Zobrazeno 1 - 10
of 367
pro vyhledávání: '"Reisinger, Christoph"'
We investigate mean-field games (MFG) in which agents can actively control their speed of access to information. Specifically, the agents can dynamically decide to obtain observations with reduced delay by accepting higher observation costs. Agents s
Externí odkaz:
http://arxiv.org/abs/2309.07877
We consider a family of McKean-Vlasov equations arising as the large particle limit of a system of interacting particles on the positive half-line with common noise and feedback. Such systems are motivated by structural models for systemic risk with
Externí odkaz:
http://arxiv.org/abs/2307.10800
In this paper we revisit an open problem posed by Aldous on the max-entropy win-probability martingale: given two players of equal strength, such that the win-probability is a martingale diffusion, which of these processes has maximum entropy and hen
Externí odkaz:
http://arxiv.org/abs/2306.07133
In this paper, we propose a novel $K$-nearest neighbor resampling procedure for estimating the performance of a policy from historical data containing realized episodes of a decision process generated under a different policy. We provide statistical
Externí odkaz:
http://arxiv.org/abs/2306.04836
In this paper, we study the Euler--Maruyama scheme for a particle method to approximate the McKean--Vlasov dynamics of calibrated local-stochastic volatility (LSV) models. Given the open question of well-posedness of the original problem, we work wit
Externí odkaz:
http://arxiv.org/abs/2302.00434
We study the global linear convergence of policy gradient (PG) methods for finite-horizon continuous-time exploratory linear-quadratic control (LQC) problems. The setting includes stochastic LQC problems with indefinite costs and allows additional en
Externí odkaz:
http://arxiv.org/abs/2211.00617
We propose an explicit drift-randomised Milstein scheme for both McKean--Vlasov stochastic differential equations and associated high-dimensional interacting particle systems with common noise. By using a drift-randomisation step in space and measure
Externí odkaz:
http://arxiv.org/abs/2208.10052
We consider two implicit approximation schemes of the one-dimensional supercooled Stefan problem and prove their convergence, even in the presence of finite time blow-ups. All proofs are based on a probabilistic reformulation recently considered in t
Externí odkaz:
http://arxiv.org/abs/2206.14641
We study the capability of arbitrage-free neural-SDE market models to yield effective strategies for hedging options. In particular, we derive sensitivity-based and minimum-variance-based hedging strategies using these models and examine their perfor
Externí odkaz:
http://arxiv.org/abs/2205.15991
Despite its popularity in the reinforcement learning community, a provably convergent policy gradient method for continuous space-time control problems with nonlinear state dynamics has been elusive. This paper proposes proximal gradient algorithms f
Externí odkaz:
http://arxiv.org/abs/2203.11758