  We propose a reinforcement learning (RL) approach to model optimal exercise
strategies for option-type products. We pursue the RL avenue in order to learn
the optimal action-value function of the underlying stopping problem. In
addition to retrieving the optimal Q-function at any time step, one can also
price the contract at inception. We first discuss the standard setting with one
exercise right, and later extend this framework to the case of multiple
stopping opportunities in the presence of constraints. We propose to
approximate the Q-function with a deep neural network, which does not require
the specification of basis functions as in the least-squares Monte Carlo
framework and is scalable to higher dimensions. We derive a lower bound on the
option price obtained from the trained neural network and an upper bound from
the dual formulation of the stopping problem, which can also be expressed in
terms of the Q-function. Our methodology is illustrated with examples covering
the pricing of swing options.
