Brochure
Enquire
- The Course Overview
- Understanding Reinforcement Learning Algorithms
- Installing and Setting Up OpenAI Gym
- Running a Visualization of the Cart Robot CartPole-v0 in OpenAI Gym
Quick Facts
| particular | details | |||
|---|---|---|---|---|
|
Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
|
Course and certificate fees
Fees information
₹ 449 ₹3,499
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Getting Started with Reinforcement Learning Using OpenAI Gym
Lights, Camera, Action - Building Blocks of Reinforcement Learning
- Exploring the Possible Actions of Your CartPole Robot in OpenAI Gym
- Understanding the Environment of CartPole in OpenAI Gym
- Coding up Your First Solution to CartPole-v0
The Multi-Armed Bandit
- Creating a Bandit with 4 Arms Using Python and Numpy
- Creating an Agent to Solve the MAB Problem Using Python and Tensorflow
- Training the Agent, and Understanding What It Learned
The Contextual Bandit
- Creating an Environment with Multiple Bandits Using Python and Numpy
- Creating Your First Policy Gradient Based RL Agent with TensorFlow
- Training the Agent, and Understanding What It Learned
Dynamic Programming - Prediction, Control and Value Approximation
- Visualizing Dynamic Programming in GridWorld in Your Browser
- Understanding Prediction Through Building a Policy Evaluation Algorithm
- Understanding Control Through Building a Policy Iteration Algorithm
- Building a Value Iteration Algorithm
- Linking It All Together in the Web-Based GridWorld Visualization
Markov Decision Process and Neural Networks
- Understanding Markov Decision Process and Dynamic Programming in CartPole-v0
- Crafting a Neural Network Using TensorFlow
- Crafting a Neural Network to Predict the Value of Being in Different Environment
- Training the Agent in CartPole-v0
- Visualizing and Understanding How Your Software Agent Has Performed
Model-Free Prediction and Control With Monte Carlo (MC)
- Running the Blackjack Environment From the OpenAI Gym
- Tallying Every Outcome of an Agent Playing Blackjack Using MC
- Visualizing the Outcomes of a Simple Blackjack Strategy
- Control – Building a Very Simple Epsilon-Greedy Policy
- Visualizing the Outcomes of the Epsilon-Greedy Policy
Model-Free Prediction and Control with Temporal Difference (TD)
- Visualizing TD and SARSA in GridWorld in Your Browser
- Running the GridWorld Environment from the OpenAI Gym
- Building a SARSA Algorithm to Find the Optimal Epsilon-Greedy Policy
- Visualizing the Outcomes of the SARSA
Articles
Popular Articles
prev
next
