Reinforcement Learning — Reward Oriented Intelligence

Aswe saw in the above image, we can see the robot is thinking. This is actually Reinforcement Learning, i.e. making computers to learn itself by making various decisions. Let’s look at the definition part:

Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Actually, you might find this definition difficulty to understand but don’t worry, even I don’t understand definitions properly. So, let me conclude the definition: Reinforcement Learning is a type of Machine Learning. This learning makes the computer itself to learn from it’s environment, gets reward on successfully completing a task and main aim is to maximize the reward after the end of all tasks. Trough various blogs, I have already completed all supervised and unsupervised Machine Learning algorithms with math intuition, and now it’s time to learn reinforcement learning.

Reinforcement Learning has** great scope** in future, it is said to be the hope of true artificial intelligence. Reinforcement Learning is growing rapidly, producing wide variety of learning algorithms for different applications. Hence it is important to be familiar with the techniques of reinforcement learning.

Terms in Reinforcement Learning

  1. Agent. The program you train to perform specific task is an agent.
  2. **Environment. **The surrounding (real or virtual) in which the agent performs actions.
  3. Action. A move made by the agent, which causes a status change in the environment.
  4. **Rewards. **The evaluation or score of an action performed by agent, which can be positive or negative.

We can understand this terminology by looking at a reinforced learned robot, it will surely be interesting.

Image for post

Source: here

This is basically a plastic cleaning robot, it’s main aim is to collect plastics garbage from the floor. The robot works this way:

  • Gets +10 points when it successfully pick a plastic.
  • Gets** -10** when it hits a person.
  • Gets -50 when it falls off.
  • Gets +50 when it successfully collect all the garbage in desired time.

Here our **Robot is Agent, room’s Floor is Environment, Pick garbage **is **Action and Points earned **is Rewards.

#artificial-intelligence #machine-intelligence #data-science #machine-learning #reinforcement-learning

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Reinforcement Learning — Reward Oriented Intelligence

Reinforcement Learning — Reward Oriented Intelligence

Aswe saw in the above image, we can see the robot is thinking. This is actually Reinforcement Learning, i.e. making computers to learn itself by making various decisions. Let’s look at the definition part:

Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Actually, you might find this definition difficulty to understand but don’t worry, even I don’t understand definitions properly. So, let me conclude the definition: Reinforcement Learning is a type of Machine Learning. This learning makes the computer itself to learn from it’s environment, gets reward on successfully completing a task and main aim is to maximize the reward after the end of all tasks. Trough various blogs, I have already completed all supervised and unsupervised Machine Learning algorithms with math intuition, and now it’s time to learn reinforcement learning.

Reinforcement Learning has** great scope** in future, it is said to be the hope of true artificial intelligence. Reinforcement Learning is growing rapidly, producing wide variety of learning algorithms for different applications. Hence it is important to be familiar with the techniques of reinforcement learning.

Terms in Reinforcement Learning

  1. Agent. The program you train to perform specific task is an agent.
  2. **Environment. **The surrounding (real or virtual) in which the agent performs actions.
  3. Action. A move made by the agent, which causes a status change in the environment.
  4. **Rewards. **The evaluation or score of an action performed by agent, which can be positive or negative.

We can understand this terminology by looking at a reinforced learned robot, it will surely be interesting.

Image for post

Source: here

This is basically a plastic cleaning robot, it’s main aim is to collect plastics garbage from the floor. The robot works this way:

  • Gets +10 points when it successfully pick a plastic.
  • Gets** -10** when it hits a person.
  • Gets -50 when it falls off.
  • Gets +50 when it successfully collect all the garbage in desired time.

Here our **Robot is Agent, room’s Floor is Environment, Pick garbage **is **Action and Points earned **is Rewards.

#artificial-intelligence #machine-intelligence #data-science #machine-learning #reinforcement-learning

Jackson  Crist

Jackson Crist

1617331066

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

Larry  Kessler

Larry Kessler

1617355640

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

Angela  Dickens

Angela Dickens

1598466660

Reinforcing the Science Behind Reinforcement Learning

You’re getting bore stuck in lockdown, you decided to play computer games to pass your time.

You launched Chess and chose to play against the computer, and you lost!

But how did that happen? How can you lose against a machine that came into existence like 50 years ago?

Image for post

This is the magic of** Reinforcement learning.**

**Reinforcement learning lies under the umbrella of Machine Learning. **They aim at developing intelligent behavior in a complex dynamic environment. Nowadays since the range of AI is expanding enormously, we can easily locate their importance around us. From _Autonomous Driving, Recommender Search Engines, Computer games to Robot skills, _AI is playing a vital role.

Pavlov’s Conditioning

When we think about AI, we have a perception of thinking about the future, but our idea takes us back in the late 19th century, Ivan Pavlov, a Russian physiologist was studying the salivation effect in dogs. He was interested in knowing how much dogs salivate when they see food, but, while conducting the experiment, he noticed that dogs were even salivating before seeing any food. After his conclusions on that experiment, Pavlov would ring a bell before feeding them and as expected they again started salivating. The reason behind their behavior can be their ability to learn** because they had learned that after the bell, they’ll be fed**. Another thing to ponder is, the dog doesn’t salivate because the bell is ringing but because given past experiences he had learned that food will follow the bell.

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

What is Machine learning and Why is it Important?

Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives.

It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance.

To help you understand this topic I will give answers to some relevant questions about machine learning.

But before we answer these questions, it is important to first know about the history of machine learning.

A Brief History of Machine Learning

You might think that machine learning is a relatively new topic, but no, the concept of machine learning came into the picture in 1950, when Alan Turing (Yes, the one from Imitation Game) published a paper answering the question “Can machines think?”.

In 1957, Frank Rosenblatt designed the first neural network for computers, which is now commonly called the Perceptron Model.

In 1959, Bernard Widrow and Marcian Hoff created two neural network models called Adeline, that could detect binary patterns and Madeline, that could eliminate echo on phone lines.

In 1967, the Nearest Neighbor Algorithm was written that allowed computers to use very basic pattern recognition.

Gerald DeJonge in 1981 introduced the concept of explanation-based learning, in which a computer analyses data and creates a general rule to discard unimportant information.

During the 1990s, work on machine learning shifted from a knowledge-driven approach to a more data-driven approach. During this period, scientists began creating programs for computers to analyse large amounts of data and draw conclusions or “learn” from the results. Which finally overtime after several developments formulated into the modern age of machine learning.

Now that we know about the origin and history of ml, let us start by answering a simple question - What is Machine Learning?

#machine-learning #machine-learning-uses #what-is-ml #supervised-learning #unsupervised-learning #reinforcement-learning #artificial-intelligence #ai