combinatorial action spaces,
random exploration may be too inefficient to achieve meaningful learning
progress. In this work, we use a curriculum of progressively growing action
spaces to accelerate learning. We assume the environment is out of our control,
but that the agent may set an internal curriculum by initially restricting its
action space. Our approach uses off-policy reinforcement learning to estimate
optimal value functions for multiple action spaces simultaneously and
efficiently transfers data, value estimates, and state representations from
restricted action spaces to the full task. We show the efficacy of our approach
in proof-of-concept control tasks and on challenging large-scale StarCraft
micromanagement tasks with large, multi-agent action spaces.
