  This paper investigates the model-based methods in multi-agent reinforcement
learning (MARL). We specify the dynamics sample complexity and the opponent
sample complexity in MARL, and conduct a theoretic analysis of return
discrepancy upper bound. To reduce the upper bound with the intention of low
sample complexity during the whole learning process, we propose a novel
decentralized model-based MARL method, named Adaptive Opponent-wise Rollout
Policy Optimization (AORPO). In AORPO, each agent builds its multi-agent
environment model, consisting of a dynamics model and multiple opponent models,
and trains its policy with the adaptive opponent-wise rollout. We further prove
the theoretic convergence of AORPO under reasonable assumptions. Empirical
experiments on competitive and cooperative tasks demonstrate that AORPO can
achieve improved sample efficiency with comparable asymptotic performance over
the compared MARL methods.
