Title: Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies,
Abstract: Decision making in multi-agent systems (MAS) is a great challenge due to
enormous state and joint action spaces as well as uncertainty, making
centralized control generally infeasible. Decentralized control offers better
scalability and robustness but requires mechanisms to coordinate on joint tasks
and to avoid conflicts. Common approaches to learn decentralized policies for
cooperative MAS suffer from non-stationarity and lacking credit assignment,
which can lead to unstable and uncoordinated behavior in complex environments.
In this paper, we propose Strong Emergent Policy approximation (STEP), a
scalable approach to learn strong decentralized policies for cooperative MAS
with a distributed variant of policy iteration. For that, we use function
approximation to learn from action recommendations of a decentralized
multi-agent planning algorithm. STEP combines decentralized multi-agent
planning with centralized learning, only requiring a generative model for
distributed black box optimization. We experimentally evaluate STEP in two
challenging and stochastic domains with large state and joint action spaces and
show that STEP is able to learn stronger policies than standard multi-agent
reinforcement learning algorithms, when combining multi-agent open-loop
planning with centralized function approximation. The learned policies can be
reintegrated into the multi-agent planning process to further improve
performance.