Technical Note: \cal Q-Learning
Machine Learning
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
The complexity of computing a Nash equilibrium
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
If multi-agent learning is the answer, what is the question?
Artificial Intelligence
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Using game theory and reinforcement learning, we created and analyzed generalized agent-based models of hepatic toxin elimination processes to explore plausible causes of hepatic functional zonation. We considered a general situation in which a group of protective agents (analogous to liver cells) cooperate and self-organize their efforts to minimize optimally the negative effects of toxin intrusions. The model suggests that in order to do so, the agents should adjust their resource consumption based on two factors: 1) their ranked proximity to the common wealth and 2) the potential damage caused by toxins. We verified that liver cells do the same.