Reinforcement Learning for Dynamic Pricing

Aim

Workshop Objectives

Workshop Structure

💹 Day 1 — Hands-On Pricing Data, Demand Modeling & Simulation

• Focus: Building demand-response foundations and a safe pricing environment for experimentation
• Hands-On Activities:
  • Defining pricing objectives, constraints, and KPIs (revenue, margin, conversion, churn)
  • Demand forecasting and elasticity estimation using baseline models
  • Designing a pricing simulator with seasonality, customer response, and non-stationary demand
  • Generating synthetic marketplace datasets for controlled pricing experiments
  • Packaging the simulator and baseline workflow into reusable notebooks

🤖 Day 2 — Hands-On Bandits to Reinforcement Learning for Dynamic Pricing

• Focus: Training learning-based pricing policies and benchmarking performance
• Hands-On Activities:
  • Implementing multi-armed and contextual bandits for price selection
  • Formulating dynamic pricing as an MDP (state, action, reward, delayed effects)
  • Training RL pricing agents and comparing against rule-based and forecasting baselines
  • Evaluating policies under changing demand regimes and exploration strategies
  • Building an evaluation dashboard with business-aligned metrics

📊 Day 3 — Hands-On Platform Pricing, Competition & Responsible Deployment

• Focus: Applying RL pricing to platforms and marketplaces with competition and governance constraints
• Hands-On Activities:
  • Modeling two-sided market dynamics (supply–demand balance, take-rate, network effects)
  • Simulating competitive pricing scenarios and introducing multi-agent pricing dynamics
  • Offline evaluation and robustness checks for safe deployment
  • Implementing guardrails: price caps, fairness constraints, and risk-aware objectives
  • Packaging a capstone notebook and deployment checklist for real-world use

Who Should Enrol?

Important Dates

Workshop Outcomes

Fee Structure

What You’ll Gain