Master Deep Reinforcement Learning with Python: AI for Games & Finance

What you will learn:

Master Deep Reinforcement Learning fundamentals
Build and train neural networks using PyTorch
Develop agents for various games (Grid World, Mountain Car, Flappy Bird, Ms. Pac-Man)
Create a stock trading bot using Deep Q-Learning
Implement advanced DRL techniques: Dueling DQN, Prioritized Experience Replay, N-step Q-learning, Noisy Networks
Solve sparse reward problems using ICM and RND modules
Understand and apply exploration-exploitation strategies
Effectively preprocess financial data for AI applications
Design and build custom DRL environments
Improve your agent's learning and performance

Description

Unlock the power of AI! This comprehensive course dives deep into the world of Deep Reinforcement Learning (DRL) using Python. Learn to build intelligent agents capable of mastering complex environments, from classic games like Ms. Pac-Man and Flappy Bird to the challenging world of stock trading.

No prior experience is needed. We'll start with the fundamentals of reinforcement learning, neural networks, and Python programming. Then, we'll progressively build your skills through hands-on projects. You'll learn various DRL algorithms and techniques, such as Deep Q-Learning, and explore advanced concepts like ICM and RND modules for handling sparse rewards.

What you'll build:

Intelligent agents solving Grid World, Mountain Car, Flappy Bird, and Ms. Pac-Man
A stock trading bot using DRL
Customizable neural networks for diverse AI applications

Throughout the course, you'll gain a solid understanding of:

Reinforcement learning principles: policies, value functions, Q-functions
Neural network architectures and training
Exploration-exploitation strategies (e.g., epsilon-greedy)
Advanced DRL techniques: Dueling DQN, Prioritized Experience Replay, N-step Q-learning, Noisy Networks
Solving sparse reward problems with ICM and RND modules
Data preprocessing and building trading environments

Join now and transform your understanding of artificial intelligence!

Curriculum

Introduction to Deep Reinforcement Learning

This foundational section lays the groundwork for your DRL journey. You'll learn the core concepts of reinforcement learning, including policies, value functions, Q-functions, and the role of neural networks. Interactive quizzes reinforce your understanding at each step, ensuring a firm grasp of the basics before moving to more advanced topics.

Setting Up Your Environment

We'll guide you through setting up your Python environment using Anaconda, installing necessary packages like Gym, and getting familiar with the coding structure used throughout the course. You'll learn how to run and adapt the provided code for each project, ensuring a smooth learning experience.

Grid World Game & Deep Q-Learning

This section marks your first hands-on experience with DRL. You’ll build an intelligent agent to solve the classic Grid World game using Deep Q-Learning. We'll cover building and training your neural network in PyTorch, implementing exploration-exploitation strategies like epsilon-greedy, and optimizing training using batch training and target networks. Multiple quizzes will check your understanding at each stage.

Mountain Car Game & Deep Q-Learning

This section tackles the Mountain Car game, introducing you to more advanced techniques. You'll learn to handle sparse reward problems with the ICM (Intrinsic Curiosity Module) and RND (Random Network Distillation) modules. You'll build and implement these modules from scratch, further strengthening your DRL skills. Comprehensive explanations and Python code will ensure a complete understanding.

Flappy Bird Game & Deep Q-Learning

Conquer the challenging Flappy Bird game, using convolutional neural networks. Explore various Q-network variants, including Dueling Q-networks, prioritized experience replay, and 2-step Q-networks. These advanced techniques will significantly enhance your agent's learning capabilities.

Ms. Pac-Man Game & Deep Q-Learning

Test your DRL skills on the iconic Ms. Pac-Man game. This section introduces you to more advanced DRL algorithms, like noisy Q-networks, double Q-networks, and N-step Q-networks, equipping you with a diverse range of approaches for tackling complex environments.

Stock Trading & Deep Q-Learning

Apply your accumulated DRL expertise to a real-world application: building a stock trading bot. You'll learn about stock data preprocessing, designing a trading environment, creating a dueling convolutional Q-network, and training your agent to make optimal trading decisions.

Deal Source: real.discount