Showing posts with label Reinforcement Learning. Show all posts
Showing posts with label Reinforcement Learning. Show all posts
Reinforcement Learning beginner to master - AI in Python
Wednesday, October 29, 2025
Reinforcement Learning beginner to master - AI in Python
Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: A2C, REINFORCE, DQN, etc.
Preview this Course
What you'll learn
- Understand the Reinforcement Learning paradigm and the tasks that it's best suited to solve.
- Understand the process of solving a cognitive task using Reinforcement Learning
- Understand the different approaches to solving a task using Reinforcement Learning and choose the most fitting
- Implement Reinforcement Learning algorithms completely from scratch
- Fundamentally understand the learning process for each algorithm
- Debug and extend the algorithms presented
- Understand and implement new algorithms from research papers
Requirements
- Be comfortable programming in Python
- Know basic linear algebra and calculus (matrices, vectors, determinants, derivatives, etc.)
- Know basic statistics and probability theory (mean, variance, normal distribution, etc.)
Description
This is the most complete Reinforcement Learning course on Udemy. In it you will learn the basics of Reinforcement Learning, one of the three paradigms of modern artificial intelligence. You will implement from scratch adaptive algorithms that solve control tasks based on experience. You will also learn to combine these algorithms with Deep Learning techniques and neural networks, giving rise to the branch known as Deep Reinforcement Learning.
This course will give you the foundation you need to be able to understand new algorithms as they emerge. It will also prepare you for the next courses in this series, in which we will go much deeper into different branches of Reinforcement Learning and look at some of the more advanced algorithms that exist.
The course is focused on developing practical skills. Therefore, after learning the most important concepts of each family of methods, we will implement one or more of their algorithms in jupyter notebooks, from scratch.
This course is divided into three parts and covers the following topics:
Part 1 (Tabular methods):
- Markov decision process
- Dynamic programming
- Monte Carlo methods
- Time difference methods (SARSA, Q-Learning)
- N-step bootstrapping
Part 2 (Continuous state spaces):
- State aggregation
- Tile Coding
Part 3 (Deep Reinforcement Learning):
- Deep SARSA
- Deep Q-Learning
- REINFORCE
- Advantage Actor-Critic / A2C (Advantage Actor-Critic / A2C method)
Who this course is for:
- Developers who want to get a job in Machine Learning
- Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge.
- Researchers/scholars seeking to enhance their practical coding skills.
Reinforcement Learning & Deep RL Python(Theory & Projects)
Monday, March 28, 2022
Reinforcement Learning & Deep RL Python(Theory & Projects)
Reinforcement Learning: Deep Q-Learning, SARSA, Deep RL, with Car Racing and Trading Project and Project and Interview
- New
Preview this Course
What you'll learn
● The introduction and importance of Reinforcement & Deep Reinforcement Learning
● Practical explanation and live coding with Python
● Deep Reinforcement Learning applications
● Q-Learning using Python
● SARSA using Python
● Random Solutions using Python
● Hyper-parameters of Deep RL
● MDP
● Mini Project (Frozen Lake) using Python
● Open AI GYM
● Intro to Deep Learning
● Deep Learning Fundamentals
● Mini Project (CIFAR) using Pytorch
● Fundamentals of DQN
● Cart-Pole from Scratch Project using Python
● Stable Baseline 3
● Cart-Pole from Scratch Project using Stable Baseline 3
● Car Racing Game Project using Stable Baseline 3
● Trading Bot Project using Stable Baseline 3
● Interview Preparations
Requirements
● Prior knowledge of Python.
● An elementary understanding of programming.
● A willingness to learn and practice.
Description
Comprehensive Course Description:
Reinforcement Learning (RL) is a subset of machine learning. In the RL training method, desired actions are rewarded, and undesired actions are punished. In general, an RL agent can understand and interpret its environment, take actions, and also learn through trial and error.
Deep Reinforcement Learning (Deep RL) is also a subfield of machine learning. In Deep RL, intelligent machines and software are trained to learn from their actions in the same way that humans learn from experience. That is, Deep RL blends RL techniques with Deep Learning (DL) strategies.
Deep RL has the capability to solve complex problems that were unmanageable by machines in the past. Therefore, the potential applications of Deep RL in various sectors such as robotics, medicine, finance, gaming, smart grids, and more are enormous.
The phenomenal ability of Artificial Neural Networks (ANNs) to process unstructured information fast and learn like a human brain is starting to be exploited only now. We are only in the initial stages of seeing the full impact of the technology that combines the power of RL and ANNs. This latest technology has the potential to revolutionize every sphere of commerce and science.
How Is This Course Different?
In this detailed Learning by Doing course, each new theoretical explanation is followed by practical implementation. This course offers you the right balance between theory and practice. Six projects have been included in the course curriculum to simplify your learning. The focus is to teach RL and Deep RL to a beginner. Hence, we have tried our best to simplify things.
