Foundations of Reinforcement Learning with Applications in Finance
portes grátis
Foundations of Reinforcement Learning with Applications in Finance
Jelvis, Tikhon; Rao, Ashwin
Taylor & Francis Ltd
12/2022
500
Dura
Inglês
9781032124124
15 a 20 dias
1124
Descrição não disponível.
Section I. Processes and Planning Algorithms. 1. Markov Processes. 2. Markov Decision Processes. 3. Dynamic Programming Algorithms. 4. Function Approximation and Approximate Dynamic Programming. Section II. Modeling Financial Applications. 5. Utility Theory. 6. Dynamic Asset-Allocation and Consumption. 7. Derivatives Pricing and Hedging. 8. Order-Book Trading Algorithms. Section III. Reinforcement Learning Algorithms. 9. Monte-Carlo and Temporal-Difference for Prediction. 10. Monte-Carlo and Temporal-Difference for Control. 11. Batch RL, Experience-Replay, DQN, LSPI, Gradient TD. 12. Policy Gradient Algorithms. Section IV. Finishing Touches. 13. Multi-Armed Bandits: Exploration versus Exploitation. 14. Blending Learning and Planning. 15. Summary and Real-World Considerations. Appendices.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Reinforcement Learning;Finance;Sequential Optimal Decisioning under Uncertainty;algorithms;Sutton;financial mathematics;Barto;Markov Decision Process Framework;Bellman Optimality Equation;Markov Decision Process;Deterministic Optimal Policy;RL Algorithm;AI Agent;Monte Carlo Tree Search;Banach Fixed Point Theorem;Risk Neutral Probability Measure;Non-terminal States;CRRA Utility Function;POMDP;Trace Experience;MDP.;Module III;Riskless Asset;Atomic Experience;Policy Iteration;Risky Asset;Function Approximation;ADP;PG;Dynamic Programming Algorithms;Stockout Cost;Eligibility Traces
Section I. Processes and Planning Algorithms. 1. Markov Processes. 2. Markov Decision Processes. 3. Dynamic Programming Algorithms. 4. Function Approximation and Approximate Dynamic Programming. Section II. Modeling Financial Applications. 5. Utility Theory. 6. Dynamic Asset-Allocation and Consumption. 7. Derivatives Pricing and Hedging. 8. Order-Book Trading Algorithms. Section III. Reinforcement Learning Algorithms. 9. Monte-Carlo and Temporal-Difference for Prediction. 10. Monte-Carlo and Temporal-Difference for Control. 11. Batch RL, Experience-Replay, DQN, LSPI, Gradient TD. 12. Policy Gradient Algorithms. Section IV. Finishing Touches. 13. Multi-Armed Bandits: Exploration versus Exploitation. 14. Blending Learning and Planning. 15. Summary and Real-World Considerations. Appendices.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Reinforcement Learning;Finance;Sequential Optimal Decisioning under Uncertainty;algorithms;Sutton;financial mathematics;Barto;Markov Decision Process Framework;Bellman Optimality Equation;Markov Decision Process;Deterministic Optimal Policy;RL Algorithm;AI Agent;Monte Carlo Tree Search;Banach Fixed Point Theorem;Risk Neutral Probability Measure;Non-terminal States;CRRA Utility Function;POMDP;Trace Experience;MDP.;Module III;Riskless Asset;Atomic Experience;Policy Iteration;Risky Asset;Function Approximation;ADP;PG;Dynamic Programming Algorithms;Stockout Cost;Eligibility Traces
