Master deep reinforcement learning Python through real projects. Train robots, beat games, and explore AI-powered trading. Start learning now! Read more.
Welcome, I’m Riad! With over five years in AI and Machine Learning, I've developed and deployed advanced algorithms across various domains, including deep learning, reinforcement learning, AI-driven image generation, and Brain-Computer Interface (BCI) technologies. On Kaggle, I earned silver medals in both the MOA Competition and the Pet Finder Challenge. Beyond competitions, I've created innovative AI solutions that bridge human neural activity with digital systems. I'm passionate ab
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About This Course
Who this course is for:
- Individuals looking to explore artificial intelligence and deep learning
- Anyone curious about how intelligent robots are created using reinforcement learning
What you’ll learn:Â
- Core concepts of deep reinforcement learning and how to apply them
- How to build and train your own neural networks
- Implementation of 5 hands-on reinforcement learning projects
- Techniques to improve the performance and intelligence of your AI agents
Requirements:Â
- Familiarity with Python libraries: NumPy, Matplotlib, and Pandas
- Basic understanding of gradient descent
- Knowledge of object-oriented programming
- General understanding of deep learning principles
Welcome to Deep Reinforcement Learning using Python!
Have you ever wondered how robots can learn to solve complex tasks, outperform humans in strategy games, or even trade stocks? In this course, you’ll explore the exciting intersection of deep learning and reinforcement learning—two cutting-edge subfields of machine learning—through practical projects and hands-on coding in Python.
Deep reinforcement learning combines the decision-making power of reinforcement learning with the pattern recognition abilities of deep learning using neural networks. This course walks you through every major concept and shows you how to apply them to real-world problems, making it perfect for those who want to gain experience in building intelligent systems.
You’ll develop AI agents that can:
- Navigate environments
- Master games like Grid World, Flappy Bird, and Ms. Pacman
- Solve continuous control problems like Mountain Car
- Perform stock trading using decision-based learning
Here’s what you’ll cover, step by step:
Section 1: Introduction to Deep Reinforcement Learning
- Learn the foundations: policies, value functions, Q-functions, and how neural networks are integrated.
Section 2: Setting Up the Environment
- Create your virtual environment and install all the necessary packages for running deep reinforcement learning Python projects.
Section 3: Grid World Game & Deep Q-Learning
- Build your first smart agent to solve the Grid World Game.
- Understand training, exploration, and exploitation strategies.
Section 4: Mountain Car Game & Sparse Rewards
- Tackle sparse rewards by implementing Intrinsic Curiosity Module (ICM) and Random Network Distillation (RND).
- Train a robot to solve the Mountain Car challenge.
Section 5: Flappy Bird & Advanced Q-Networks
- Develop a smart agent for Flappy Bird using variants like:
- Dueling Q-Network
- Prioritized Experience Replay
- Two-Step Q-Network
Section 6: Ms. Pacman & Noisy Networks
- Build a robot to play Ms. Pacman using advanced techniques including:
- Noisy Q-Networks
- Double Q-Learning
- N-Step Bootstrapping
Section 7: Stock Trading with Deep Q-Learning
- Create an AI agent to make intelligent trading decisions in the stock market using reinforcement learning strategies.
Our Promise to You
By the end of this course, you’ll not only understand the theory behind deep reinforcement learning Python applications, but you’ll also have built a portfolio of working AI projects. Whether you’re aiming to launch a career in AI or simply looking to expand your skills, this course provides practical, expert-guided experience that sets you apart.
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.
Discover your potential—my courses are here to guide your growth every step of the way.
Keep Learning and Head to Our Blog Posts For More Actionable Tips and Advanced Strategies!