The course ‘A Complete Guide to Reinforcement & Deep Reinforcement Learning’ reflects the most in-demand workplace skills. The explanations of all the theoretical concepts are clear and concise. The instructors lay special emphasis on complex theoretical concepts, making it easier for you to understand them. The pace of the video presentation is neither fast nor slow. It’s perfect for learning. You will understand all the essential RL and Deep RL concepts and methodologies. The course is:
• Simple and easy to learn.
• Self-explanatory.
• Highly detailed.
• Practical with live coding.
• Up-to-date covering the latest knowledge of this field.
As this course is an exhaustive compilation of all the fundamental concepts, you will be motivated to learn RL and Deep RL. Your learning progress will be quick. You are certain to experience much more than what you learn. At the end of each new concept, a revision task such as Homework/activity/quiz is assigned. The solutions for these tasks are also provided. This is to assess and promote your learning. The whole process is closely linked to the concepts and methods you have already learned. A majority of these activities are coding-based, as the goal is to prepare you for real-world implementations.
In addition to high-quality video content, you will also get access to easy-to-understand course material, assessment questions, in-depth subtopic notes, and informative handouts in this course. You are welcome to contact our friendly team in case of any queries related to the course, and we assure you of a prompt response.
The course tutorials are subdivided into 145+ short HD videos. In every video, you’ll learn something new and fascinating. In addition, you’ll learn the key concepts and methodologies of RL and Deep RL, along with several practical implementations. The total runtime of the course videos is 14+ hours.
Why Should You Learn RL & Deep RL?
RL and Deep RL are the hottest research topics in the Artificial Intelligence universe.
Reinforcement learning (RL) is a subset of machine learning concerned with the actions that intelligent agents need to take in an environment in order to maximize the reward. RL is one of three essential machine learning paradigms, besides supervised learning and unsupervised learning.
Let’s look at the next hot research topic.
Deep Reinforcement Learning (Deep RL) is a subset of machine learning that blends Reinforcement Learning (RL) and Deep Learning (DL). Deep RL integrates deep learning into the solution, permitting agents to make decisions from unstructured input data without human intervention. Deep RL algorithms can take in large inputs (e.g., every pixel rendered to the user’s screen in a video game) and determine the best actions to perform to optimize an objective (e.g., attain the maximum game score).
Deep RL has been used for an assortment of applications, including but not limited to video games, oil & gas, natural language processing, computer vision, retail, education, transportation, and healthcare.
Course Content:
The comprehensive course consists of the following topics:
1. Introduction
a. Motivation
i. What is Reinforcement Learning?
ii. How is it different from other Machine Learning Frameworks?
iii. History of Reinforcement Learning
iv. Why Reinforcement Learning?
v. Real-world examples
vi. Scope of Reinforcement Learning
vii. Limitations of Reinforcement Learning
viii. Exercises and Thoughts
b. Terminologies of RL with Case Studies and Real-World Examples
i. Agent
ii. Environment
iii. Action
iv. State
v. Transition
vi. Reward
vii. Quiz/Solution
viii. Policy
ix. Planning
x. Exercises and Thoughts
2. Hands-on to Basic Concepts
a. Naïve/Random Solution
i. Intro to game
ii. Rules of the game
iii. Setups
iv. Implementation using Python
b. RL-based Solution
i. Intro to Q Table
ii. Dry Run of states
iii. How RL works
iv. Implementing RL-based solution using Python
v. Comparison of solutions
vi. Conclusion
3. Different types of RL Solutions
a. Hyper Parameters and Concepts
I. Intro to Epsilon
II. How to update epsilon
III. Quiz/Solution
IV. Gamma, Discount Factor
V. Quiz/Solution
VI. Alpha, Learning Rate
VII. Quiz/Solution
VIII. Do’s and Don’ts of Alpha
IX. Q Learning Equation
X. Optimal Value for number of Episodes
XI. When to Stop Training
b. Markov Decision Process
i. Agent-environment interaction
ii. Goals
iii. Returns
iv. Episodes
v. Value functions
vi. Optimization of policy
vii. Optimization of the value function
viii. Approximations
ix. Exercises and Thoughts
c. Q-Learning
i. Intro to QL
ii. Equation Explanation
iii. Implementation using Python
iv. Off-Policy Learning
d. SARSA
i. Intro to SARSA
ii. State, Action, Reward, State, Action
iii. Equation Explanation
iv. Implementation using Python
v. On-Policy Learning
e. Q-Learning vs. SARSA
i. Difference in Equation
ii. Difference in Implementation
iii. Pros and Cons
iv. When to use SARSA
v. When to use Q Learning
vi. Quiz/Solution
4. Mini Project Using the Above Concepts (Frozen Lake)
a. Intro to GYM
b. Gym Environment
c. Intro to Frozen Lake Game
d. Rules
e. Implementation using Python
f. Agent Evaluation
g. Conclusion
5. Deep Learning/Neural Networks
a. Deep Learning Framework
i. Intro to Pytorch
ii. Why Pytorch?
