Reinforcement Learning

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Master Reinforcement Learning by enrolling in EDTIA'S Reinforcement Learning Certification Training and upskill your knowledge and technological skill in Machine Learning.

Course Description

Learn Reinforcement Learning, an area of Machine Learning, through this course. You will learn the Markov Decision Processes, Bandit Algorithms, Dynamic Programming, Temporal Difference (TD) methods, the Value function, Bellman Equation, and Value iteration.

Reinforcement learning is a machine learning training method that rewards desired behaviors and punishes undesired ones. A reinforcement learning agent can sense and analyze its surroundings, take actions and learn through trial and error.

Web Developers Software Developers Programmers Anyone who wants to learn reinforcement learning

Reinforcement learning (RL) is an area of machine learning involved with how intelligent agents take actions in an environment to maximize the concept of cumulative reward. Reinforcement learning is one of three basic machine learning parts.

Fundamentals in AI & ML Probability Python Neural Networks Frameworks Deep Learning libraries like PyTorch/ Theano/ Tensorflow.

There are four types of Reinforcement: Positive Reinforcement, Negative Reinforcement, extinction, and punishment.

Reinforcement learning Maximizes Performance. Sustain Change for a long period. Much Reinforcement can lead to an excess of states which can diminish the results.

Reinforcement learning can potentially find incredible, innovative solutions to help organizations outpace their competition.

Some autonomous driving tasks where reinforcement learning is applied include trajectory optimization, motion planning, dynamic pathing, controller optimization, and highway scenario-based learning policies.

What you'll learn

  • In this course, you will learn: Markov Decision Processes, Bandit Algorithms, Dynamic Programming, Temporal Difference (TD) methods and more.


  • basic knowledge of AI & ML, Probability, Python, Neural Networks, Framework, Deep Learning libraries like PyTorch/ Theano/ Tensorflow.


Learn fundamentals of Reinforcement Learning and its elements, OpenAI Gym - a programming environment used for implementing RL agents.

Branches of Machine Learning
What is Reinforcement Learning?
The Reinforcement Learning Process
Elements of Reinforcement Learning
RL Agent Taxonomy
Reinforcement Learning Problem
Introduction to OpenAI Gym

understand Bandit Algorithms and Markov Decision Process.

Bandit Algorithms
Markov Process
Markov Reward Process

Know Dynamic Programming Algorithms and Temporal Difference Learning methods.

Introduction to Dynamic Programming
Dynamic Programming Algorithms
Monte Carlo Methods
Temporal Difference Learning Methods

know Policy Gradients and Deep Q Learning

Policy Gradients
Policy Gradients using TensorFlow
Deep Q learning
Q learning with replay buffers, target networks, and CNN

Have hands-on experience in Reinforcement Learning.


Edtia Support Unit is available 24/7 to help with your queries during and after completing Reinforcement Learning.

Dynamic programming and Q-Learning are Reinforcement Learning algorithms.

Reinforcement learning describes learning problems where an agent must take actions in an environment to maximize some defined reward function. Unlike supervised deep learning, enormous data with the correct input-output pairs are not explicitly explained.

The average salary for a machine learning engineer is $131,822 per year.

To better understand reinforcement learning, one must learn as per the curriculum.

Reinforcement learning is an area of Machine Learning, and it is about taking suitable action to maximize reward in a particular situation. Maximizes Performance. Sustain Change for a long period. High Reinforcement can lead to an excess of states which can reduce the results.

Reinforcement Learning is used for various planning problems, including travel plans, budget planning, and business strategy. The two advantages of using RL are that it considers the probability of outcomes and allows us to control parts of the environment.

The purpose of reinforcement learning is for the agent to learn an optimal, or nearly-optimal, a policy that maximizes the "reward function" or another user-provided reinforcement signal that accumulates from the immediate rewards.

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Training Course Features


Every certification training session is followed by a quiz to assess your course learning.

Mock Tests
Mock Tests

The Mock Tests Are Arranged To Help You Prepare For The Certification Examination.

Lifetime Access
Lifetime Access

A lifetime access to LMS is provided where presentations, quizzes, installation guides & class recordings are available.

24x7 Expert Support
24x7 Expert Support

A 24x7 online support team is available to resolve all your technical queries, through a ticket-based tracking system.


For our learners, we have a community forum that further facilitates learning through peer interaction and knowledge sharing.


Successfully complete your final course project and Edtia will provide you with a completion certification.

Reinforcement Learning

You will receive Edtia Reinforcement Learning Training certification on completing live online instructor-led classes. After completing the Reinforcement Learning course module, you will receive the certificate.

A Reinforcement Learning certificate is a certification that verifies that the holder has the knowledge and skills required to work with machine learning technology.

By enrolling in the Reinforcement Learning Training Certification course and completing the module, you can get Edtia Reinforcement Learning.

Yes, Access to the course material will be available for a lifetime once you have enrolled in the Edita Reinforcement Learning Training Certification Course.

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