Learning Objectives: The aim of this module is to introduce you to the fundamentals of Reinforcement Learning and its elements. This module also introduces you to OpenAI Gym – a programming environment used for implementing RL agents.

Topics:

• 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

Learning Objectives: The aim of this module is to learn Bandit Algorithms and Markov Decision Process.

Topics:

• Bandit Algorithms
• Markov Process
• Markov Reward Process
• Markov Decision Process

Learning Objectives: The aim of this module is to develop an understanding of Dynamic Programming Algorithms and Temporal Difference Learning methods.

Topics:

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

Learning Objectives: The aim of this module is to learn Policy Gradients and develop an understanding of Deep Q Learning

Topics:

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

In this course, you will be introduced to Reinforcement Learning, an area of Machine Learning. You will learn the Markov Decision Processes, Bandit Algorithms, Dynamic Programming, and Temporal Difference (TD) methods. You will be introduced to Value function, Bellman Equation, and Value iteration. You will also learn Policy Gradient methods. You will learn to make decisions in uncertain environment.

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

Required Pre-requisites:

• Fundamentals in AI & ML, Probability, Python, Neural Networks, Frameworks, Deep Learning library like PyTorch/ Theano/ Tensorflow

CertAdda offers you complimentary self-paced courses

• Statistics and Machine learning algorithms
• Python Essentials

The system requirement is a system with an Intel i3 processor or above, minimum 3GB RAM (4GB recommended) and an operating system either of 32bit or 64bit.

Cloud Lab has been provided to ensure you get real-time hands-on experience to practice your new skills on a pre-configured environment.

• Project Statement: Train an RL Agent to win a Game
  Description: Using a given Environment in OpenAI Gym, train an RL Agent to accomplish a predefined task. In this project, you will be creating a Neural Network, and applying Policy Gradient Algorithm to train the Agent.

You will never miss a lecture at CertAdda You can choose either of the two options: View the recorded session of the class available in your LMS or You can attend the missed session, in any other live batch.

Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course.

Post-enrolment, the LMS access will be instantly provided to you and will be available for lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments. Moreover, access to our 24×7 support team will be granted instantly as well. You can start learning right away.

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.