CARLOS employs an aggregate deep neural network trained on progressively finer time grids with adaptive sampling to learn continuous-time exercise boundaries for optimal stopping, delivering higher values than discrete Bermudan methods.
and Shevchenko, Pavel V
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Continuous-time Optimal Stopping through Deep Reinforcement Learning
CARLOS employs an aggregate deep neural network trained on progressively finer time grids with adaptive sampling to learn continuous-time exercise boundaries for optimal stopping, delivering higher values than discrete Bermudan methods.