C51 Distributional DQN (v0.8) for bridge fleet maintenance optimization. Implements categorical return distributions (Bellemare et al., PMLR 2017) with 300x speedup via vectorized projection. Combines Noisy Networks, Dueling DQN, Double DQN, PER, and n-step learning. Validated on 200-bridge fleet: +3,173 reward in 83 min (25k episodes).

This repository contains a collection of beginner-to-intermediate Jupyter Notebooks that demonstrate the core features and functionalities of **NumPy**, the fundamental package for scientific computing with Python.

Исследование производительности операций с данными и статистический анализ товаров бисера. Сравнение медленных и оптимизированных методов обработки данных, проверка статистических гипотез и выявление закономерностей.

This repository contains a Jupyter notebook that benchmarks the execution time of element-wise multiplication using: - ✅ Python Lists - ✅ NumPy Arrays