Video description
Learn reinforcement learning from scratch.
About This Video
- Gain an understanding of all theoretical concepts related to reinforcement learning
- Master learning models such as model-free learning, Q-learning, temporal difference learning
- Model the uncertainty of the environment, environment stochastic policies, and environment value functions
In Detail
Although introduced academically decades ago, the recent developments in the field of reinforcement learning have been phenomenal. Domains such as self-driving cars, natural language processing, healthcare industry, online recommender systems, and so on have already seen how RL-based AI agents can bring tremendous gains.
This course will help you get started with reinforcement learning first by establishing the motivation for this field and then covering all the essential topics, such as Markov Decision Processes, policy and rewards, model-free learning, temporal difference learning, and so on.
Each topic is accompanied by exercises and complementing analysis to help you gain practical and tangible coding skills.
By the end of this course, not only will you have gained the necessary understanding to implement RL in your projects but also implemented an actual Frozenlake project using the OpenAI Gym toolkit.
Publisher resources
Table of contents
- Chapter 1 : Introduction to Course and Instructor
- Chapter 2 : Motivation Reinforcement Learning
- Chapter 3 : Terminology of Reinforcement Learning
- Chapter 4 : GridWorld Example
-
Chapter 5 : Markov Decision Process Prerequisites
- Probability
- Probability 2
- Probability 3
- Conditional Probability
- Conditional Probability Fun Example
- Joint Probability
- Joint probability 2
- Joint probability 3
- Expected Value
- Conditional Expectation
- Modeling Uncertainty of Environment
- Modeling Uncertainty of Environment 2
- Modeling Uncertainty of Environment 3
- Modeling Uncertainty of Environment Stochastic Policy
- Modeling Uncertainty of Environment Stochastic Policy 2
- Modeling Uncertainty of Environment Value Functions
- Running Averages
- Running Averages 2
- Running Averages as Temporal Difference
- Activity
- Chapter 6 : Elements of Markov Decision Process
- Chapter 7 : More on Reward
-
Chapter 8 : Solving Markov DP
- MDP Recap
- Value Functions
- Optimal Value Function
- Optimal Policy
- Bellman Equation
- Value Iteration
- Value Iteration Quiz
- Value Iteration Quiz Gamma Missing
- Value Iteration Solution
- Problems of Value Iteration
- Policy Evaluation
- Policy Evaluation 2
- Policy Evaluation 3
- Policy Evaluation d Form Solution
- Policy Iteration
- State Action Values
- V and Q Comparisons
- Chapter 9 : Value Approximation
- Chapter 10 : Temporal Differencing - Q Learning
- Chapter 11 : TD Lambda
- Chapter 12 : Project Frozenlake (Open AI Gym)
Product information
- Title: Reinforcement Learning with Python Explained for Beginners
- Author(s):
- Release date: February 2021
- Publisher(s): Packt Publishing
- ISBN: 9781801072274
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