iii. Installation
iv. Tensors
v. Auto Differentiation
vi. Pytorch Practice
b. Architecture of DNN
i. Why DNN?
ii. Intro to DNN
iii. Perceptron
iv. Architecture
v. Feed Forward
vi. Quiz/Solution
vii. Activation Function
viii. Loss Function
ix. Gradient Descent
x. Weight Initialization
xi. Quiz/Solution
xii. Learning Rate
xiii. Batch Normalization
xiv. Optimizations
xv. Dropout
xvi. Early Stopping
c. Implementing DNN for CIFAR Using Python
6. Deep RL / Deep Q Network (DQN)
a. Getting to DQN
i. Intro to Deep Q Network
ii. Need of DQN
iii. Basic Concepts
iv. How DQN is related to DNN
v. Replay Memory
vi. Epsilon Greedy Strategy
vii. Quiz/Solution
viii. Policy Network
ix. Target Network
x. Weights Sharing/Target update
xi. Hyper-parameters
b. Implementing DQN
i. DQN Project – Cart and Pole using Pytorch
ii. Moving Averages
iii. Visualizing the agent
iv. Performance Evaluation
7. Car Racing Project
a. Intro to game
b. Implementation using DQN
8. Trading Project
a. Stable Baseline
b. Trading Bot using DQN
9. Interview Preparation
Successful completion of this course will enable you to:
● Relate the concepts and practical applications of Reinforcement and Deep Reinforcement Learning with real-world problems.
● Apply for the jobs related to Reinforcement and Deep Reinforcement Learning.
● Work as a freelancer for jobs related to Reinforcement and Deep Reinforcement Learning.
● Implement any project that requires Reinforcement and Deep Reinforcement Learning knowledge from scratch.
● Extend or improve the implementation of any other project for performance improvement.
● Know the theory and practical aspects of Reinforcement and Deep Reinforcement Learning.
Who this course is for:
● Beginners who know absolutely nothing about Reinforcement and Deep Reinforcement Learning.
● People who want to develop intelligent solutions.
● People who love to learn the theoretical concepts first before implementing them using Python.
● People who want to learn PySpark along with its implementation in realistic projects.
● Machine Learning or Deep Learning Lovers.
● Anyone interested in Artificial Intelligence.
Who this course is for:
● Beginners who know absolutely nothing about Reinforcement and Deep Reinforcement Learning.
● People who want to develop intelligent solutions.
● People who love to learn the theoretical concepts first before implementing them using Python.
Advanced Reinforcement Learning - AI in Python
Tuesday, March 22, 2022
Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: DDPG, TD3, SAC, NAF, HER.
- New
Preview this Course
What you'll learn
- Master some of the most advanced Reinforcement Learning algorithms.
- Learn how to create AIs that can act in a complex environment to achieve their goals.
- Create from scratch advanced Reinforcement Learning agents using Python's most popular tools (PyTorch Lightning, OpenAI gym, Brax, Optuna)
- Learn how to perform hyperparameter tuning (Choosing the best experimental conditions for our AI to learn)
- Fundamentally understand the learning process for each algorithm.
- Debug and extend the algorithms presented.
- Understand and implement new algorithms from research papers.
Requirements
- Be comfortable programming in Python
- Completing our course "Reinforcement Learning beginner to master" or being familiar with the basics of Reinforcement Learning (or watching the leveling sections included in this course).
- Know basic statistics (mean, variance, normal distribution)
Description
This is the most complete Advanced Reinforcement Learning course on Udemy. In it, you will learn to implement some of the most powerful Deep Reinforcement Learning algorithms in Python using PyTorch and PyTorch lightning. You will implement from scratch adaptive algorithms that solve control tasks based on experience. You will learn to combine these techniques with Neural Networks and Deep Learning methods to create adaptive Artificial Intelligence agents capable of solving decision-making tasks.
This course will introduce you to the state of the art in Reinforcement Learning techniques. It will also prepare you for the next courses in this series, where we will explore other advanced methods that excel in other types of task.
The course is focused on developing practical skills. Therefore, after learning the most important concepts of each family of methods, we will implement one or more of their algorithms in jupyter notebooks, from scratch.
Leveling modules:
- Refresher: The Markov decision process (MDP).
- Refresher: Q-Learning.
- Refresher: Brief introduction to Neural Networks.
- Refresher: Deep Q-Learning.
- Refresher: Policy gradient methods
Advanced Reinforcement Learning:
- PyTorch Lightning.
