rl-foundations

What problem does it solve?

This skill provides the rigorous theoretical foundation for reinforcement learning, enabling learners to reason about MDPs, value functions, Bellman equations, and optimal policies rather than just implementing algorithms by rote.

Core Features & Use Cases

• MDP fundamentals: Formal definitions, Markov property, and problem framing for sequential decision making.
• Value functions & Bellman equations: Intuition, derivations, and practical implications for policy evaluation and improvement.
• Policy concepts: Evaluation, improvement, and greedy vs exploration strategies; suitable for coursework, interviews, and planning algorithm design.
• Use Case: A researcher uses these foundations to design and reason about novel RL algorithms before coding.

Quick Start

Start by asking for a concise explanation of MDPs or derivations of Bellman equations; e.g., "Explain the Bellman backup for V(s)."