In social dilemmas individuals face a temptation to increase their payoffs in the short run at a cost to the long run total welfare. Much is known about how cooperation can be stabilized in the simplest of such settings: repeated Prisoner's Dilemma games. However, there is relatively little work on generalizing these insights to more complex situations. Solving this problem is extremely important if we wish to construct artificially intelligent agents that interact with each other and humans in real world scenarios. We show how to use modern reinforcement learning methods to generalize a highly successful Prisoner's Dilemma strategy: tit-for-tat. We construct agents that act in ways that are simple to understand, nice (begin by cooperating), provokable (try to avoid being exploited), and forgiving (following a bad turn try to return to mutual cooperation). We show both theoretically and experimentally that such agents can maintain cooperation in more complex environments. We also show that such agents teach simple reactive agents to be cooperative. In contrast, we show that standard 'reactive self-
Deep reinforcement learning methods can be used to train agents to cooperate in complex environments REF .
Maintaining cooperation in complex social dilemmas using deep reinforcement learning
ArXiv
ArXiv
Computer Science