Practical Reinforcement Learning using Python - 8 AI Agents
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Join the most comprehensive Reinforcement Learning course on Udemy and learn how to build Amazing Reinforcement Learning Applications!
Do you want to learn how to build cutting edge trading algorithms that leverage todays technology? Or do you want to learn the tools and skills that are considered the state of the art of Artificial Intelligence? Or do you just want to learn Reinforcement Learning in a Highly practical way?
After completing this course you will be able to:
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Build any reinforcement learning algorithm in any environment
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Use Reinforcement Learning for your own scientific experiments
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Solve problems using Reinforcement Learning
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Leverage Cutting Edge Technologies for your own project
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Master OpenAI gym’s
Why should you choose this course?
This course guides you through a step-by-step process of building state of the art trading algorithms and ensures that you walk away with the practical skills to build any reinforcement learning algorithm idea you have and implement it efficiently.
Here’s what’s included in the course:
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Atari Reinforcement Learning Agent
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Build Q-Learning from scratch and implement it in Autonomous Taxi Environment
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Build Deep Q-Learning from scratch and implement it in Flappy Bird
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Build Deep Q-Learning from scratch and implement it in Mario
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Build a Stock Reinforcement Learning Algorithm
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Build a intelligent car that can complete various environments
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And much more!
This course is for you if …
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You’re interested in cutting edge technology and applying it in practical ways
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You’re passionate about Deep Learning/AI
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Want to learn about cutting-edge technologies!
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Want to learn reinforcement learning by doing cool projects!
Course prerequisites:
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Python!
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2Setting up for Course & Installing Python PackagesVideo lesson
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3Importing Packages & Setting up Gym EnvironmentVideo lesson
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4Building Neural NetworkVideo lesson
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5Building Reinforcement Learning AgentVideo lesson
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6Training AgentVideo lesson
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7Visualizing our AgentVideo lesson
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8Save & Load AgentVideo lesson
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9Setting up Project & Installing Python PackagesVideo lesson
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10Importing Packages & Setting up Gym EnvironmentVideo lesson
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11What is Q-Learning?Video lesson
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12Implementing Q-Learning from ScratchVideo lesson
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13Training Q-Learning AgentVideo lesson
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14Analyzing our Trained AgentVideo lesson
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15Visualizing our Agent & Testing in other EnvironmentsVideo lesson
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16Setting up Project & Installing Python PackagesVideo lesson
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17Importing PackagesVideo lesson
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18Building class for Flappy Bird AgentVideo lesson
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19What is Deep Q Learning ?Video lesson
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20Building Neural NetworkVideo lesson
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21Acting FunctionVideo lesson
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22Train FunctionVideo lesson
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23Learn FunctionVideo lesson
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24Visualize FunctionVideo lesson
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25Visualize Trained AgentVideo lesson
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26Setting up Project & Installing Python PackagesVideo lesson
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27Importing PackagesVideo lesson
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28Setting up Gym EnvironmentVideo lesson
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29Building Class for Mario AgentVideo lesson
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30Building Neural NetworkVideo lesson
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31Act FunctionVideo lesson
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32Update Epsilon FunctionVideo lesson
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33Train FunctionVideo lesson
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34Preprocess State for Neural Network FunctionVideo lesson
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35Learn FunctionVideo lesson
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36Creating a Better Learning Environment for AgentVideo lesson
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37Save & Load AgentVideo lesson
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38Visualizing our AgentVideo lesson
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39Setting up Project & Installing Python PackagesVideo lesson
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40Importing PackagesVideo lesson
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41Getting our S&P 500 DataVideo lesson
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42Preprocessing our DataVideo lesson
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43Creating our EnvironmentVideo lesson
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44Taking Random Actions in EnvironmentVideo lesson
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45Creating Model & Learning from EnvironmentVideo lesson
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46Visualizing our AgentVideo lesson
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47PART 2: Implementing 89 different Technical Indicators in DataVideo lesson
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48PART 2: Creating Environment with Technical IndicatorsVideo lesson
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49PART 2: Visualizing our Agent in TA EnvironmentVideo lesson
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