  Bimodal, stochastic environments present a challenge to typical Reinforcement
Learning problems. This problem is one that is surprisingly common in real
world applications, being particularly applicable to pricing problems. In this
paper we present a novel learning approach to the tabular Q-learning algorithm,
tailored to tackling these specific challenges by using batch updates. A
simulation of pricing problem is used as a testbed to compare a typically
updated agent with a batch learning agent. The batch learning agents are shown
to be both more effective than the typically-trained agents, and to be more
resilient to the fluctuations in a large stochastic environment. This work has
a significant potential to enable practical, industrial deployment of
Reinforcement Learning in the context of pricing and others.
