Proximal Policy Optimization in Action: Real-Time Pricing with Trust-Region Learning

Author(s): Shenggang Li

Originally published on Towards AI.

A Practical Guide to Actor–Critic Methods for Dynamic, Data-Driven Decisions

Every time a customer opens an app or website, the platform must set a surcharge in milliseconds to balance rider supply, demand spikes, and weather. Simple if-then rules can’t adapt fast enough, while naive trial-and-error risks wasted revenue or angry customers.

This article explores Proximal Policy Optimization (PPO) in the context of real-time pricing policies for dynamic decision making, emphasizing its efficiency and adaptability compared to traditional methods. The author presents a practical overview of PPO, including its core mechanisms and broader applications in business scenarios like delivery surcharges. Through experimental evaluations against standard Actor-Critic methods, the article demonstrates how PPO consistently achieves balanced pricing decisions, enhancing profitability while minimizing customer dissatisfaction in volatile environments.

Read the full blog for free on Medium.

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Published via Towards AI