Reinforcement Learning

Udemy

Intermediate

Online

₹ 499 799

particular

details

                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study

                                    Mode of Delivery
                                    Video and Text Based

Introduction (New Content)

Course Structure
How to Make the Most Out of This Course
What is Reinforcement Learning and Why we need Reinforcement Learning?
Reward
Introduction to the agent, environment, action and observation
What is Tensorflow and how to install it
Setting up the working environment
What is OpenAI Gym
Anaconda Installation

(New Content) Robot Control System Using Deep Reinforcement Learning

Introduction to Robot Control and three laws of Robotics
Short robotics timeline and Automatic control
Reinforcement learning basics and Agent-environment interface
Reinforcement Learning Algorithm
Keras DQN
Cart Pole Implementation Part 1
Cart Pole Implementation Part 2
Cart Pole Implementation Part 3
Summary of the project

(New Content) Playing Atari Games

Developing a policy gradient algorithm Part 1
Developing a policy gradient algorithm Part 2
Developing a policy gradient algorithm Part 3
Developing a policy gradient algorithm Part 4
Developing hill climbing part 1
Developing hill climbing part 2
Developing hill climbing part 3
Developing hill climbing part 4
Simulating Atari environments part 1
Simulating Atari environments part 2

(New Content) Markov Decision Process and Dynamic Programming

Introduction
Theory about Markov Chain and steps to create Markov Chain
Creating Markov Chain Part 1
Creating Markov Chain Part 2
How does Markov chain work?
MDP Introduction and steps to create MDP
MDP Implementation part 1
MDP Implementation part 2
How does MDP work?
Introduction to policy evaluation and steps to create policy evaluation
Policy evaluation Implementation Part 1
Policy evaluation Implementation Part 2
How does Policy evaluation work?
Policy evaluation Implementation Part 3
Introduction to Simulating the FrozenLake environment
Simulating the FrozenLake environment Part 1
Simulating the FrozenLake environment Part 2
Simulating the FrozenLake environment Part 3 (How does it work?)
Simulating the FrozenLake environment Part 4
Introduction to MDP with a value iteration algorithm and steps to implement it
Solving an MDP with a value iteration algorithm Part 1
Solving an MDP with a value iteration algorithm Part 2
Solving an MDP with a value iteration algorithm Part 3 (How does it work?)
Solving an MDP with a value iteration algorithm Part 4
Solving an MDP with a value iteration algorithm Part 5
Introduction to Solving an MDP with a policy iteration algorithm
Solving an MDP with a policy iteration algorithm Part 1
Solving an MDP with a policy iteration algorithm Part 2
Solving an MDP with a policy iteration algorithm Part 3
Solving an MDP with a policy iteration algorithm (How does it work?)
Solving an MDP with a policy iteration algorithm Part 5
coin-flipping gamble problem Introduction
coin-flipping gamble problem Part 1
coin-flipping gamble problem Part 2
coin-flipping gamble problem Part 3
coin-flipping gamble problem Part 4
coin-flipping gamble problem Part 5
coin-flipping gamble problem (How does it work?)
coin-flipping gamble problem (How does it work?) continued
coin-flipping gamble problem Part 6

(New Content) Monte Carlo Methods for Making Numerical Estimations

Introduction
Introduction to Calculating Pi using the Monte Carlo method
Calculating Pi using the Monte Carlo method Implementation Part 1
Calculating Pi using the Monte Carlo method Implementation Part 2
Calculating Pi using the Monte Carlo method explanation
Monte Carlo policy evaluation Introduction
Monte Carlo policy evaluation Implementation Part 1
Monte Carlo policy evaluation Implementation Part 2
Monte Carlo policy evaluation explanation
Introduction to Playing Blackjack with Monte Carlo prediction
Playing Blackjack with Monte Carlo prediction Part 1
Playing Blackjack with Monte Carlo prediction Part 2
Playing Blackjack with Monte Carlo prediction Part 3
Playing Blackjack with Monte Carlo prediction Part 4
Playing Blackjack with Monte Carlo prediction explanation
On-policy Monte Carlo Control Introduction
On-policy Monte Carlo Control Implementation Part 1
On-policy Monte Carlo Control Implementation Part 2
On-policy Monte Carlo Control Implementation Part 3
On-policy Monte Carlo Control Implementation Explanation
MC control with epsilon-greedy policy Introduction
MC control with epsilon-greedy policy Implementation Part 1
MC control with epsilon-greedy policy Implementation Part 2
MC control with epsilon-greedy policy explanation
Off-policy Monte Carlo control Introduction
Off-policy Monte Carlo control Implementation part 1
Off-policy Monte Carlo control Implementation part 2
Off-policy Monte Carlo control explanation
MC control with weighted importance sampling Introduction
MC control with weighted importance sampling Implementation Part 1
MC control with weighted importance sampling explanation
MC control with weighted importance sampling Part 2

