Overview
What this challenge is about.
Train a DQN agent in a warehouse grid to beat A* routing, then report key metrics. Earn a verifiable certificate.
The scenario
The startup operates around 400 robots across 12 warehouses in the Benelux region; each percent of routing efficiency gained translates to roughly EUR 90,000 per year in throughput at current contracts.
The Brief
What you'll do, and what you'll demonstrate.
Train a DQN warehouse-routing policy that beats the production A* planner on travel time and collision rate, then recommend whether to pilot it on real robots.
Earning criteria — what you'll demonstrate
- Implement deep Q-learning with experience replay and a target network
- Design a fair comparison between a learned policy and a classical planner
- Diagnose RL training instability via diagnostic plots
- Translate experimental results into a business recommendation
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.
- Deep Q Learning
Apply deep q learning to solve real industry problems and demonstrate production-level capability.
- Reinforcement Learning
Apply reinforcement learning to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Experiment Design
Apply experiment design to solve real industry problems and demonstrate production-level capability.
- Simulation
Apply simulation to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking 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:
ML Researcher
Running a deep RL training campaign with multi-seed evaluation, diagnostic plots, and a written recommendation is the daily reality of applied ML research at any robotics company.
This challenge sharpens
- deep-q-learning
- reinforcement-learning
- experiment-design
Machine Learning Engineer
Packaging a training pipeline so anyone can rerun it, plus benchmarking against a production baseline, mirrors the MLE craft of shipping research into reproducible systems.
This challenge sharpens
- pytorch
- benchmarking
- experiment-design
Applied AI Scientist
Translating an RL training run into a costed go/no-go memo for engineering leadership is exactly what applied AI scientists ship every quarter.
This challenge sharpens
- reinforcement-learning
- benchmarking
- simulation