Escaping Local Views: Discovering Latent Concepts for Interpretable Multi-Agent Reinforcement Learning
Efficient cooperation is challenging due to the usual partial observability of each agent in multi-agent reinforcement learning. Recurrent networks encode local interaction histories, but their hidden representations provide limited insight into the information underlying individual decisions. To address these challenges, we propose a novel interpretable framework, called escaping local views (ELV), which introduces semantically structured latent concepts to render policy decisions transparent. Specifically, each agent extracts low-dimensional semantic concepts from its local observation and action-observation trajectory. These concepts are jointly encoded into a contextual latent variabl...
| Authors | Yijie Sun, Sanquan Sun, Yanda Zhu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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