Reinforcement Learning
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Three types of ML: supervised, unsupervised, and reinforcement — and why learning from data beats hand-written rules.
Gridworld discounted return from a sequence of actions.
Full course outline in basic-to-advanced order. Every topic with links to curriculum, prerequisites, and learning path.
Reinforcement learning explained with everyday analogies — no math, no code. Read this before starting the curriculum.
The CartPole (Inverted Pendulum) environment: state, actions, and solving it with value-based or policy-based methods.
Apply Q-learning and function approximation to a simplified stock trading environment—data, Q-model, design, and code.