Self-Referenced Social Preferences: Cooperation without Observing Others Rewards
Social preferences can promote cooperation in multi-agent reinforcement learning, but existing approaches often require agents to observe the rewards of their peers. In many real-world interactions, however, an agent can, as humans do, observe others' behavior and outcomes without access to their private reward signals. We introduce self-referenced social preferences, in which each agent learns a model of its own reward, applies it to other agents' observed transitions to assess their outcomes from its own perspective, and feeds these self-referenced assessments into standard social preferences. We study two ways to incorporate these assessments: modifying the learning reward, or using th...
| Authors | Mohamed Ayman Mohamed, Harshil Kotamreddy, Marcos Menon Jose |
|---|---|
| Primary category | cs.AI |
| Published | 6 October 2026 |
| Last updated at source | 7 October 2026 |
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