Compare MDP Solvers for a Smart-Grid Battery Dispatch Pilot
Overview
What this challenge is about.
Compare MDP Solvers for a Smart-Grid Battery Dispatch Pilot. Intermediate challenge in code. Writing production code that solves real engineering problems, e...
The Brief
What you'll do, and what you'll demonstrate.
Solve a smart-grid battery dispatch MDP three ways and recommend one method for the team's roadmap.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Formulate a real-world control problem as an MDP
- Implement and contrast classical and learning-based MDP solvers
- Reason about convergence and sample efficiency trade-offs
- Write a technical comparison for a graduate-level audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Artificial Intelligence: Principles and Techniques
Master · Ai Systems
Strong alignment
This challenge maps to Artificial Intelligence: Principles and Techniques at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Markov Decision Processes
Apply markov decision processes to solve real industry problems and demonstrate production-level capability.
- Value Iteration
Apply value iteration to solve real industry problems and demonstrate production-level capability.
- Policy Iteration
Apply policy iteration 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.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Reinforcement Learning
Apply reinforcement learning 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:
Applied AI Scientist
Translating a control problem into an MDP and choosing a method defensibly is core applied-AI work in any energy-tech team.
This challenge sharpens
- markov-decision-processes
- reinforcement-learning
- value-iteration
ML Researcher
Comparing classical and learning-based MDP solvers with honest convergence analysis is the literal practice ML researchers cut their teeth on.
This challenge sharpens
- q-learning
- value-iteration
- policy-iteration
Machine Learning Engineer
Reproducible, defensible, well-instrumented experiments are the MLE's deliverable when productionizing an RL method.
This challenge sharpens
- python
- q-learning
- reinforcement-learning