Empathy in Multi-Agent Reinforcement Learning project

_projects/empathy_marl.md

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+---
+title: Empathy in Multi-Agent Reinforcement Learning
+type: Research
+status: Completed
+date: 2026-01-25
+authors:
+  - name: Christina Eirini Christodoulou
+    avatar: /assets/img/team/christina_christodoulou.png
+  - name: Simone De Giorgi
+    avatar: /assets/img/team/simone_degiorgi.jpg
+  - name: Lavinia Maria Alexandra Skandali
+    avatar: /assets/img/team/lavinia_skandali.jpg
+---
+
+This paper studies whether valuing other agents’ rewards can improve outcomes in multi-agent reinforcement learning. We model empathy as a reward-weighting parameter and compare fixed empathy with empathy that is learned over time using a bandit-based adaptation mechanism.
+
+We evaluate this setup in the Prisoner’s Dilemma, the Stag Hunt, and a Renewable Resource Sharing environment. In all three settings, higher empathy clearly improves cooperation, welfare, and long-run stability. Yet when agents adapt empathy autonomously, they stabilize at intermediate levels instead of reaching the welfare-maximizing one. 
+
+Read the full paper to see how this gap emerges and why the adaptation mechanism becomes the key constraint!
+
+[Full Paper](/assets/reports/Empathy_MARL.pdf)