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最新文章

• A Note on State-Independent Policies in Network Revenue Management AI翻译
  作者：Manchiraju, Chandrasekhar; Dawande, Milind; Janakiraman, Ganesh
  作者单位：University of Texas System; University of Texas Dallas
  摘要：We revisit two classical price-based and choice-based network revenue management problems studied in the literature. The setting for the problems is as follows: A firm sells multiple products over a finite horizon using a limited supply of resources. Product demands are stochastic. The demand rate for each product depends on the current price vector (respectively, assortment displayed). The firm's goal is to obtain a pricing (respectively, assortment) policy that maximizes its expected revenue...
• Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-Order Convergence AI翻译
  作者：Luo, Yuetian; Huang, Wen; Li, Xudong; Zhang, Anru
  作者单位：University of Chicago; Xiamen University; Fudan University; Duke University
  摘要：In this paper, we propose a recursive importance sketching algorithm for rank constrained least squares optimization (RISRO). The key step of RISRO is recursive importance sketching, a new sketching framework based on deterministically designed recursive projections, and it significantly differs from the randomized sketching in the literature. Several existing algorithms in the literature can be reinterpreted under this new sketching framework, and RISRO offers clear advantages over them. RISR...
• Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model AI翻译
  作者：Li, Gen; Wei, Yuting; Chi, Yuejie; Chen, Yuxin
  作者单位：University of Pennsylvania; Carnegie Mellon University; University of Pennsylvania
  摘要：This paper is concerned with the sample efficiency of reinforcement learning, assuming access to a generative model (or simulator). We first consider gamma-discounted infinite-horizon Markov decision processes (MDPs) with state space S and action space A. Despite a number of prior works tackling this problem, a complete picture of the trade-offs between sample complexity and statistical accuracy has yet to be determined. In particular, all prior results suffer from a severe sample size barrier...
• Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis AI翻译
  作者：Li, Gen; Cai, Changxiao; Chen, Yuxin; Wei, Yuting; Chi, Yuejie
  作者单位：University of Pennsylvania; University of Pennsylvania; Carnegie Mellon University
  摘要：Q-learning, which seeks to learn the optimal Q-function of a Markov decision process (MDP) in a model-free fashion, lies at the heart of reinforcement learning. When it comes to the synchronous setting (such that independent samples for all state-action pairs are drawn from a generative model in each iteration), substantial progress has been made toward understanding the sample efficiency of Q-learning. Consider a gamma-discounted infinite-horizon MDP with state space S and action space A: to ...
• A Planner-Trader Decomposition for Multimarket Hydro Scheduling AI翻译
  作者：Schindler, Kilian; Rujeerapaiboon, Napat; Kuhn, Daniel; Wiesemann, Wolfram
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; National University of Singapore; Imperial College London
  摘要：Peak/off-peak spreads on European electricity forward and spot markets are eroding due to the ongoing nuclear phaseout in Germany and the steady growth in photovoltaic capacity. The reduced profitability of peak/off-peak arbitrage forces hydropower producers to recover part of their original profitability on the reserve markets. We propose a bilayer stochastic programming framework for the optimal operation of a fleet of interconnected hydropower plants that sells energy on both the spot and t...
• Resource-Aware Cost-Sharing Methods for Scheduling Games AI翻译
  作者：Christodoulou, George; Gkatzelis, Vasilis; Sgouritsa, Alkmini
  作者单位：Aristotle University of Thessaloniki; Drexel University; Athens University of Economics & Business
  摘要：We study the performance of cost-sharing methods in a selfish scheduling setting where a group of users schedule their jobs on machines with load-dependent cost functions, aiming to minimize their own cost. Anticipating this user behavior, the system designer chooses a decentralized protocol that defines how the cost generated on each machine is to be shared among its users, and the performance of the protocol is evaluated over the Nash equilibria of the induced game. Previous work on selfish ...
• Persuading Risk-Conscious Agents: A Geometric Approach AI翻译
  作者：Anunrojwong, Jerry; Iyer, Krishnamurthy; Lingenbrink, David
  作者单位：Columbia University; University of Minnesota System; University of Minnesota Twin Cities; Cornell University
  摘要：We consider a persuasion problem between a sender and a receiver where utility may be nonlinear in the latter's belief; we call such receivers risk conscious. Such utility models arise when the receiver exhibits systematic biases away from expected utility maximization, such as uncertainty aversion (e.g., from sensitivity to the variance of the waiting time for a service). Because of this nonlinearity, the standard approach to finding the optimal persuasion mechanism using revelation principle...
• Identifying Merger Opportunities: The Case of Air Traffic Control AI翻译
  作者：Adler, Nicole; Olesen, Ole Bent; Volta, Nicola
  作者单位：Hebrew University of Jerusalem; University of Southern Denmark; Cranfield University
  摘要：Horizontal mergers and acquisitions offer firms the means to grow. However, forecasting these actions' potential effects on the market is not a simple task. We propose a model that identifies optimal horizontal merger configurations for an industry. The model endogenizes the merger choice by maximizing the overall potential efficiency gain at the level of an industry or firm with multiple branches. We further extend the model to consider mergers that create contiguous firms, should network eff...
• An Approximate Dynamic Programming Approach to Repeated Games with Vector Losses AI翻译
  作者：Kamble, Vijay; Loiseau, Patrick; Walrand, Jean
  作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Inria; Max Planck Society; University of California System; University of California Berkeley
  摘要：We describe an approximate dynamic programming (ADP) approach to compute approximations of the optimal strategies and of the minimal losses that can be guaranteed in discounted repeated games with vector-valued losses. Among other applications, such vector-valued games prominently arise in the analysis of worst-case regret in repeated decision making in unknown environments, also known as the adversarial online learning framework. At the core of our approach is a characterization of the lower ...
• Mechanism Design Under Approximate Incentive Compatibility AI翻译
  作者：Balseiro, Santiago R.; Besbes, Omar; Castro, Francisco
  作者单位：Columbia University; University of California System; University of California Los Angeles
  摘要：A fundamental assumption in classical mechanism design is that buyers are perfect optimizers. However, in practice, buyers may be limited by their computational capabilities or a lack of information and may not be able to perfectly optimize their response to a mechanism. This has motivated the introduction of approximate incentive compatibility (IC) as an appealing solution concept for practical mechanism design. Although most of the literature has focused on the analysis of particular approxi...