Course Curriculum
| Section 1 - An Introduction To Deep Reinforcement Learning | |||
| What is Reinforcement Learning? | 00:00:00 | ||
| Policy, Value Function And Q Function | 00:00:00 | ||
| What Are Neural Networks? | 00:00:00 | ||
| Optimal Q Function | 00:00:00 | ||
| Quiz for Unit 1 | Unlimited | ||
| Section 2 - Setting Up The Environment | |||
| Creating Anaconda Environment | 00:00:00 | ||
| Gym Package | 00:00:00 | ||
| How To Run The Code Of Each Section | 00:00:00 | ||
| Section 3 - Grid World Game & Deep Q-Learning | |||
| What Is the Grid World Game? | 00:00:00 | ||
| How To Use the Grid World Environment | 00:00:00 | ||
| How To Build Your Network | 00:00:00 | ||
| How To Build Your First Q-Network Using PyTorch | 00:00:00 | ||
| How To Make Your Neural Network Learn | 00:00:00 | ||
| Exploration & Exploitation Using Epsilon-Greedy | 00:00:00 | ||
| Training Your Neural Network Using PyTorch Part 1 | 00:00:00 | ||
| Training Your Neural Network Using PyTorch Part 2 | 00:00:00 | ||
| Batch Training | 00:00:00 | ||
| Train On Batches Python Code | 00:00:00 | ||
| Reward Metric | 00:00:00 | ||
| Target Network | 00:00:00 | ||
| Train Your Agent With Target Network Python Code | 00:00:00 | ||
| Quiz for Unit 3 | Unlimited | ||
| Section 4 - Mountain Car Game & Deep Q-Learning | |||
| Mountain Car In Python | 00:00:00 | ||
| Dynamics Network | 00:00:00 | ||
| Epsilon Greedy Strategy Mountain Car Game In Python | 00:00:00 | ||
| Dynamics Network With Python | 00:00:00 | ||
| Multi-Variate Gaussian Distribution | 00:00:00 | ||
| Multi-Variate Gaussian Distribution With Python | 00:00:00 | ||
| Model-Based Exploration Strategy With Mountain Car In Python | 00:00:00 | ||
| What Is ICM Module? | 00:00:00 | ||
| Filter Network | 00:00:00 | ||
| Building Filter Net Python Code | 00:00:00 | ||
| Inverse Network | 00:00:00 | ||
| Building Inverse Net Python Code | 00:00:00 | ||
| Forward Network | 00:00:00 | ||
| Building Forward Network Python Code | 00:00:00 | ||
| Building Agent Q Network & Target Q Network Python Code | 00:00:00 | ||
| Training Q Network With ICM | 00:00:00 | ||
| Training Agent Q Network With ICM Python Code | 00:00:00 | ||
| What Is RND Module? | 00:00:00 | ||
| Building P Net & T Net Python Code | 00:00:00 | ||
| Training Agent Q Network With RND Module | 00:00:00 | ||
| Quiz for Unit 4 Part 1 | Unlimited | ||
| Quiz for Unit 4 Part 2 | Unlimited | ||
| Section 5 - Flappy Bird Game & Deep Q-Learning | |||
| Flappy Bird Game | 00:00:00 | ||
| Flappy Bird Game Python Code | 00:00:00 | ||
| Building Convolution Q Network | 00:00:00 | ||
| Conv Q Network With Epsilon Greedy Approach Python Code | 00:00:00 | ||
| 2-Steps Q Network | 00:00:00 | ||
| 2-Steps Q Network Python Code | 00:00:00 | ||
| Prioritized Experience Replay Buffer | 00:00:00 | ||
| Prioritized Experience Replay Buffer Python Code | 00:00:00 | ||
| Dueling Q Network | 00:00:00 | ||
| Dueling Q Network Python Code | 00:00:00 | ||
| Quiz for Unit 5 | Unlimited | ||
| Section 6 - Ms. Pacman Game & Deep Q-Learning | |||
| Ms. Pacman Game | 00:00:00 | ||
| Ms. Pacman Game Python Code | 00:00:00 | ||
| Basic Q Network Python Code | 00:00:00 | ||
| N-Steps Q Network | 00:00:00 | ||
| N-Steps Q Network Python Code | 00:00:00 | ||
| Noisy Q Network | 00:00:00 | ||
| Noisy Q Network Python Code | 00:00:00 | ||
| Noisy Double Dueling Q Network Python Code | 00:00:00 | ||
| Section 7 - Stock Trading & Deep Q-Learning | |||
| Basics Of Trading | 00:00:00 | ||
| Stock Data Preprocessing | 00:00:00 | ||
| Building The Trading Environment | 00:00:00 | ||
| Building Dueling Conv1d Q Network | 00:00:00 | ||
| Train Your Trading Robot | 00:00:00 | ||
| Section 8 - Downloadable Resources | |||
| Downloadable Resources | 00:00:00 | ||
About This Course
Who this course is for:
- Individuals looking to explore artificial intelligence and deep learning
- Anyone curious about how intelligent robots are created using reinforcement learning
What you’ll learn:Â
- Core concepts of deep reinforcement learning and how to apply them
- How to build and train your own neural networks
- Implementation of 5 hands-on reinforcement learning projects
- Techniques to improve the performance and intelligence of your AI agents
Requirements:Â
- Familiarity with Python libraries: NumPy, Matplotlib, and Pandas
- Basic understanding of gradient descent
- Knowledge of object-oriented programming
- General understanding of deep learning principles
Welcome to Deep Reinforcement Learning using Python!
