This course will introduce you to Reinforcement Learning, a subfield of Machine Learning. Markov Decision Processes, Bandit Algorithms, Dynamic Programming, and Temporal Difference (TD) approaches will be covered. The Value function, Bellman Equation, and Value iteration will be introduced to you. You will also be introduced to Policy Gradient techniques. You will get experience making judgements in an unpredictable setting.
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Self paced classes
Reinforcement learning is a type of machine learning that trains agents to make decisions and take actions to maximize rewards in real-world problems. Its benefits include versatility, adaptability, and the ability to find optimal solutions in complex environments, making it a powerful tool for improving performance and reducing costs in various industries.
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Learning Objectives: This module's goal is to teach you the principles of Reinforcement Learning and its components. This lecture will also introduce you to OpenAI Gym, a programming environment for developing 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 goal of this subject is to teach you about Bandit Algorithms and the Markov Decision Process.
Topics:
Bandit Algorithms
Markov Process
Markov Reward Process
Markov Decision Process
Learning Objectives: The goal of this session is to teach you about Dynamic Programming Algorithms and Temporal Difference Learning techniques.
Topics:
Introduction to Dynamic Programming
Dynamic Programming Algorithms
Monte Carlo Methods
Temporal Difference Learning Methods
Learning Objectives: The goal of this session is to teach you about Policy Gradients and Deep Q Learning.
Topics:
Policy Gradients
Policy Gradients using TensorFlow
Deep Q learning
Q learning with replay buffers, target networks, and CNN
Goal: This module's goal is to provide you hands-on experience with Reinforcement Learning.
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Following enrollment, you will have immediate access to the LMS and will have it for the rest of your life. You will get access to all past class recordings, PPTs, PDFs, and assignments.
Yes, once you join in the course, you will have lifetime access to the course material.
This course will introduce you to Reinforcement Learning, a subfield of Machine Learning. Markov Decision Processes, Bandit Algorithms, Dynamic Programming, and Temporal Difference (TD) approaches will be covered. The Value function, Bellman Equation, and Value iteration will be introduced to you. You will also be introduced to Policy Gradient techniques. You will get experience making judgements in an unpredictable setting.
Web Developers
Software Developers
Programmers
Anyone who wants to learn reinforcement learning
Required Prerequisites
Fundamentals in AI & ML, Probability, Python, Neural Networks, Frameworks, Deep Learning library like PyTorch/ Theano/ Tensorflow
CertHippo offers you complimentary self-paced courses
Statistics and Machine learning algorithms
Python Essentials
A PC with an Intel i3 CPU or above, at least 3GB RAM (4GB preferred), and a 32bit or 64bit operating system are required.
Project Statement: Train an RL Agent to win a Game
Description: Train an RL Agent in OpenAI Gym using a provided Environment to complete a preset goal. In this project, you will create a Neural Network and train the Agent using the Policy Gradient Algorithm.
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