01. Project Overview

Project Introduction

Welcome to the Reinforcement Learning Trading Agent Project! In this project, you'll embark on an exciting journey to design, encode, and train a trading agent from scratch using reinforcement learning (RL). By the end of this project, you'll have a hands-on understanding of how RL can be applied to the financial markets, empowering you to create intelligent agents capable of making trading decisions.

Relevant Concepts in this Project:

• **Intermediate Python & Tensorflow: **This project requires an intermediate understanding of Python, including Tensorflow.
• Reinforcement Learning Basics: Understanding the fundamentals of RL, including key concepts such as agents, environments, actions, states, and rewards.
• Deep Q-Networks (DQN): Understanding the architecture and mechanics of DQN, a popular RL algorithm used for training trading agents.
• Reward Functions: Understanding how to design effective reward functions that guide your agent towards profitable trading strategies.
• Agent Training: Gaining practical experience in encoding and training your trading agent, including handling data, optimizing the DQN model via experience replay, and interpreting the training process output.