Learning Adaptable Risk-Sensitive Policies to Coordinate in Multi-Agent General-Sum Games

Understand

In general-sum games, the interaction of self-interested learning agents commonly leads to socially worse outcomes, such as defect-defect in the iterated stag hunt (ISH).

• Previous works address this challenge by sharing rewards or shaping their opponents' learning process, which require too strong assumptions.
• In this paper, we demonstrate that agents trained to optimize expected returns are more likely to choose a safe action that leads to guaranteed but lower rewards.
• However, there typically exists a risky action that leads to higher rewards in the long run only if agents cooperate, e.g., cooperate-cooperate in ISH.