  One of the main challenges in offline Reinforcement Learning (RL) is the
distribution shift that arises from the learned policy deviating from the data
collection policy. This is often addressed by avoiding out-of-distribution
(OOD) actions during policy improvement as their presence can lead to
substantial performance degradation. This challenge is amplified in the offline
Multi-Agent RL (MARL) setting since the joint action space grows exponentially
with the number of agents. To avoid this curse of dimensionality, existing MARL
methods adopt either value decomposition methods or fully decentralized
training of individual agents. However, even when combined with standard
conservatism principles, these methods can still result in the selection of OOD
joint actions in offline MARL. To this end, we introduce AlberDICE, an offline
MARL algorithm that alternatively performs centralized training of individual
agents based on stationary distribution optimization. AlberDICE circumvents the
exponential complexity of MARL by computing the best response of one agent at a
time while effectively avoiding OOD joint action selection. Theoretically, we
show that the alternating optimization procedure converges to Nash policies. In
the experiments, we demonstrate that AlberDICE significantly outperforms
baseline algorithms on a standard suite of MARL benchmarks.
