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Found 14 papers, 0 papers with code
Control randomisation approach for policy gradient and application to reinforcement learning in optimal switching
We propose a comprehensive framework for policy gradient methods tailored to continuous time reinforcement learning.
Actor critic learning algorithms for mean-field control with moment neural networks
We develop a new policy gradient and actor-critic algorithm for solving mean-field control problems within a continuous time reinforcement learning setting.
Generative modeling for time series via Schr{ö}dinger bridge
We propose a novel generative model for time series based on Schr{\"o}dinger bridge (SB) approach.
Actor-Critic learning for mean-field control in continuous time
We study policy gradient for mean-field control in continuous time in a reinforcement learning setting.
Mean-field neural networks-based algorithms for McKean-Vlasov control problems *
This paper is devoted to the numerical resolution of McKean-Vlasov control problems via the class of mean-field neural networks introduced in our companion paper [25] in order to learn the solution on the Wasserstein space.
Mean-field neural networks: learning mappings on Wasserstein space
We study the machine learning task for models with operators mapping between the Wasserstein space of probability measures and a space of functions, like e. g. in mean-field games/control problems.
Differential learning methods for solving fully nonlinear PDEs
Compared to existing methods, the addition of a differential loss function associated to the gradient, and augmented training sets with Malliavin derivatives of the forward process, yields a better estimation of the PDE's solution derivatives, in particular of the second derivative, which is usually difficult to approximate.
Neural networks-based algorithms for stochastic control and PDEs in finance
This paper presents machine learning techniques and deep reinforcement learningbased algorithms for the efficient resolution of nonlinear partial differential equations and dynamic optimization problems arising in investment decisions and derivative pricing in financial engineering.
Optimization and Control Computational Finance
Discrete-time portfolio optimization under maximum drawdown constraint with partial information and deep learning resolution
We study a discrete-time portfolio selection problem with partial information and maxi\-mum drawdown constraint.
Equilibrium price in intraday electricity markets
(i) When there is no uncertainty on generation, it is shown that the market price is a convex combination of forecasted marginal cost of each agent, with deterministic weights.
Mean-variance portfolio selection with tracking error penalization
Such consideration is motivated as follows: (i) On the one hand, it is a way to robustify the mean-variance allocation in case of misspecified parameters, by "fitting" it to a reference portfolio that can be agnostic to market parameters; (ii) On the other hand, it is a procedure to track a benchmark and improve the Sharpe ratio of the resulting portfolio by considering a mean-variance criterion in the objective function.
Deep backward schemes for high-dimensional nonlinear PDEs
We analyze the convergence of the deep learning schemes and provide error estimates in terms of the universal approximation of neural networks.
Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysis
This paper develops algorithms for high-dimensional stochastic control problems based on deep learning and dynamic programming.
Algorithmic trading in a microstructural limit order book model
In particular, we simulated an order book with constant/ symmet-ric/ asymmetrical/ state dependent intensities, and compared the computed optimal strategy with naive strategies.
