Dynamic Pricing with Multi-Armed Bandit: Learning by Doing

In the vast world of decision-making problems, one dilemma is particularly owned by Reinforcement Learning strategies: exploration versus exploitation. Imagine walking into a casino with rows of slot machines (also known as “one-armed bandits”) where each machine pays out a different, unknown reward. Do you explore and play each machine to discover which one has the highest payout, or do you stick to one machine, hoping it’s the jackpot? This metaphorical scenario underpins the concept of the Multi-armed Bandit (MAB) problem. The objective is to find a strategy that maximizes the rewards over a series of plays. While exploration offers new insights, exploitation leverages the information you already possess. Now, transpose this principle to dynamic pricing in a retail scenario. Suppose you are an e-commerce store owner with a new product. You aren’t certain about its optimal selling price. How do you set a price that maximizes your revenue? Should you explore different prices to understand customer willingness to pay, or should you exploit a price that has been performing well historically? Dynamic pricing is essentially a MAB problem in disguise. At each time step, every candidate price point can be seen as an “arm” of a slot machine and the revenue generated from that price is its “reward.” Another way to see this is that the objective of dynamic pricing is to swiftly and accurately measure how a customer base’s demand reacts to varying price points. In simpler terms, the aim is to pinpoint the demand curve that best mirrors customer behavior. <a href="https://towardsdatascience.com/dynamic-pricing-with-multi-armed-bandit-learning-by-doing-3e4550ed02ac">Visit Now</a>