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 action, 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:
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: