Overview
What this challenge is about.
Train a Soft Actor-Critic agent to auto-tune HVAC setpoints for an 8-zone smart building, then earn a verifiable certificate.
The scenario
The operator manages around 1.4 million square meters of commercial space across Singapore and Kuala Lumpur; even a 5% HVAC energy reduction at the tower is worth roughly SGD 180,000 per year.
The Brief
What you'll do, and what you'll demonstrate.
Train a SAC HVAC policy that beats the rule-based controller on energy use while never violating occupant comfort bounds, and propose pilot guard rails.
Earning criteria — what you'll demonstrate
- Implement and tune Soft Actor-Critic for continuous control
- Design a constrained reward balancing energy and comfort
- Evaluate policies seasonally on held-out weather
- Translate RL safety considerations into operational guard rails
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.
- Actor Critic
Apply actor critic to solve real industry problems and demonstrate production-level capability.
- Soft Actor Critic
Apply soft actor critic to solve real industry problems and demonstrate production-level capability.
- Continuous Control
Apply continuous control 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.
- Safety Constraints
Apply safety constraints to solve real industry problems and demonstrate production-level capability.
- 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
Training a deep RL controller against a real simulator with strict safety bounds is the kind of system MLEs ship in industrial and building-controls companies.
This challenge sharpens
- soft-actor-critic
- continuous-control
- simulation
AI Safety Researcher
Designing constrained rewards, failure-mode analyses, and operational guard rails for a learned controller is exactly the day-one work of AI safety researchers in applied settings.
This challenge sharpens
- safety-constraints
- actor-critic
- reinforcement-learning
Applied AI Scientist
Translating a research-grade SAC training run into a pilot-ready memo with quantified guard rails is core applied-AI-scientist work.
This challenge sharpens
- soft-actor-critic
- safety-constraints
- simulation