Have you ever wondered how robots can learn to solve complex tasks, outperform humans in strategy games, or even trade stocks? In this course, you’ll explore the exciting intersection of deep learning and reinforcement learning—two cutting-edge subfields of machine learning—through practical projects and hands-on coding in Python.
Deep reinforcement learning combines the decision-making power of reinforcement learning with the pattern recognition abilities of deep learning using neural networks. This course walks you through every major concept and shows you how to apply them to real-world problems, making it perfect for those who want to gain experience in building intelligent systems.
You’ll develop AI agents that can:
- Navigate environments
- Master games like Grid World, Flappy Bird, and Ms. Pacman
- Solve continuous control problems like Mountain Car
- Perform stock trading using decision-based learning
Here’s what you’ll cover, step by step:
Section 1: Introduction to Deep Reinforcement Learning
- Learn the foundations: policies, value functions, Q-functions, and how neural networks are integrated.
Section 2: Setting Up the Environment
- Create your virtual environment and install all the necessary packages for running deep reinforcement learning Python projects.
Section 3: Grid World Game & Deep Q-Learning
- Build your first smart agent to solve the Grid World Game.
- Understand training, exploration, and exploitation strategies.
Section 4: Mountain Car Game & Sparse Rewards
- Tackle sparse rewards by implementing Intrinsic Curiosity Module (ICM) and Random Network Distillation (RND).
- Train a robot to solve the Mountain Car challenge.
Section 5: Flappy Bird & Advanced Q-Networks
- Develop a smart agent for Flappy Bird using variants like:
- Dueling Q-Network
- Prioritized Experience Replay
- Two-Step Q-Network
Section 6: Ms. Pacman & Noisy Networks
- Build a robot to play Ms. Pacman using advanced techniques including:
- Noisy Q-Networks
- Double Q-Learning
- N-Step Bootstrapping
Section 7: Stock Trading with Deep Q-Learning
- Create an AI agent to make intelligent trading decisions in the stock market using reinforcement learning strategies.
Our Promise to You
By the end of this course, you’ll not only understand the theory behind deep reinforcement learning Python applications, but you’ll also have built a portfolio of working AI projects. Whether you’re aiming to launch a career in AI or simply looking to expand your skills, this course provides practical, expert-guided experience that sets you apart.
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.
Discover your potential—my courses are here to guide your growth every step of the way.
Keep Learning and Head to Our Blog Posts For More Actionable Tips and Advanced Strategies!
