  Abstract In this work, we build two environments, namely the modified QLBS
and RLOP models, from a mathematics perspective which enables RL methods in
option pricing through replicating by portfolio. We implement the environment
specifications (the source code can be found at
https://github.com/owen8877/RLOP), the learning algorithm, and agent
parametrization by a neural network. The learned optimal hedging strategy is
compared against the BS prediction. The effect of various factors is considered
and studied based on how they affect the optimal price and position.
