Mitigating Retaliatory Algorithmic Collusion in Repeated Games
Reinforcement learning agents trained to maximize their own reward in repeated interactions can converge to supra-competitive outcomes resembling explicit collusion, without communication or shared design. Existing mitigation approaches are largely tied to specific economic settings, like two-sided platforms and auctions, leaving open how to design interventions for general repeated games. We address this gap by formalizing the connection between empirical observations from prior work on Q-learning collusion and classical theory of Simple Penal Codes (SPCs). We show any non-trivial SPC induces a quantifiable conditional dependence in agents' policies, detectable via the total variation di...
| Authors | Karthik Sivachandran, Rohan Paleja |
|---|---|
| Primary category | cs.LG |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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