Prerequisites

5–8 questions to check readiness after prerequisites. Solutions included.

What to learn before or alongside reinforcement learning—math, programming, and ML basics.

Python basics for RL: data structures, classes, functions, control flow, and OOP.

NumPy for RL: arrays, indexing, broadcasting, random, and batch operations.

Pandas for RL: DataFrames, Series, grouping, and logging metrics.

What to plot, how to read learning curves, and when to use Matplotlib vs Chart.js for RL.

Matplotlib for RL: learning curves, subplots, heatmaps, and saving figures.

PyTorch for RL: tensors, autograd, nn.Module, optimizers, and GPU.

TensorFlow and Keras for RL: models, GradientTape, optimizers, and GPU.

Standard RL environments: reset, step, spaces, wrappers, and seeding.

JAX, stable-baselines3, wandb, and other RL-related tools.

Python, NumPy, PyTorch, Gym/Gymnasium, and related tools the curriculum assumes. Complete tasks on the prerequisites index, then the Phase 2 quiz.