Course Curriculum
| Section 1 - An Introduction To Deep Reinforcement Learning | |||
| What is Reinforcement Learning? | 00:00:00 | ||
| Policy, Value Function And Q Function | 00:00:00 | ||
| What Are Neural Networks? | 00:00:00 | ||
| Optimal Q Function | 00:00:00 | ||
| Quiz for Unit 1 | Unlimited | ||
| Section 2 - Setting Up The Environment | |||
| Creating Anaconda Environment | 00:00:00 | ||
| Gym Package | 00:00:00 | ||
| How To Run The Code Of Each Section | 00:00:00 | ||
| Section 3 - Grid World Game & Deep Q-Learning | |||
| What Is the Grid World Game? | 00:00:00 | ||
| How To Use the Grid World Environment | 00:00:00 | ||
| How To Build Your Network | 00:00:00 | ||
| How To Build Your First Q-Network Using PyTorch | 00:00:00 | ||
| How To Make Your Neural Network Learn | 00:00:00 | ||
| Exploration & Exploitation Using Epsilon-Greedy | 00:00:00 | ||
| Training Your Neural Network Using PyTorch Part 1 | 00:00:00 | ||
| Training Your Neural Network Using PyTorch Part 2 | 00:00:00 | ||
| Batch Training | 00:00:00 | ||
| Train On Batches Python Code | 00:00:00 | ||
| Reward Metric | 00:00:00 | ||
| Target Network | 00:00:00 | ||
| Train Your Agent With Target Network Python Code | 00:00:00 | ||
| Quiz for Unit 3 | Unlimited | ||
| Section 4 - Mountain Car Game & Deep Q-Learning | |||
| Mountain Car In Python | 00:00:00 | ||
| Dynamics Network | 00:00:00 | ||
| Epsilon Greedy Strategy Mountain Car Game In Python | 00:00:00 | ||
| Dynamics Network With Python | 00:00:00 | ||
| Multi-Variate Gaussian Distribution | 00:00:00 | ||
| Multi-Variate Gaussian Distribution With Python | 00:00:00 | ||
| Model-Based Exploration Strategy With Mountain Car In Python | 00:00:00 | ||
| What Is ICM Module? | 00:00:00 | ||
| Filter Network | 00:00:00 | ||
| Building Filter Net Python Code | 00:00:00 | ||
| Inverse Network | 00:00:00 | ||
| Building Inverse Net Python Code | 00:00:00 | ||
| Forward Network | 00:00:00 | ||
| Building Forward Network Python Code | 00:00:00 | ||
| Building Agent Q Network & Target Q Network Python Code | 00:00:00 | ||
| Training Q Network With ICM | 00:00:00 | ||
| Training Agent Q Network With ICM Python Code | 00:00:00 | ||
| What Is RND Module? | 00:00:00 | ||
| Building P Net & T Net Python Code | 00:00:00 | ||
| Training Agent Q Network With RND Module | 00:00:00 | ||
| Quiz for Unit 4 Part 1 | Unlimited | ||
| Quiz for Unit 4 Part 2 | Unlimited | ||
| Section 5 - Flappy Bird Game & Deep Q-Learning | |||
| Flappy Bird Game | 00:00:00 | ||
| Flappy Bird Game Python Code | 00:00:00 | ||
| Building Convolution Q Network | 00:00:00 | ||
| Conv Q Network With Epsilon Greedy Approach Python Code | 00:00:00 | ||
| 2-Steps Q Network | 00:00:00 | ||
| 2-Steps Q Network Python Code | 00:00:00 | ||
| Prioritized Experience Replay Buffer | 00:00:00 | ||
| Prioritized Experience Replay Buffer Python Code | 00:00:00 | ||
| Dueling Q Network | 00:00:00 | ||
| Dueling Q Network Python Code | 00:00:00 | ||
| Quiz for Unit 5 | Unlimited | ||
| Section 6 - Ms. Pacman Game & Deep Q-Learning | |||
| Ms. Pacman Game | 00:00:00 | ||
| Ms. Pacman Game Python Code | 00:00:00 | ||
| Basic Q Network Python Code | 00:00:00 | ||
| N-Steps Q Network | 00:00:00 | ||
| N-Steps Q Network Python Code | 00:00:00 | ||
| Noisy Q Network | 00:00:00 | ||
| Noisy Q Network Python Code | 00:00:00 | ||
| Noisy Double Dueling Q Network Python Code | 00:00:00 | ||
| Section 7 - Stock Trading & Deep Q-Learning | |||
| Basics Of Trading | 00:00:00 | ||
| Stock Data Preprocessing | 00:00:00 | ||
| Building The Trading Environment | 00:00:00 | ||
| Building Dueling Conv1d Q Network | 00:00:00 | ||
| Train Your Trading Robot | 00:00:00 | ||
| Section 8 - Downloadable Resources | |||
| Downloadable Resources | 00:00:00 | ||
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