Infinite-horizon average-cost Markov decision process routing games
Infinite-horizon average-cost Markov decision process routing games
复制标题
无限视野平均成本马尔可夫决策过程路由博弈
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
S. Sastry
中科院分区：
文献类型：
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作者：
Daniel J. Calderone;S. Sastry
我们探索了非原子路由博弈的扩展，我们称之为马尔可夫决策过程路由博弈，其中每个代理选择网络中节点之间的过渡策略，而不是从原始节点到目标节点的路径，即群体中的每个代理解决马尔可夫决策过程而不是最短路径问题。这种类型的游戏最初是在有限视界总成本的情况下引入的。这里我们给出了无限视界平均成本情况。我们给出了Wardrop平衡的适当定义以及求平衡的势函数程序。这项工作可以被认为是连续种群随机博弈（平均场博弈或匿名序列博弈）的基于路由博弈的公式。我们将我们的模型应用于在城市地区竞争车费的拼车司机。
We explore an extension of nonatomic routing games that we call Markov decision process routing games where each agent chooses a transition policy between nodes in a network rather than a path from an origin node to a destination node, i.e. each agent in the population solves a Markov decision process rather than a shortest path problem. This type of game was first introduced in [1] in the finite-horizon total-cost case. Here we present the infinite-horizon average-cost case. We present the appropriate definition of a Wardrop equilibrium as well as a potential function program for finding the equilibrium. This work can be thought of as a routing-game-based formulation of continuous population stochastic games (mean-field games or anonymous sequential games). We apply our model to ridesharing drivers competing for fares in an urban area.