Summary by CodyWild 4 weeks ago
A central question of this paper is: under what circumstances will you see agents that have been trained to optimize their own reward implement strategies - like tit for tat - that are are more sophisticated and higher overall reward than each agent simply pursuing its dominant strategy. The games under consideration here are “general sum” games like Iterated Prisoner’s Dilemma, where each agent’s dominant strategy is to defect, but with some amount of coordination or reciprocity, better overall outcomes are possible. Previously, models have achieved this via explicit hardcoding, but this paper strove to use a simpler, more general approach: allowing each agent A to optimize its reward not only with regard to a fixed opponent, but with regard to an opponent that will make a predictable update move in response to the action A is about to take.
Specifically, this model - shorthanded as LOLA, Learning with Opponent-Learning Awareness - maximizes a given agent’s expected discount reward, but looks at reward *conditional on* the ways the opponent will update to a given action. In a simplified world where the explicit reward function is known, it’s possible to literally take the derivative through the opponent’s expected update step, taking into account the ways your expected reward is changed by the response you expect of your opponent. Outside of this simplified framework, in the world of policy gradients, there’s no analytic loss function; you can no longer directly differentiate your reward function with respect to your opponent’s actions, but you can differentiate your expected reward estimator with respect to them. This concept is quite similar to a 2016 paper, Metz et al, that used this concept to train a more effective GAN, by allowing each network in the adversarial pair to “look ahead” to their opponent’s expected response as a way to avoid getting stuck in repetitive action/response cycles. In circumstances where the parameters of the opponent are not known - obviously closer to realistic for an adversarial scenario - the paper demonstrates proof of concept ability to model an opponent’s strategy based on their past actions, and use that to conduct response-step estimates.
https://i.imgur.com/5xddJRj.png
It should of course be said in all this: even though this setup did produce results closer to what we would expect in rational reciprocity, it’s still very simplified. In most of the experiments, each agent had perfect knowledge of the opponent’s priorities and likely responses; in most game theory scenarios, constructing a model of your opponent is a nontrivial part of the difficulty. Nonetheless, I found it a

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