Overview
What this challenge is about.
Implement tabular Q-learning on a warehouse simulator with 8,000 states and 6 slotting actions. Earn a verifiable certificate.
The scenario
The startup (around 25 staff, Series A) serves 8 mid-market 3PLs in the Benelux and labour costs are 60-70 percent of operator P&L, so even modest travel-time wins drive contract renewals.
The Brief
What you'll do, and what you'll demonstrate.
Use tabular Q-learning to learn a warehouse slotting policy that materially beats the current rule-based heuristic on simulated demand.
Earning criteria — what you'll demonstrate
- Implement tabular Q-learning with epsilon-greedy exploration from scratch
- Tune exploration schedules and learning rates for convergence
- Evaluate RL policies against a non-RL baseline on operationally meaningful metrics
- Communicate RL results to a non-technical operations audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Tabular Rl
Apply tabular rl to solve real industry problems and demonstrate production-level capability.
- Q Learning
Apply q learning to solve real industry problems and demonstrate production-level capability.
- Epsilon Greedy
Apply epsilon greedy to solve real industry problems and demonstrate production-level capability.
- Policy Evaluation
Apply policy evaluation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Simulation
Apply simulation to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Machine Learning Engineer
Implementing tabular RL on a real operational simulator and beating a rule-based baseline is the kind of MLE win that ships in logistics products.
This challenge sharpens
- tabular-rl
- q-learning
- python
Data Scientist
Validating an RL policy against a heuristic baseline on operationally meaningful metrics is the daily craft of data scientists in logistics and operations roles.
This challenge sharpens
- policy-evaluation
- simulation
- python
Applied AI Scientist
Choosing tabular RL over deep RL because the state space allows it is exactly the kind of judgement applied AI scientists exercise.
This challenge sharpens
- tabular-rl
- epsilon-greedy
- policy-evaluation