Overview
What this challenge is about.
Train a PPO policy for an energy-storage trading bot, backtest it, and write a risk memo. Earn a verifiable certificate.
The scenario
The scale-up (around 80 people, 350 MWh of storage across 4 sites in Denmark and southern Sweden) earns most of its margin on the intraday market; a 10% profit lift would justify productionizing the policy.
The Brief
What you'll do, and what you'll demonstrate.
Train and backtest a PPO bidding policy for grid-scale battery storage and quantify whether it beats the rule-based baseline net of degradation and risk.
Earning criteria — what you'll demonstrate
- Implement and tune Proximal Policy Optimization on a continuous-control problem
- Design a realistic RL environment around a physical system with degradation costs
- Backtest a learned policy with held-out time periods to detect overfitting
- Communicate RL results to a non-ML quant audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Deep Reinforcement Learning
Master · Reinforcement Learning
Strong alignment
This challenge maps to Deep Reinforcement Learning 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.
- Policy Gradients
Apply policy gradients to solve real industry problems and demonstrate production-level capability.
- Ppo
Apply ppo 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.
- Backtesting
Apply backtesting to solve real industry problems and demonstrate production-level capability.
- Environment Design
Apply environment design to solve real industry problems and demonstrate production-level capability.
- Risk Analysis
Apply risk analysis 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
Coupling RL training with rigorous backtests and a trader-facing memo is core applied-AI-scientist work at any quant or climate-tech firm.
This challenge sharpens
- ppo
- backtesting
- risk-analysis
ML Researcher
Designing a faithful RL environment around a physical system with degradation costs is the kind of problem ML researchers tackle in industry research labs.
This challenge sharpens
- policy-gradients
- environment-design
- reinforcement-learning
Data Scientist
Walk-forward evaluation and overfitting analysis on time-series data is the data-scientist craft that transfers to any forecasting or trading role.
This challenge sharpens
- backtesting
- risk-analysis
- environment-design