Linear least-squares algorithms for temporal difference learning
Machine Learning - Special issue on reinforcement learning
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Least-squares policy iteration
The Journal of Machine Learning Research
50th ANNIVERSARY ARTICLE: Option Pricing: Valuation Models and Applications
Management Science
Regression methods for pricing complex American-style options
IEEE Transactions on Neural Networks
Learning to trade via direct reinforcement
IEEE Transactions on Neural Networks
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Options are important financial instruments, whose prices are usually determined by computational methods. Computational finance is a compelling application area for reinforcement learning research, where hard sequential decision making problems abound and have great practical significance. In this paper, we investigate reinforcement learning methods, in particular, least squares policy iteration (LSPI), for the problem of learning an exercise policy for American options. We also investigate a method by Tsitsiklis and Van Roy, referred to as FQI. We compare LSPI and FQI with LSM, the standard least squares Monte Carlo method from the finance community. We evaluate their performance on both real and synthetic data. The results show that the exercise policies discovered by LSPI and FQI gain larger payoffs than those discovered by LSM, on both real and synthetic data. Our work shows that solution methods developed in reinforcement learning can advance the state of the art in an important and challenging application area, and demonstrates furthermore that computational finance remains an under-explored area for deployment of reinforcement learning methods.