Slicing

This page covers the NumPy you need for the preliminary assessment: creating arrays, indexing, slicing, and element-wise operations. Back to Preliminary. Why this matters for RL Environments return observations as arrays; neural networks consume batches of arrays. NumPy is the standard way to represent states, reward vectors, and batches of transitions. You need to create and reshape arrays, slice them, and know the difference between element-wise and matrix multiplication. Learning objectives Create and index NumPy arrays; set rows/columns; compute element-wise products and matrix-vector products; use np.dot or @ correctly. ...