  Despite significant progress on multi-agent reinforcement learning (MARL) in
recent years, coordination in complex domains remains a challenge. Work in MARL
often focuses on solving tasks where agents interact with all other agents and
entities in the environment; however, we observe that real-world tasks are
often composed of several isolated instances of local agent interactions
(subtasks), and each agent can meaningfully focus on one subtask to the
exclusion of all else in the environment. In these composite tasks, successful
policies can often be decomposed into two levels of decision-making: agents are
allocated to specific subtasks and each agent acts productively towards their
assigned subtask alone. This decomposed decision making provides a strong
structural inductive bias, significantly reduces agent observation spaces, and
encourages subtask-specific policies to be reused and composed during training,
as opposed to treating each new composition of subtasks as unique. We introduce
ALMA, a general learning method for taking advantage of these structured tasks.
ALMA simultaneously learns a high-level subtask allocation policy and low-level
agent policies. We demonstrate that ALMA learns sophisticated coordination
behavior in a number of challenging environments, outperforming strong
baselines. ALMA's modularity also enables it to better generalize to new
environment configurations. Finally, we find that while ALMA can integrate
separately trained allocation and action policies, the best performance is
obtained only by training all components jointly. Our code is available at
https://github.com/shariqiqbal2810/ALMA
