No title
This thesis investigates the suitability of reinforcement learning (RL) for solving the optimal execution problem in financial trading. It evaluates the strengths, limitations, and practical considerations of applying RL techniques to this task. Several RL algorithms were implemented and tested, including Double Deep Q-Network (Double DQN) and Proximal Policy Optimization (PPO), both of which achi
