Title: Towards Global Optimality in Cooperative MARL with Sequential Transformation. Abstract: Policy learning in multi-agent reinforcement learning (MARL) is challenging due to the exponential growth of joint state-action space with respect to the number of agents. To achieve higher scalability, the paradigm of centralized training with decentralized execution (CTDE) is broadly adopted with factorized structure in MARL. However, we observe that existing CTDE algorithms in cooperative MARL cannot achieve optimality even in simple matrix games. To understand this phenomenon, we analyze two mainstream classes of CTDE algorithms -- actor-critic algorithms and value-decomposition algorithms. Our theoretical and experimental results characterize the weakness of these two classes of algorithms when the optimization method is taken into consideration, which indicates that the currently used centralized training manner is deficient in compatibility with decentralized policy. To address this issue, we present a transformation framework that reformulates a multi-agent MDP as a special "single-agent" MDP with a sequential structure and can allow employing off-the-shelf single-agent reinforcement learning (SARL) algorithms to efficiently learn corresponding multi-agent tasks. After that, a decentralized policy can still be learned by distilling the "single-agent" policy. This framework retains the optimality guarantee of SARL algorithms into cooperative MARL. To instantiate this transformation framework, we propose a Transformed PPO, called T-PPO, which can theoretically perform optimal policy learning in the finite multi-agent MDPs and shows significant outperformance on a large set of cooperative multi-agent tasks.