ALMA: Hierarchical Learning for Composite Multi-Agent Tasks

Abstract

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.Preprint. Under review.

A hierarchical multi-agent allocation-action learning framework for multi-subtask games

“…The task allocation problem is solved by search algorithms, and task execution is solved by coordinated reinforcement learning. The work [4] proposes a hierarchical method to learn both high-level subtask allocation policy and low-level agent policies. The high-level policy considers the states of all subtasks as input to estimate values for different allocations.…”

“…In this section, we examine the performance of the proposed method on two challenging environments, i.e., Google Research Football (GRF) [22] and SAVETHECITY [4]. We aim to answer three questions in the following experiments:…”

“…In fact, many realworld problems are composed of a series of subtasks with a specific execution procedure. For example, fire-fighting [4] involves subtasks like water supply and fire control. Firefighters need be distributed to subtasks, and then, they collaborate to accomplish all subtasks in accordance with the fire-fighting procedure.…”