- Hyperparameter tuning with Optuna.
- Deep Q-Learning for continuous action spaces (Normalized advantage function - NAF).
- Deep Deterministic Policy Gradient (DDPG).
- Twin Delayed DDPG (TD3).
- Soft Actor-Critic (SAC).
- Hindsight Experience Replay (HER).
Who this course is for:
- Developers who want to get a job in Machine Learning.
- Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge.
- Robotics students and researchers.
- Engineering students and researchers.
Modern Reinforcement Learning: Actor-Critic Methods
Wednesday, January 19, 2022
Free Coupon Discount - Modern Reinforcement Learning: Actor-Critic Methods, How to Implement Cutting Edge Artificial Intelligence Research Papers in the Open AI Gym Using the PyTorch Framework | Created by Phil Tabor
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Description
In this advanced course on deep reinforcement learning, you will learn how to implement policy gradient, actor critic, deep deterministic policy gradient (DDPG), and twin delayed deep deterministic policy gradient (TD3) algorithms in a variety of challenging environments from the Open AI gym.
The course begins with a practical review of the fundamentals of reinforcement learning, including topics such as:
The Bellman Equation
Markov Decision Processes
Monte Carlo Prediction
Monte Carlo Control
Temporal Difference Prediction TD(0)
Temporal Difference Control with Q Learning
And moves straight into coding up our first agent: a blackjack playing artificial intelligence. From there we will progress to teaching an agent to balance the cart pole using Q learning.
After mastering the fundamentals, the pace quickens, and we move straight into an introduction to policy gradient methods. We cover the REINFORCE algorithm, and use it to teach an artificial intelligence to land on the moon in the lunar lander environment from the Open AI gym. Next we progress to coding up the one step actor critic algorithm, to again beat the lunar lander.
With the fundamentals out of the way, we move on to our harder projects: implementing deep reinforcement learning research papers. We will start with Deep Deterministic Policy Gradients, which is an algorithm for teaching robots to excel at a variety of continuous control tasks.
Finally, we implement a state of the art artificial intelligence algorithm: Twin Delayed Deep Deterministic Policy Gradients. This algorithm sets a new benchmark for performance in robotic control tasks, and we will demonstrate world class performance in the Bipedal Walker environment from the Open AI gym.
By the end of the course, you will know the answers to the following fundamental questions in Actor-Critic methods:
Why should we bother with actor critic methods when deep Q learning is so successful?
Can the advances in deep Q learning be used in other fields of reinforcement learning?
How can we solve the explore-exploit dilemma with a deterministic policy?
How do we get overestimation bias in actor-critic methods?
How do we deal with the inherent errors in deep neural networks?
This course is for the highly motivated and advanced student. To succeed, you must have prior course work in all the following topics:
College level calculus
Reinforcement learning
Deep learning
The pace of the course is brisk, but the payoff is that you will come out knowing how to read cutting edge research papers and turn them into functional code as quickly as possible.
Who this course is for:
Advanced students of artificial intelligence who want to implement state of the art academic research papers
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Reinforcement Learning with Pytorch
Thursday, August 27, 2020
Free Coupon Discount - Reinforcement Learning with Pytorch, Learn to apply Reinforcement Learning and Artificial Intelligence algorithms using Python, Pytorch and OpenAI Gym
Created by Atamai AI Team
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Description
UPDATE:
All the code and installation instructions have been updated and verified to work with Pytorch 1.6 !!
Artificial Intelligence is dynamically edging its way into our lives. It is already broadly available and we use it - sometimes even not knowing it - on daily basis. Soon it will be our permanent, every day companion.
And where can we place Reinforcement Learning in AI world? Definitely this is one of the most promising and fastest growing technologies that can eventually lead us to General Artificial Intelligence! We can see multiple examples where AI can achieve amazing results - from reaching super human level while playing games to solving real life problems (robotics, healthcare, etc).
Without a doubt it's worth to know and understand it!
And that's why this course has been created.
We will go through multiple topics, focusing on most important and practical details. We will start from very basic information, gradually building our understanding, and finally reaching the point where we will make our agent learn in human-like way - only from video input!
What's important - of course we need to cover some theory - but we will mainly focus on practical part. Goal is to understand WHY and HOW.
In order to evaluate our algorithms we will use environments from - very popular - OpenAI Gym. We will start from basic text games, through more complex ones, up to challenging Atari games
What will be covered during the course ?
- Introduction to Reinforcement Learning
- Markov Decision Process
- Deterministic and stochastic environments
- Bellman Equation
- Q Learning
- Exploration vs Exploitation
- Scaling up
- Neural Networks as function approximators
- Deep Reinforcement Learning
- DQN
- Improvements to DQN
- Learning from video input
- Reproducing some of most popular RL solutions
- Tuning parameters and general recommendations
See you in the class!
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