Reinforcement Learning Made Simple: Intro to Basic Concepts and Terminology

You’ve probably started hearing a lot more about Reinforcement Learning in the last few years, ever since the AlphaGo model, which was trained using reinforcement-learning, stunned the world by beating the then reigning world champion at the complex game of Go.Over a series of articles, I’ll go over the basics of Reinforcement Learning (RL) and some of the most popular algorithms and deep learning architectures used to solve RL problems. We’ll try to focus on understanding these principles in as intuitive a way as possible without going too much into mathematical theory. In this first article, I’ll introduce many of the fundamental concepts and terminology of RL, so that we can build solutions using them in the following articles.

Overview of RL

Where does RL fit in the world of Machine Learning?

Typically when people provide an overview of ML, the first thing they explain is that it can be divided into two categories, Supervised Learning and Unsupervised Learning. However, there is a third category, viz. RL although it isn’t mentioned as often as its two more glamorous siblings.

Supervised Learning uses labeled data as input, and predicts outcomes. It receives feedback from a Loss function acting as a ‘supervisor’.Unsupervised Learning uses unlabeled data as input and detects hidden patterns in the data such as clusters or anomalies. It receives no feedback from a supervisor.Reinforcement Learning gathers inputs and receives feedback by interacting with the external world. It outputs the best actions that it needs to take while interacting with that world.

How is RL different from Supervised (or Unsupervised) Learning?

• There is no supervisor to guide the trainingYou don’t train with a large (labeled or unlabeled) pre-collected dataset. Rather, your ‘data’ is provided to you dynamically via feedback from the real-world environment with which you are interacting.You iteratively make decisions over a sequence of time-steps eg. In a Classification problem, you run inference once on data input to produce an output prediction. With Reinforcement Learning, you run inference repeatedly, navigating through the real-world environment as you go.

What problems are RL used to solve?

Rather than the typical ML problems such as Classification, Regression, Clustering and so on, RL is most commonly used to solve a different class of real-world problems, such as a Control task or Decision task, where you operate a system that interacts with the real world.

• eg. A robot or drone that has to learn the task of picking a device from one box and putting it in a container

It is useful for a variety of applications like:

• Operating a drone or autonomous vehicleManipulating a robot to navigate the environment and perform various tasksManaging an investment portfolio and taking trading decisionsPlaying games such as Go, Chess, video games

Reinforcement Learning happens through trial and error

With RL the learning happens from experience by trial and error, similar to a human eg. A baby can touch fire or milk and then learns from negative or positive reinforcement.

• The baby takes some actionReceives feedback from the environment about the result of that actionRepeats this process till it learns which actions produce favorable results and which actions produce unfavorable results.

#reinforcement-learning #machine-learning #ai #data-science #algorithms

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Reinforcement Learning Made Simple: Intro to Basic Concepts and Terminology

You’ve probably started hearing a lot more about Reinforcement Learning in the last few years, ever since the AlphaGo model, which was trained using reinforcement-learning, stunned the world by beating the then reigning world champion at the complex game of Go.Over a series of articles, I’ll go over the basics of Reinforcement Learning (RL) and some of the most popular algorithms and deep learning architectures used to solve RL problems. We’ll try to focus on understanding these principles in as intuitive a way as possible without going too much into mathematical theory. In this first article, I’ll introduce many of the fundamental concepts and terminology of RL, so that we can build solutions using them in the following articles.

Overview of RL

Where does RL fit in the world of Machine Learning?

Typically when people provide an overview of ML, the first thing they explain is that it can be divided into two categories, Supervised Learning and Unsupervised Learning. However, there is a third category, viz. RL although it isn’t mentioned as often as its two more glamorous siblings.

Supervised Learning uses labeled data as input, and predicts outcomes. It receives feedback from a Loss function acting as a ‘supervisor’.Unsupervised Learning uses unlabeled data as input and detects hidden patterns in the data such as clusters or anomalies. It receives no feedback from a supervisor.Reinforcement Learning gathers inputs and receives feedback by interacting with the external world. It outputs the best actions that it needs to take while interacting with that world.

How is RL different from Supervised (or Unsupervised) Learning?

• There is no supervisor to guide the trainingYou don’t train with a large (labeled or unlabeled) pre-collected dataset. Rather, your ‘data’ is provided to you dynamically via feedback from the real-world environment with which you are interacting.You iteratively make decisions over a sequence of time-steps eg. In a Classification problem, you run inference once on data input to produce an output prediction. With Reinforcement Learning, you run inference repeatedly, navigating through the real-world environment as you go.

What problems are RL used to solve?

Rather than the typical ML problems such as Classification, Regression, Clustering and so on, RL is most commonly used to solve a different class of real-world problems, such as a Control task or Decision task, where you operate a system that interacts with the real world.

• eg. A robot or drone that has to learn the task of picking a device from one box and putting it in a container

It is useful for a variety of applications like:

• Operating a drone or autonomous vehicleManipulating a robot to navigate the environment and perform various tasksManaging an investment portfolio and taking trading decisionsPlaying games such as Go, Chess, video games

Reinforcement Learning happens through trial and error

With RL the learning happens from experience by trial and error, similar to a human eg. A baby can touch fire or milk and then learns from negative or positive reinforcement.

• The baby takes some actionReceives feedback from the environment about the result of that actionRepeats this process till it learns which actions produce favorable results and which actions produce unfavorable results.

