What You Need to Know About Deep Reinforcement Learning - KDnuggets

Machine Learning (ML) and Artificial Intelligence (AI) algorithms are increasingly powering our modern society and leaving their mark on everything from finance to healthcare to transportation. If the late half of the 20th century was about the general progress in computing and connectivity (internet infrastructure), the 21st century is shaping up to be dominated by intelligent computing and a race toward smarter machines.

Most of the discussion and awareness about these novel computing paradigms, however, circle around the so-called ‘supervised learning,’ in which Deep Learning (DL) occupies a central position. The recent advancement and astounding success of Deep Neural Networks (DNN) – from disease classification to image segmentation to speech recognition – has led to much excitement and application of DNNs in all facets of high-tech systems.

DNN systems, however, need a lot of training data (labelled samples for which the answer is already known) to work properly, and they do not exactly mimic the way human beings learn and apply their intelligence. Almost all AI experts agree that simply scaling up the size and speed of DNN-based systems will never lead to true “human-like” AI systems or anything even close to it.

Consequently, there is a lot of research and interest in exploring ML/AI paradigms and algorithms that go beyond the realm of supervised learning, and try to follow the curve of the human learning process. Reinforcement Learning (RL) is the most widely researched and exciting of these.

In this article, we briefly discuss how modern DL and RL can be enmeshed together in a field called Deep Reinforcement Learning (DRL) to produce powerful AI systems.

What is Deep Reinforcement Learning?

What is Reinforcement Learning?

Humans excel at solving a wide variety of challenging problems, from low-level motor control (e.g., walking, running, playing tennis) to high-level cognitive tasks (e.g., doing mathematics, writing poetry, conversation).

Reinforcement learning aims to enable a software/hardware agent to mimic this human behavior through well-defined, well-designed computing algorithms. The goal of such a learning paradigm is not to map labelled examples in a simple input/output functional manner (like a standalone DL system) but to build a strategy that helps the intelligent agent to take action in a sequence with the goal of fulfilling some ultimate goal.

More formally, RL refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or how to maximize along a particular dimension over many steps. The following examples illustrate their use:

  • A board game which maximizes the probability of winning
  • A financial simulation maximizing the gain of a transaction
  • A robot moving through a complex environment minimizing the error in its movements

The idea is that the agent receives input from the environment through sensor data, processes it using RL algorithms, and then takes an action towards satisfying the predetermined goal. This is very similar to how we humans behave in our daily life.

Some Essential Definitions in Deep Reinforcement Learning

It is useful, for the forthcoming discussion, to have a better understanding of some key terms used in RL.

Agent: A software/hardware mechanism which takes certain action depending on its interaction with the surrounding environment; for example, a drone making a delivery, or Super Mario navigating a video game. The algorithm is the agent.

Action: An action is one of all the possible moves the agent can make. An action is almost self-explanatory, but it should be noted that agents usually choose from a list of discrete possible actions.

Environment: The world through which the agent moves, and which responds to the agent. The environment takes the agent’s current state and action as input, and returns as output the agent’s reward and its next state.

State: A state is a concrete and immediate situation in which the agent finds itself, i.e., a specific place and moment, an instantaneous configuration that puts the agent in relation to other significant things. An example is a particular configuration of a chessboard.

Reward: A reward is the feedback by which we measure the success or failure of an agent’s actions in a given state. For example, in a game of chess, important actions such as eliminating the bishop of the opponent can bring some reward, while winning the game may bring a big reward. Negative rewards are also defined in a similar sense, e.g., loss in a game.

Discount factor: The discount factor is a multiplier. Future rewards, as discovered by the agent, are multiplied by this factor in order to dampen these rewards’ cumulative effect on the agent’s current choice of action. This is at the heart of RL, i.e., gradually reducing the value of future rewards so that recent actions are given more weight. This is critically important for a paradigm that works on the principle of ‘delayed action.’

Policy: The policy is the strategy that the agent employs to determine the next action based on the current state. It maps states to actions, the actions that promise the highest reward.

Value: The expected long-term return with the discount, as opposed to the short-term reward. The value is defined as the expected long-term return of the current state under a particular policy.

Q-value or action-value: Q-value is similar to value, except that it takes an extra parameter, the current action. It refers to the long-term return of an action taking a specific action under a specific policy from the current state.

