Basics of Reinforcement Learning AI Engineers Must Know Of

Machine learning (ML) or ML-powered algorithms are getting deployed in every business sector you can think of, these days. The role of ML and artificial intelligence (AI) in the functioning of our society has been on an unprecedented high off late, with new innovations getting discovered each day. From finance to healthcare, to travel, to manufacturing, AI and ML are being deployed across industry sectors, and benefitting them heavily.

If the second half of the 20th century got known for the impressive progress in web connectivity and computing power, 21st century is getting dominated by the emergence of a plethora of disruptive technologies such as ML and AI.

What Do We Mean by Reinforcement Learning?

Humans are great at solving an array of challenging issues, ranging from low-level motor control activities (running, playing football, walking) to high-level perceptive tasks (doing poetry, writing, conversation, mathematics). The technique of reinforcement learning allows hardware/software to imitate human behavior by leveraging computer algorithms that are specially designed for the said purpose.

In a more formal language, reinforcement learning deals in the development of goal-specific algorithms that help learn a machine on how to accomplish a sophisticated objective, or how to optimize the chances of attaining a predetermined result.

#ai engineers #ai certifications #ai #machine learning #algorithms #artificial intelligence

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Basics of Reinforcement Learning AI Engineers Must Know Of

Machine learning (ML) or ML-powered algorithms are getting deployed in every business sector you can think of, these days. The role of ML and artificial intelligence (AI) in the functioning of our society has been on an unprecedented high off late, with new innovations getting discovered each day. From finance to healthcare, to travel, to manufacturing, AI and ML are being deployed across industry sectors, and benefitting them heavily.

If the second half of the 20th century got known for the impressive progress in web connectivity and computing power, 21st century is getting dominated by the emergence of a plethora of disruptive technologies such as ML and AI.

What Do We Mean by Reinforcement Learning?

Humans are great at solving an array of challenging issues, ranging from low-level motor control activities (running, playing football, walking) to high-level perceptive tasks (doing poetry, writing, conversation, mathematics). The technique of reinforcement learning allows hardware/software to imitate human behavior by leveraging computer algorithms that are specially designed for the said purpose.

In a more formal language, reinforcement learning deals in the development of goal-specific algorithms that help learn a machine on how to accomplish a sophisticated objective, or how to optimize the chances of attaining a predetermined result.

#ai engineers #ai certifications #ai #machine learning #algorithms #artificial intelligence

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

Paper Summary: Discovering Reinforcement Learning Agents

Introduction

Although the field of deep learning is evolving extremely fast, unique research with the potential to get us closer to Artificial General Intelligence (AGI) is rare and hard to find. One exception to this rule can be found in the field of meta-learning. Recently, meta-learning has also been applied to Reinforcement Learning (RL) with some success. The paper “Discovering Reinforcement Learning Agents” by Oh et al. from DeepMind provides a new and refreshing look at the application of meta-learning to RL.

**Traditionally, RL relied on hand-crafted algorithms **such as Temporal Difference learning (TD-learning) and Monte Carlo learning, various Policy Gradient methods, or combinations thereof such as Actor-Critic models. These RL algorithms are usually finely adjusted to train models for a very specific task such as playing Go or Dota. One reason for this is that multiple hyperparameters such as the discount factor γ and the bootstrapping parameter λ need to be tuned for stable training. Furthermore, the very update rules as well as the choice of predictors such as value functions need to be chosen diligently to ensure good performance of the model. The entire process has to be performed manually and is often tedious and time-consuming.

DeepMind is trying to change this with their latest publication. In the paper, the authors propose a new meta-learning approach that discovers the learning objective as well as the exploration procedure by interacting with a set of simple environments. They call the approach the Learned Policy Gradient (LPG). The most appealing result of the paper is that the algorithm is able to effectively generalize to more complex environments, suggesting the potential to discover novel RL frameworks purely by interaction.

In this post, I will try to explain the paper in detail and provide additional explanation where I had problems with understanding. Hereby, I will stay close to the structure of the paper in order to allow you to find the relevant parts in the original text if you want to get additional details. Let’s dive in!

#meta-learning #reinforcement-learning #machine-learning #ai #deep-learning #deep learning

Gaming ML/AI based on Reinforcement Learning

Today you turn on your TV, you listen to the radio, you read a newspaper and, unbelievable… you most probably come across about machine learning and artificial intelligence.
The Machine Learning approach helps humans by taking decisions to solve a problem into automatic flow identifying hidden patterns, and observing multiple state variables impossible to be detected easily by human beings.
As it is for humans, school is the base for knowing and progressing, so the first thing that is involved in ML is training.
Let’s send ML to school and explore how these algorithms create their knowledge. ;)

#reinforcement-learning #tensorflow #machine-learning-ai #federated-learning #ai

This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai