Reinforcement Learning with Python Explained for Beginners

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AI Sciences

9:06:29

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00001 Introduction to Course and Instructor.mp4

04:17

00002 What is Reinforcement Learning.mp4

08:46

00003 What is Reinforcement Learning Hiders and Seekers by OpenAI.mp4

06:09

00004 RL Versus Other ML Frameworks.mp4

07:43

00005 Why Reinforcement Learning.mp4

03:48

00006 Examples of Reinforcement Learning.mp4

05:02

00007 Limitations of Reinforcement Learning.mp4

08:10

00008 Exercises.mp4

02:16

00009 What is Environment.mp4

03:30

00010 What is Environment 2.mp4

05:55

00011 What is Agent.mp4

05:35

00012 What is State.mp4

05:59

00013 State Belongs to Environment and not to Agent.mp4

05:02

00014 What is Action.mp4

05:41

00015 What is Reward.mp4

09:44

00016 Goal.mp4

04:04

00017 Policy.mp4

04:16

00018 Summary.mp4

08:35

00019 Setup 1.mp4

03:14

00020 Setup 2.mp4

05:03

00021 Setup 3.mp4

07:09

00022 Policy Comparison.mp4

08:11

00023 Deterministic Environment.mp4

07:25

00024 Stochastic Environment.mp4

08:04

00025 Stochastic Environment 2.mp4

04:59

00026 Stochastic Environment 3.mp4

09:55

00027 Non-Stationary Environment.mp4

08:36

00028 GridWorld Summary.mp4

05:52

00029 Activity.mp4

01:16

00030 Probability.mp4

03:32

00031 Probability 2.mp4

04:58

00032 Probability 3.mp4

04:07

00033 Conditional Probability.mp4

05:14

00034 Conditional Probability Fun Example.mp4

06:04

00035 Joint Probability.mp4

03:28

00036 Joint probability 2.mp4

03:45

00037 Joint probability 3.mp4

02:52

00038 Expected Value.mp4

06:08

00039 Conditional Expectation.mp4

02:33

00040 Modeling Uncertainty of Environment.mp4

04:58

00041 Modeling Uncertainty of Environment 2.mp4

04:02

00042 Modeling Uncertainty of Environment 3.mp4

03:07

00043 Modeling Uncertainty of Environment Stochastic Policy.mp4

03:19

00044 Modeling Uncertainty of Environment Stochastic Policy 2.mp4

02:56

00045 Modeling Uncertainty of Environment Value Functions.mp4

07:59

00046 Running Averages.mp4

01:30

00047 Running Averages 2.mp4

04:52

00048 Running Averages as Temporal Difference.mp4

04:23

00049 Activity.mp4

01:37

00050 Markov Property.mp4

04:03

00051 State Space.mp4

04:20

00052 Action Space.mp4

03:31

00053 Transition Probabilities.mp4

03:52

00054 Reward Function.mp4

04:20

00055 Discount Factor.mp4

03:52

00056 Summary.mp4

04:07

00057 Activity.mp4

01:09

00058 MOR Quiz 1.mp4

03:18

00059 MOR Quiz Solution 1.mp4

06:29

00060 MOR Quiz 2.mp4

03:09

00061 MOR Quiz Solution 2.mp4

04:35

00062 MOR Reward Scaling.mp4

04:37

00063 MOR Infinite Horizons.mp4

06:17

00064 MOR Quiz 3.mp4

03:01

00065 MOR Quiz Solution 3.mp4

04:55

00066 MDP Recap.mp4

02:15

00067 Value Functions.mp4

05:18

00068 Optimal Value Function.mp4

04:56

00069 Optimal Policy.mp4

05:21

00070 Bellman Equation.mp4

05:29

00071 Value Iteration.mp4

03:53

00072 Value Iteration Quiz.mp4

02:52

00073 Value Iteration Quiz Gamma Missing.mp4

00:56

00074 Value Iteration Solution.mp4

10:08

00075 Problems of Value Iteration.mp4

05:50

00076 Policy Evaluation.mp4

06:59

00077 Policy Evaluation 2.mp4

05:00

00078 Policy Evaluation 3.mp4

05:35

00079 Policy Evaluation d Form Solution.mp4

04:03

00080 Policy Iteration.mp4

07:30

00081 State Action Values.mp4

06:58

00082 V and Q Comparisons.mp4

04:51

00083 What Does it Mean that MDP is Unknown.mp4

02:45

00084 Why Transition Probabilities are Important.mp4

03:48

00085 Model-Based Solutions.mp4

04:32

00086 Model-Free Solutions.mp4

03:09

00087 Monte-Carlo Learning.mp4

04:23

00088 Monte-Carlo Learning Example.mp4

09:55

00089 Monte-Carlo Learning Limitations.mp4

02:59

00090 Running Average.mp4

05:09

00091 Learning Rate.mp4

07:05

00092 Learning Equation.mp4

03:52

00093 TD Algorithm.mp4

05:11

00094 Exploration Versus Exploitation.mp4

02:40

00095 Epsilon Greedy Policy.mp4

03:11

00096 SARSA.mp4

02:48

00097 Q-Learning.mp4

06:35

00098 Q-Learning Implementation for MAPROVER Clipped.mp4

22:55

00099 N-Step Look a Head.mp4

04:11

00100 Formulation.mp4

04:03

00101 Values.mp4

03:05

00102 TD Q-Learning TD Lambda.mp4

06:19

00103 TD Q-Learning TD Lambda TD Lambda MAPRover Activity.mp4

03:54

00104 Frozenlake 1.mp4

02:02

00105 Frozenlake Implementation.mp4

22:49

Description

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.

All resources and code files are placed here: https://github.com/PacktPublishing/Reinforcement-Learning-with-Python-Explained-for-Beginners

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AI Sciences

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AI Sciences is a group of experts, PhDs, and practitioners of AI, ML, computer science, and statistics. Some of the experts work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.They have produced a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of machine learning, statistics, artificial intelligence, and data science.Initially, their objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory. Today, they also publish more complete courses for a wider audience. Their courses have had phenomenal success and have helped more than 100,000 students master AI and data science.

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