Agents stop reading each other's rewards: watching peers' behavior is enough to cooperate and split returns more evenly
Multi-agent reinforcement learning no longer needs peers' private rewards: each agent evaluates others' transitions with its own learned reward model, learns cooperation in Escape Room, Clean Up, and Commons Harvest, and often divides returns more evenly than agents that see the true rewards.