Brochure
Enquire
- 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
Quick Facts
| particular | details | |||
|---|---|---|---|---|
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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 ₹799
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Introduction (New Content)
(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
Thank you
- Thank you
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