This document describes using reinforcement learning for stock trading with Python. It discusses implementing Q-learning to learn the optimal trading policy, using a deep Q-network to represent the action-value function. The trading environment defines the state, actions, and rewards. States represent the stock price window, actions are buy, sell, or sit, and rewards incentivize profit. The agent trains on experience samples to learn the optimal policy for making trading decisions.