Abstract：The multi-agent reinforcement learning algorithm is difficult to adapt to dynamically changing environments of agent scale. Aiming at this problem, a sequence to sequence multi-agent reinforcement learning algorithm(SMARL) based on sequential learning and block structure is proposed. The control network of an agent is divided into action network and target network based on deep deterministic policy gradient structure and sequence-to-sequence structure, respectively, and the correlation between algorithm structure and agent scale is removed. Inputs and outputs of the algorithm are also processed to break the correlation between algorithm policy and agent scale. Agents in SMARL can quickly adapt to the new environment, take different roles in task and achieve fast learning. Experiments show that the adaptability, performance and training efficiency of the proposed algorithm are superior to baseline algorithms.
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