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- Introduction
- Course Structure
- Environment Setup
Deep Reinforcement Learning: Hands-on AI Tutorial in Python
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 499 ₹2,299
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Introduction
Jump into Reinforcement Learning
- Introduction
- RL Applications
- RL vs. Supervised and Unsupervised Learning
- What is reinforcement learning?
RL Algorithms
- Markov Decision Process
- Optimal Policy
- Bellman Equation
- Q-Learning
- Step-by-step Example
- Sarsa
- Deep Q-Network
- Exploration vs. Exploitation
- Define RL Problem - Examples
- Reinforcement learning algorithms
- SARSA algorithm
Hands-on Project 1 - Maze Problem
- Overall Design
- Create Project
- Create files
- Create Maze Environment class
- Implement Building Maze Grid
- Test build_maze method
- Render and Reset methods
- Implement getting next state and reward
- Create Agent class
- Implement adding states
- Implement choosing action
- Implement learn method
- Create App
- Implement main method
- Implement plotting results
- Run the App
- Expand Maze Environment
Hands-on Project 2 - Stock Trading
- Overall Design
- Start project
- Prepare dataset
- Create Market Environment class
- Implement getting data
- Implement getting all states
- Implement getting next state and reward
- Create Agent class
- Implement creating deep learning model and reset method
- Implement getting action
- Implement buy and sell
- Implement experience replay
- Create training app
- Test training app
- Create evaluation app
- Implement plotting results
- Run training and evaluation
- Extending Stock Trading with Multiple Features
- Multiple Feature Stock Trader
Summary
- Summary
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