(New Content) Temporal Difference and Q-Learning

Introduction
Introduction to Cliff Walking environment playground
Cliff Walking environment playground Implementation
Cliff Walking environment playground explanation
Introduction to Q-learning algorithm
Q-learning algorithm Implementation Part 1
Q-learning algorithm Implementation Part 2
Q-learning algorithm explanation
Windy Gridworld environment playground Introduction
Windy Gridworld environment playground Implementation Part 1
Windy Gridworld environment playground Implementation Part 2
Windy Gridworld environment playground explanation
SARSA algorithm Introduction
SARSA algorithm Implementation part 1
SARSA algorithm Implementation part 2
SARSA algorithm explanation
Taxi problem with Q-learning Introduction
Taxi problem with Q-learning Implementation Part 1
Taxi problem with Q-learning Explanation
Introduction to Taxi problem with SARSA
Taxi problem with SARSA Implementation Part 1
Taxi problem with SARSA Implementation Part 2
Taxi problem with SARSA Explanation
Double Q-learning algorithm Introduction
Double Q-learning algorithm Implementation Part 1
Double Q-learning algorithm explanation

(New Content) Case Study - The MAB Problem

Introduction
The MAB Problem
Creating a bandit in the Gym Part 1
Creating a bandit in the Gym Part 2
Creating a bandit in the Gym Part 3
Creating a bandit in the Gym Part 4
Creating a bandit in the Gym Part 5
Creating a bandit in the Gym Part 6
Applications of MAB
Finding the best advertisement banner using bandits
Summary
Introduction to Solving internet advertising problems with contextual bandits
Solving internet advertising problems with contextual bandits Part 1 (Bonus)
Solving internet advertising problems with contextual bandits Part 2 (Bonus)
Solving internet advertising problems with contextual bandits explanation

(New Content) Deep Q-Networks in Action

Introduction
Deep Q-networks Introduction
deep Q-networks Implementation Part 1
deep Q-networks Implementation Part 2
deep Q-networks Implementation Part 3
Double deep Q-Networks Introduction
Double deep Q-Networks Implementation Part 1
Double deep Q-Networks Implementation Part 2
Dueling deep Q-Networks Introduction
Dueling deep Q-Networks Implementation Part 1
Dueling deep Q-Networks explanation

Policy Gradients and Policy Optimizations

Introduction
REINFORCE Algorithm Introduction
REINFORCE Algorithm Implementation Part 1
REINFORCE Algorithm Implementation Part 2
REINFORCE algorithm with baseline Introduction
REINFORCE algorithm with baseline Implementation Part 1
REINFORCE algorithm with baseline Implementation Part 2
Actor-critic algorithm Implementation part 1
Actor-critic algorithm Implementation part 2
Cliff Walking with the actor-critic algorithm Introduction
Cliff Walking with the actor-critic algorithm Implementation Part 1
Cliff Walking with the actor-critic algorithm Implementation Part 2
Setting up Mountain Car environment
Solving Mountain Car environment Introduction
Solving Mountain Car environment Part 1
Solving Mountain Car environment Part 2

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Reinforcement Learning

Intermediate

Online

₹ 499 799

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The syllabus

Introduction (New Content)

(New Content) Robot Control System Using Deep Reinforcement Learning

(New Content) Playing Atari Games

(New Content) Markov Decision Process and Dynamic Programming

(New Content) Monte Carlo Methods for Making Numerical Estimations

(New Content) Temporal Difference and Q-Learning

(New Content) Case Study - The MAB Problem

(New Content) Deep Q-Networks in Action

Policy Gradients and Policy Optimizations

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