For a non-stationary opponent in a multi-agent environment, traditional methods model the opponent through its complex information to learn one or more optimal response policies. However, the response policy learned earlier is prone to catastrophic forgetting due to data imbalance in the online-updated replay buffer for non-stationary changes of opponent policies. This paper focuses on how to learn new response policies without forgetting old policies that have been learned when the opponent

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... icy is constantly changing. We extract the representation of opponent policies and make explicit clustering distinctions through the contrastive learning autoencoder. With the idea of balancing the replay buffer, we maintain continuous learning of the trajectory data of various opponent policies that have appeared to avoid policy forgetting. Finally, we demonstrate the effectiveness of the method under a classical opponent modeling environment (soccer) and show the clustering effect of different opponent policies.