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Courses chevron_right Deep Learning chevron_right Premium

Deep Learning Course

Reinforcement Learning - Developing Intelligent Agents

Introducing Theory Mathematically Focused Coding Focused Project Based

Pytorch OpenAI Jupyter Discord

Level: Advanced

Instructor: Mandy

$79.99 $99.99 20% Off - Limited Offer

Course thumbnail for Reinforcement Learning - Developing Intelligent Agents

What's Included:

20 Video Lectures

596 Lines of Code

Watch Duration: 3 Hours

104 Inline Math Snippets

65 Pages of Lecture Notes

98 Inline Code Snippets

Reading Time: 6 Hours

47 Custom Visual Graphics

53 Quiz Questions

Updated and Maintained

Reinforcement Learning.

In this course, we cover the intuition, math, and code behind Reinforcement Learning.

What you'll learn ...

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Understand Markov Decision Processes (MDPs)

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Learn how to structure a reinforcement learning problem

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Learn the relationship between an agent and its environment

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Gain an understanding of fundamental concepts like policies, value functions, and expected returns

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Learn how to implement the Q-learning algorithm with value iteration

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Understand the relationship between environment exploration and exploitation

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Learn how to use the Gymnasium API for implementing RL tasks in code

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Learn how to build and train an RL agent in code

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Understand how Deep Q-learning makes use of neural networks

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Learn the algorithm for training a Deep Q-network

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Gain an understanding for the training concepts of Replay Memory and Fixed Q-targets

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Learn how to build and train a Deep Q-network in code to solve an environment

Reinforcement Learning Syllabus.

Learn the fundamental concepts and how to use existing technology tools to develop reinforcement learning models.

Reinforcement Learning - Developing Intelligent Agents

$79.99 $99.99 20% Off - Limited Offer

Enrolled

What's Included:

Reinforcement Learning.

What you'll learn ...

Reinforcement Learning Syllabus.

Part 1 - Introduction to Reinforcement Learning

Section 1 - Markov Decision Processes (MDPs)

Lesson #1

Reinforcement Learning Series Intro - Syllabus Overview

Lesson #2

Markov Decision Processes (MDPs) - Structuring a Reinforcement Learning Problem

Lesson #3

Expected Return - What Drives a Reinforcement Learning Agent in an MDP

Lesson #4

Policies and Value Functions - Good Actions for a Reinforcement Learning Agent

Lesson #5

What do Reinforcement Learning Algorithms Learn - Optimal Policies

Section 2 - Q-Learning

Lesson #6

Q-Learning Explained - A Reinforcement Learning Technique

Lesson #7

Exploration vs. Exploitation - Learning the Optimal Reinforcement Learning Policy

Section 3 - Code project - Implement Q-learning with Python

Lesson #8

OpenAI Gym and Python for Q-learning - Reinforcement Learning Code Project

Lesson #9

Train Q-learning Agent with Python - Reinforcement Learning Code Project

Lesson #10

Watch Q-learning Agent Play Game with Python - Reinforcement Learning Code Project

Part 2 - Deep Reinforcement Learning

Section 1 - Deep Q-networks (DQNs)

Lesson #11

Deep Q-Learning - Combining Neural Networks and Reinforcement Learning

Lesson #12

Replay Memory Explained - Experience for Deep Q-Network Training

Lesson #13

Training a Deep Q-Network - Reinforcement Learning

Lesson #14

Training a Deep Q-Network with Fixed Q-targets - Reinforcement Learning

Section 2 - Code project - Implement deep Q-network with PyTorch

Lesson #15

Deep Q-Network Code Project Intro - Reinforcement Learning

Lesson #16

Build Deep Q-Network - Reinforcement Learning Code Project

Lesson #17

Deep Q-Network Image Processing and Environment Management - Reinforcement Learning Code Project

Lesson #18

Deep Q-Network Training Code - Reinforcement Learning Code Project

Lesson #19

Solving Cart and Pole with Deep Q-Network - Reinforcement Learning Code Project

Lesson #20

Reinforcement Learning - Course Conclusion