  Multi-agent systems require effective coordination between groups and
individuals to achieve common goals. However, current multi-agent reinforcement
learning (MARL) methods primarily focus on improving individual policies and do
not adequately address group-level policies, which leads to weak cooperation.
To address this issue, we propose a novel Consensus-oriented Strategy (CoS)
that emphasizes group and individual policies simultaneously. Specifically, CoS
comprises two main components: (a) the vector quantized group consensus module,
which extracts discrete latent embeddings that represent the stable and
discriminative group consensus, and (b) the group consensus-oriented strategy,
which integrates the group policy using a hypernet and the individual policies
using the group consensus, thereby promoting coordination at both the group and
individual levels. Through empirical experiments on cooperative navigation
tasks with both discrete and continuous spaces, as well as Google research
football, we demonstrate that CoS outperforms state-of-the-art MARL algorithms
and achieves better collaboration, thus providing a promising solution for
achieving effective coordination in multi-agent systems.
