Build skills
that ship agents.
Structured skill tracks for RL practitioners and engineering teams — from environment setup to production deployment. Every skill is hands-on, reproducible, and built around real workloads.
Foundations of Reinforcement Learning
7 skills · ~8 hrsBuild a rigorous mental model before writing a single line of training code. Covers MDPs, value functions, policy gradients, and the intuitions that separate practitioners from tutorial-followers.
What is Reinforcement Learning?
The agent–environment loop: observations, actions, rewards, and why RL is fundamentally different from supervised learning. The concepts your intuition will rely on for everything that follows.
Markov Decision Processes
States, actions, transitions, and reward functions. The mathematical backbone of every RL problem — stated precisely so you can identify MDPs in the wild.
Value Functions & Bellman Equations
V(s), Q(s,a), and the Bellman operator. Understand why temporal credit assignment is hard — and how value functions solve it.
Policy Gradients from Scratch
Derive REINFORCE by hand. The log-derivative trick, variance reduction baselines, and why policy gradient methods dominate continuous control.
Exploration vs. Exploitation
ε-greedy to entropy regularisation. Know when your agent is stuck in a local optimum — and the interventions that actually work at scale.
Reward Function Design
Reward hacking, shaping, and specification. Why a cleverly optimised reward often produces unwanted behaviour — and the methods that catch it before deployment.
The Sample Efficiency Problem
On-policy vs. off-policy, replay buffers, and model-based methods. Why some algorithms converge in hours while others require a week of compute.
Start training today
All tracks are free — always
Deploy your first agent
Book a 20-minute demo with our team