The Application of Adaptive Reinforcement Learning in Dynamic Pricing Strategies
Abstract
Dynamic pricing is crucial for maximizing revenue and maintaining competitiveness in markets with fluctuating demand, perishable goods, and diverse customer preferences. Preferences in purchasing differ significantly, and traditional methods, such as rule-based algorithms and statistical scale forecasting, struggle to adapt to rapidly changing market conditions, competitive maneuvers, and evolving consumer strategies. Behavior shifts in the real world, yet these approaches frequently fail to react effectively, leading to sub-optimal pricing and decreased profitability. Profitability in dynamic markets is often hindered because past research, while applying machine learning techniques to pricing improvement, has produced models that adapt slowly to real-time changes, depend heavily on historical data, and struggle to handle multi-agent scenarios. Scenarios involving fast-changing and unpredictable environments ultimately impact overall profitability. Profitability in a dynamic marketplace is enhanced through an Adaptive Reinforcement Learning (ARL)-based pricing framework that utilizes Q-Learning and Deep Q-Networks (DQN) for real-time optimization in response to changing market conditions, competition, and inventory levels. Inventory challenges are addressed by utilizing a curated dataset that has been enhanced through feature engineering, transformation, and systematic cleaning, providing reliable inputs for training. Training strength is validated by benchmarking against fixed, rule-based models and cost-plus in controlled experimentation. Experiments highlight a reward anatomical structure that balances income, profit, efficiency, justice, and customer retention, moving beyond income-only goals. Goals are achieved by modeling pricing as a Markov-based Decision Process, where ARL agents continuously refine policies through interaction with the environment. When compared to baseline approaches, the ARL-based model's accuracy in revenue and price optimization decreased by less than 20%, indicating that it can adapt and optimize pricing techniques in intricate, cutthroat markets.References
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