On the Approximation Performance of Fictitious Play in Finite Games
نویسندگان
چکیده
We study the performance of Fictitious Play, when used as a heuristic for finding an approximate Nash equilibrium of a two-player game. We exhibit a class of two-player games having payoffs in the range [0, 1] that show that Fictitious Play fails to find a solution having an additive approximation guarantee significantly better than 1/2. Our construction shows that for n×n games, in the worst case both players may perpetually have mixed strategies whose payoffs fall short of the best response by an additive quantity 1/2−O(1/n1−δ) for arbitrarily small δ. We also show an essentially matching upper bound of 1/2 − O(1/n).
منابع مشابه
Stochastic fictitious play with continuous action sets
Continuous action space games form a natural extension to normal form games with finite action sets. However, whilst learning dynamics in normal form games are now well studied, it is not until recently that their continuous action space counterparts have been examined. We extend stochastic fictitious play to the continuous action space framework. In normal form games the limiting behaviour of ...
متن کاملA Fictitious Play Approach to Large-Scale Optimization
In this paper we investigate the properties of the sampled version of the fictitious play algorithm, familiar from game theory, for games with identical payoffs, and propose a heuristic based on fictitious play as a solution procedure for discrete optimization problems of the form max{u(y) : y = (y1, . . . , yn) ∈ Y1 × · · · × Yn}, i.e., in which the feasible region is a Cartesian product of fi...
متن کاملDeep Reinforcement Learning from Self-Play in Imperfect-Information Games
Many real-world applications can be described as large-scale games of imperfect information. To deal with these challenging domains, prior work has focused on computing Nash equilibria in a handcrafted abstraction of the domain. In this paper we introduce the first scalable endto-end approach to learning approximate Nash equilibria without any prior knowledge. Our method combines fictitious sel...
متن کاملMulti-agent learning using Fictitious Play and Extended Kalman Filter
Decentralised optimisation tasks are important components of multiagent systems. These tasks can be interpreted as n-player potential games: therefore game-theoretic learning algorithms can be used to solve decentralised optimisation tasks. Fictitious play is the canonical example of these algorithms. Nevertheless fictitious play implicitly assumes that players have stationary strategies. We pr...
متن کاملFiltered Fictitious Play for Perturbed Observation Potential Games and Decentralised POMDPs
Potential games and decentralised partially observable MDPs (Dec–POMDPs) are two commonly used models of multi–agent interaction, for static optimisation and sequential decision– making settings, respectively. In this paper we introduce filtered fictitious play for solving repeated potential games in which each player’s observations of others’ actions are perturbed by random noise, and use this...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2011