Reinforcement learning and stochastic optimisation
From MaRDI portal
Publication:2072112
DOI10.1007/s00780-021-00467-2zbMath1482.91225OpenAlexW4200081317MaRDI QIDQ2072112
Publication date: 1 February 2022
Published in: Finance and Stochastics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00780-021-00467-2
Artificial neural networks and deep learning (68T07) Optimal stochastic control (93E20) Financial applications of other theories (91G80) Stochastic games, stochastic differential games (91A15)
Related Items (5)
Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learning ⋮ Generative OrnsteinUhlenbeck markets via geometric deep learning ⋮ Optimal Scheduling of Entropy Regularizer for Continuous-Time Linear-Quadratic Reinforcement Learning ⋮ Reinforcement learning with dynamic convex risk measures ⋮ Exploratory Control with Tsallis Entropy for Latent Factor Models
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The Pricing of Options and Corporate Liabilities
- Incorporating order-flow into optimal execution
- Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
- Mean field games. I: The stationary case
- Mean field games. II: Finite horizon and optimal control
- Mean field games
- Continuous-time stochastic control and optimization with financial applications
- Martingales and arbitrage in multiperiod securities markets
- Arbitrage and equilibrium in economies with infinitely many commodities
- Martingales and stochastic integrals in the theory of continuous trading
- A general version of the fundamental theorem of asset pricing
- Asynchronous stochastic approximation and Q-learning
- Mean field game of controls and an application to trade crowding
- Multilayer feedforward networks are universal approximators
- Incorporating signals into optimal trading
- \({\mathcal Q}\)-learning
- DGM: a deep learning algorithm for solving partial differential equations
- Neural networks-based backward scheme for fully nonlinear PDEs
- Convergence of the deep BSDE method for coupled FBSDEs
- Mean field games and systemic risk
- Large population stochastic dynamic games: closed-loop McKean-Vlasov systems and the Nash certainty equivalence principle
- No-dynamic-arbitrage and market impact
- Explaining Variational Approximations
- Large-Population LQG Games Involving a Major Player: The Nash Certainty Equivalence Principle
- The Future of Data Analysis
- The Master Equation and the Convergence Problem in Mean Field Games
- Outperformance and Tracking: Dynamic Asset Allocation for Active and Passive Portfolio Management
- Machine learning for quantitative finance: fast derivative pricing, hedging and fitting
- 10.1162/1532443041827880
- Deep backward schemes for high-dimensional nonlinear PDEs
- A mean field view of the landscape of two-layer neural networks
- Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games I: The Ergodic Case
- Mean-Field Controls with Q-Learning for Cooperative MARL: Convergence and Complexity Analysis
- Deep Neural Networks, Generic Universal Interpolation, and Controlled ODEs
- Mean-Field Games of Optimal Stopping: A Relaxed Solution Approach
- Mean Field Analysis of Neural Networks: A Law of Large Numbers
- Deep hedging
- Universal features of price formation in financial markets: perspectives from deep learning
- DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
- Trading algorithms with learning in latent alpha models
- Systemic Risk in Interbanking Networks
- Large-Population Cost-Coupled LQG Problems With Nonuniform Agents: Individual-Mass Behavior and Decentralized $\varepsilon$-Nash Equilibria
- Probabilistic Theory of Mean Field Games with Applications I
- Probabilistic Theory of Mean Field Games with Applications II
- Mean-Field Game Strategies for Optimal Execution
- Some methods of speeding up the convergence of iteration methods
- Mean‐field games with differing beliefs for algorithmic trading
- Continuous‐time mean–variance portfolio selection: A reinforcement learning framework
- Approximation by superpositions of a sigmoidal function
This page was built for publication: Reinforcement learning and stochastic optimisation
