mlsanityData to 14 Oct 2026

Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment

arXiv 2410.21109 ↗First posted 28 Oct 2024cs.LG econ.GN q-fin.ECcited by 3

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

We study the dynamic pricing and replenishment problems under inconsistent decision frequencies. Different from the traditional demand assumption, the discreteness of demand and the parameter within the Poisson distribution as a function of price introduce complexity into analyzing the problem property. We demonstrate the concavity of the single-period profit function with respect to product price and inventory within their respective domains. The demand model is enhanced by integrating a decision tree-based machine learning approach, trained on comprehensive market data. Employing a two-timescale stochastic approximation scheme, we address the discrepancies in decision frequencies between pricing and replenishment, ensuring convergence to local optimum. We further refine our methodology by incorporating deep reinforcement learning (DRL) techniques and propose a fast-slow dual-agent DRL algorithm. In this approach, two agents handle pricing and inventory and are updated on different scales. Numerical results from both single and multiple products scenarios validate the effectiveness of our methods.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 63 references that are in the map. First posted on arXiv 28 Oct 2024.

Builds on

11 of 63 references.

References · 11

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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2025A New Stochastic Approximation Method for Gradient-based Simulated Parameter Estimationcited by 1
  2. 2024Deep Generative Demand Learning for Newsvendor and Pricingcited by 0
  3. 2026Two-Timescale Hierarchical Reinforcement Learning for Resilient Operationscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Structure-Informed Deep Reinforcement Learning for Inventory Management
  2. 2026OR-Transformer: Scaling Real-Time Decision-Making to 1,000 Items
  3. 2026Multi-Agent Reinforcement Learning for Dynamic Pricing: Balancing Profitability,Stability and Fairness
  4. 2026Learning to replenish: A hybrid deep reinforcement learning for dynamic inventory management in the pharmaceutical supply chains
  5. 2025Deep RL Dual Sourcing Inventory Management with Supply and Capacity Risk Awareness
  6. 2025Closing the Loop: Coordinating Inventory and Recommendation via Deep Reinforcement Learning on Multiple Timescales
  7. 2024Deep Reinforcement Learning Agents for Strategic Production Policies in Microeconomic Market Simulations
  8. 2026DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management