Practical Issues in Temporal Difference Learning
Machine Learning
Prisoner's Dilemma: John Von Neumann, Game Theory and the Puzzle of the Bomb
Prisoner's Dilemma: John Von Neumann, Game Theory and the Puzzle of the Bomb
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Friend-or-Foe Q-learning in General-Sum Games
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
On-Line Learning of Coordination Plans
On-Line Learning of Coordination Plans
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Is There a Violation of Savage's Sure-Thing Principle in the Prisoner's Dilemma Game?
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Social contracts and human-computer interaction with simulated adapting agents
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
| Hi-index | 0.00 |
The predictions of classical game theory for one-shot and finitely repeated play of many 2x2 simulta neous games do not correspond to human behavior observed in laboratory experiments. The promis ing results of learning models in tracking human behavior coupled with the growing electronic market and the number of e-commerce applications has resulted in an increased interest in studying the behavior of adaptive artificial agents in different economic games. We model agents with a reinforce ment learning algorithm and analyze cooperative behavior in a sequential prisoner's dilemma game. Our results demonstrate the ability of artificial agents to learn cooperative behavior even in sequential games where defection is the subgame perfect Nash equilibrium. We attribute the reciprocal-like behavior to the structural flow of information, which reduces the risks of exploitation faced by the second-mover. Additionally, we analyze the impact of the second-mover's temptation payoff and pay off risks on the rate of cooperative behavior.