#reinforcement-learning #machine-learning #ai #data-science #algorithms

Attend The Full Day Hands-On Workshop On Reinforcement Learning

The Association of Data Scientists (AdaSci), a global professional body of data science and ML practitioners, is holding a full-day workshop on building games using reinforcement learning on Saturday, February 20.

Artificial intelligence systems are outperforming humans at many tasks, starting from driving cars, recognising images and objects, generating voices to imitating art, predicting weather, playing chess etc. AlphaGo, DOTA2, StarCraft II etc are a study in reinforcement learning.

Reinforcement learning enables the agent to learn and perform a task under uncertainty in a complex environment. The machine learning paradigm is currently applied to various fields like robotics, pattern recognition, personalised medical treatment, drug discovery, speech recognition, and more.

With an increase in the exciting applications of reinforcement learning across the industries, the demand for RL experts has soared. Taking the cue, the Association of Data Scientists, in collaboration with Analytics India Magazine, is bringing an extensive workshop on reinforcement learning aimed at developers and machine learning practitioners.

#ai workshops #deep reinforcement learning workshop #future of deep reinforcement learning #reinforcement learning #workshop on a saturday #workshop on deep reinforcement learning

Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

• Temporal difference learning (TD learning)
• Parameters
• QL & SARSA
• Comparison
• Implementation
• Conclusion

We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning

Deep Reinforcement Learning for Video Games Made Easy

In this post, we will investigate how easily we can train a Deep Q-Network (DQN) agent (Mnih et al., 2015) for Atari 2600 games using the Google reinforcement learning library Dopamine. While many RL libraries exist, this library is specifically designed with four essential features in mind:

• Easy experimentation
• Flexible development
• Compact and reliable
• Reproducible

_We believe these principles makes __Dopamine _one of the best RL learning environment available today. Additionally, we even got the library to work on Windows, which we think is quite a feat!

In my view, the visualization of any trained RL agent is an absolute must in reinforcement learning! Therefore, we will (of course) include this for our own trained agent at the very end!

We will go through all the pieces of code required (which is** minimal compared to other libraries**), but you can also find all scripts needed in the following Github repo.

1. Brief Introduction to Reinforcement Learning and Deep Q-Learning

The general premise of deep reinforcement learning is to

“derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations.”

• Mnih et al. (2015)

As stated earlier, we will implement the DQN model by Deepmind, which only uses raw pixels and game score as input. The raw pixels are processed using convolutional neural networks similar to image classification. The primary difference lies in the objective function, which for the DQN agent is called the optimal action-value function

where_ rₜ is the maximum sum of rewards at time t discounted by γ, obtained using a behavior policy π = P(a_∣_s)_ for each observation-action pair.

There are relatively many details to Deep Q-Learning, such as Experience Replay (Lin, 1993) and an _iterative update rule. _Thus, we refer the reader to the original paper for an excellent walk-through of the mathematical details.

One key benefit of DQN compared to previous approaches at the time (2015) was the ability to outperform existing methods for Atari 2600 games using the same set of hyperparameters and only pixel values and game score as input, clearly a tremendous achievement.

2. Installation

This post does not include instructions for installing Tensorflow, but we do want to stress that you can use both the CPU and GPU versions.

Nevertheless, assuming you are using Python 3.7.x, these are the libraries you need to install (which can all be installed via pip):

tensorflow-gpu=1.15   (or tensorflow==1.15  for CPU version)
cmake
dopamine-rl
atari-py
matplotlib
pygame
seaborn
pandas

#reinforcement-learning #q-learning #games #machine-learning #deep-learning #deep learning

Learn Python Tutorial from Basic to Advance

Description
Become a Python Programmer and learn one of employer’s most requested skills of 21st century!

This is the most comprehensive, yet straight-forward, course for the Python programming language on Simpliv! Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you Python 3. (Note, we also provide older Python 2 notes in case you need them)

With over 40 lectures and more than 3 hours of video this comprehensive course leaves no stone unturned! This course includes tests, and homework assignments as well as 3 major projects to create a Python project portfolio!

This course will teach you Python in a practical manner, with every lecture comes a full coding screencast and a corresponding code notebook! Learn in whatever manner is best for you!

We will start by helping you get Python installed on your computer, regardless of your operating system, whether its Linux, MacOS, or Windows, we’ve got you covered!

We cover a wide variety of topics, including:

Command Line Basics
Installing Python
Running Python Code
Strings
Lists
Dictionaries
Tuples
Sets
Number Data Types
Print Formatting
Functions
Scope
Built-in Functions
Debugging and Error Handling
Modules
External Modules
Object Oriented Programming
Inheritance
Polymorphism
File I/O
Web scrapping
Database Connection
Email sending
and much more!
Project that we will complete:

Guess the number
Guess the word using speech recognition
Love Calculator
google search in python
Image download from a link
Click and save image using openCV
Ludo game dice simulator
open wikipedia on command prompt
Password generator
QR code reader and generator
You will get lifetime access to over 40 lectures.

So what are you waiting for? Learn Python in a way that will advance your career and increase your knowledge, all in a fun and practical way!

Basic knowledge
Basic programming concept in any language will help but not require to attend this tutorial
What will you learn
Learn to use Python professionally, learning both Python 2 and Python 3!
Create games with Python, like Tic Tac Toe and Blackjack!
Learn advanced Python features, like the collections module and how to work with timestamps!
Learn to use Object Oriented Programming with classes!
Understand complex topics, like decorators.
Understand how to use both the pycharm and create .py files
Get an understanding of how to create GUIs in the pycharm!
Build a complete understanding of Python from the ground up!

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