#2020 may tutorials #overviews #deep learning #reinforcement learning #deep learning

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What You Need to Know About Deep Reinforcement Learning - KDnuggets
Marget D

Marget D

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Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

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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

What You Need to Know About Deep Reinforcement Learning - KDnuggets

Machine Learning (ML) and Artificial Intelligence (AI) algorithms are increasingly powering our modern society and leaving their mark on everything from finance to healthcare to transportation. If the late half of the 20th century was about the general progress in computing and connectivity (internet infrastructure), the 21st century is shaping up to be dominated by intelligent computing and a race toward smarter machines.

Most of the discussion and awareness about these novel computing paradigms, however, circle around the so-called ‘supervised learning,’ in which Deep Learning (DL) occupies a central position. The recent advancement and astounding success of Deep Neural Networks (DNN) – from disease classification to image segmentation to speech recognition – has led to much excitement and application of DNNs in all facets of high-tech systems.

DNN systems, however, need a lot of training data (labelled samples for which the answer is already known) to work properly, and they do not exactly mimic the way human beings learn and apply their intelligence. Almost all AI experts agree that simply scaling up the size and speed of DNN-based systems will never lead to true “human-like” AI systems or anything even close to it.

Consequently, there is a lot of research and interest in exploring ML/AI paradigms and algorithms that go beyond the realm of supervised learning, and try to follow the curve of the human learning process. Reinforcement Learning (RL) is the most widely researched and exciting of these.

In this article, we briefly discuss how modern DL and RL can be enmeshed together in a field called Deep Reinforcement Learning (DRL) to produce powerful AI systems.

What is Deep Reinforcement Learning?

What is Reinforcement Learning?

Humans excel at solving a wide variety of challenging problems, from low-level motor control (e.g., walking, running, playing tennis) to high-level cognitive tasks (e.g., doing mathematics, writing poetry, conversation).

Reinforcement learning aims to enable a software/hardware agent to mimic this human behavior through well-defined, well-designed computing algorithms. The goal of such a learning paradigm is not to map labelled examples in a simple input/output functional manner (like a standalone DL system) but to build a strategy that helps the intelligent agent to take action in a sequence with the goal of fulfilling some ultimate goal.

More formally, RL refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or how to maximize along a particular dimension over many steps. The following examples illustrate their use:

  • A board game which maximizes the probability of winning
  • A financial simulation maximizing the gain of a transaction
  • A robot moving through a complex environment minimizing the error in its movements

The idea is that the agent receives input from the environment through sensor data, processes it using RL algorithms, and then takes an action towards satisfying the predetermined goal. This is very similar to how we humans behave in our daily life.

Some Essential Definitions in Deep Reinforcement Learning

It is useful, for the forthcoming discussion, to have a better understanding of some key terms used in RL.

Agent: A software/hardware mechanism which takes certain action depending on its interaction with the surrounding environment; for example, a drone making a delivery, or Super Mario navigating a video game. The algorithm is the agent.

Action: An action is one of all the possible moves the agent can make. An action is almost self-explanatory, but it should be noted that agents usually choose from a list of discrete possible actions.

Environment: The world through which the agent moves, and which responds to the agent. The environment takes the agent’s current state and action as input, and returns as output the agent’s reward and its next state.

State: A state is a concrete and immediate situation in which the agent finds itself, i.e., a specific place and moment, an instantaneous configuration that puts the agent in relation to other significant things. An example is a particular configuration of a chessboard.

Reward: A reward is the feedback by which we measure the success or failure of an agent’s actions in a given state. For example, in a game of chess, important actions such as eliminating the bishop of the opponent can bring some reward, while winning the game may bring a big reward. Negative rewards are also defined in a similar sense, e.g., loss in a game.

Discount factor: The discount factor is a multiplier. Future rewards, as discovered by the agent, are multiplied by this factor in order to dampen these rewards’ cumulative effect on the agent’s current choice of action. This is at the heart of RL, i.e., gradually reducing the value of future rewards so that recent actions are given more weight. This is critically important for a paradigm that works on the principle of ‘delayed action.’

Policy: The policy is the strategy that the agent employs to determine the next action based on the current state. It maps states to actions, the actions that promise the highest reward.

Value: The expected long-term return with the discount, as opposed to the short-term reward. The value is defined as the expected long-term return of the current state under a particular policy.

Q-value or action-value: Q-value is similar to value, except that it takes an extra parameter, the current action. It refers to the long-term return of an action taking a specific action under a specific policy from the current state.

#2020 may tutorials #overviews #deep learning #reinforcement learning #deep learning

Tia  Gottlieb

Tia Gottlieb

1595573880

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

Image for post

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

Mikel  Okuneva

Mikel Okuneva

1603735200

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time


Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.


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