New Year Offer End Date: 30th April 2024
Program
Reinforcement Learning for Dynamic Pricing
Optimize pricing in two-sided markets—RL, constraints, and platform strategy
Skills you will gain:
About Program:
A hands-on workshop on building dynamic pricing systems with reinforcement learning—from demand modeling and simulators to training bandit/RL agents for revenue optimization in platforms and two-sided markets, with practical guardrails for responsible deployment.
Aim: Build and evaluate RL-based dynamic pricing models in Python for revenue optimization in platforms and marketplaces.
Program Objectives:
- Understand demand elasticity and revenue management metrics
- Build a pricing simulator for safe experimentation
- Create baseline pricing models for benchmarking
- Train bandit and RL agents for dynamic pricing
- Model platform/two-sided market dynamics and constraints
- Simulate competition with multi-agent pricing scenarios
- Evaluate policies with offline/robust metrics
- Apply guardrails (price caps, fairness, governance)
What you will learn?
💹 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
Mentor Profile
Fee Plan
INR 1999 /- OR USD 50
Get an e-Certificate of Participation!
Intended For :
- UG/PG/PhD students, researchers, and faculty in AI/ML, economics, marketing, or operations
- Pricing, revenue management, growth, product, and marketplace/platform professionals
- Data scientists/analysts interested in RL and optimization
- Basic Python required; ML/RL fundamentals are helpful but not mandatory
Career Supporting Skills
Program Outcomes
- Build an end-to-end dynamic pricing pipeline in Python
- Create a reusable pricing simulator for safe testing
- Train and benchmark bandit/RL pricing policies
- Prototype platform/two-sided pricing with competition scenarios
- Validate policies using offline evaluation and business metrics
- Implement responsible pricing guardrails and deliver a capstone notebook