- Introduction: Reinforcement Learning
- Some RL success stories
-
Welcome to Reinforcement Learning
- An example: OpenAI Gym
- Using a different policy
- Your turn:
-
Markov Decision Processes
- Markov chains
- Markov Reward Process
- Markov Decision Processes
- Solving MDPs: Value and Policy Iteration.
-
Chapter 3: Monte Carlo Methods
- Monte Carlo Learning
- Off-policy MC control
-
Chapter 4: Temporal Difference Learning
- Code sample: SARSA
-
Chapter 5: Function approximation.
- Gradient descent
- Feature vectors
- Function backups
- Code sample: SARSA with linear function approximation
-
Chapter 6: Experience Replay.
- Improvements since the original DQN
- #
- Code sample: Q-Learning with experience replay (Linear Approximator)
- Code sample: Q-Learning with experience replay (Neural Network Approximator)
-
Chapter 7: Policy Gradients & Policy Optimisation
- Derivative Free Methods
- Code sample: NES for FrozenLake
- Code sample: Cross entropy method for CartPole
- About the author
Reinforcement Learning with Python
A hands-on introduction
This book is 100% completeLast updated on 2024-02-17
What do Atari games, schedule planning and self-piloted drones have in common? Find the answer in this book!
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About the Book
Reinforcement Learning is regarded by many as the next big thing in data science. Beyond the hype, there is an interesting, multidisciplinary and very rich research area, with many proven successful applications, and many more promising. In this book I will introduce the main tools, ideas and history of the field, with a mixture of theory and practice (using examples from OpenAI Gym). Constantly in progres!!
Author
About the Author
Pablo Maldonado
Hi, I’m Pablo! I teach data science at companies and universities around the world.
After a few years in consulting (pricing, financial crime analytics, IoT, ecommerce), I decided to focus on teaching others.
Most of my time is devoted to teaching and individual mentoring, but I still do consulting to keep my skills fresh.
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