BL-WoLF: A Framework For Loss-Bounded Learnability In Zero-Sum Games

We present BL-WoLF, a framework for learnability in repeated zero-sum games where the cost of learning is measured by the losses the learning agent accrues (rather than the number of rounds). The game is adversarially chosen from some family that the learner knows. The opponent knows the game and the learner's learning strategy. The learner tries to either not accrue losses, or to quickly learn about the game so as to avoid future losses (this is consistent with the Win or Learn Fast (WoLF) principle; BL stands for "bounded loss"). Our framework allows for both probabilistic and approximate learning. The resultant notion of BL-WoLF-learnability can be applied to any class of games, and allows us to measure the inherent disadvantage to a player that does not know which game in the class it is in. We present guaranteed BL-WoLF-learnability results for families of games with deterministic payoffs and families of games with stochastic payoffs. We demonstrate that these fami...

• Type: Conference
• Year: 2003
• Where: ICML
• Authors: Vincent Conitzer, Tuomas Sandholm
• Keywords: Deterministic Payoffs, ICML 2003, Machine Learning, Stochastic Payoffs, Zero-sum Games