Obie  Rowe

Obie Rowe

1599026700

Deep Reinforced Learning: Addressing Complex Enterprise Challenges

Current algorithms lean more towards narrow learning, meaning they are good at learning and solving specific types of problems under specific conditions. These algorithms take a humongous amount of data as compared to humans who can learn from relatively few learning encounters. The transfer process of these learnings from one problem domain to another domain is somewhat limited as well.

Recently, reinforcement learning (RL) has been gaining popularity compared to other deep learning techniques. The buzz around reinforcement learning started with the advent of AlphaGo by DeepMind. AlphaGo was built to play the very complex game of Go. The essence of RL is that it can train models through the interaction with the environment and learn and calibrate from their mistakes. Learning happens through a delayed and cumulative reward system where an agent deduces an action, which then acts on the environment to make a state change. The agent takes the next best action based on the optimized delayed reward. The system retains the learning and recalls the best action when a similar circumstance arises.

This feature of RL — to improve and evolve without constant human or programmatic intervention — makes it interesting to real-world problems like autonomous driving. The autonomous driving puzzle cannot be solved by conventional AI alone, which typically leverages computer vision using Convolutional Neural Networks (CNNs). Autonomous driving cannot be modeled as a supervised learning problem due to strong interaction with the environment, including other vehicles, pedestrians, driver behavior, and road infrastructures. At an abstract level, an autonomous driving agent is an implementation of three steps of sequential tasks: sense (recognize), plan, and control.

Figure 2

Figure 2: Autonomous Driving sequential tasks

The recognition problem has been solved with a high degree of accuracy with advancements in computer vision. We now have the capability to detect pedestrians, curb space, free space between vehicles, traffic signs with low computing power, and high accuracy. Path planning is the most difficult piece of the puzzle. One needs to take a series of environmental inputs and incorporate recognitions and predictions to chart the future driving actions that maneuver the vehicle safely to its destination (reward) by avoiding any accidents/delays (penalties). The control task is relatively easy, as it simply involves passing the signal to either speed (brake, accelerator) or direction control (steering).

What makes RL so attractive and suitable for autonomous driving is the fact that driving is a multi-player, multi-state problem that involves implicit negotiations and interactions. There can literally be thousands of combinations while entering or exiting a freeway ramp or negotiating a crowded roundabout. The driver’s temperament, skill level, and experience level cannot be programmed with supervised learning. Through exploration and exploitation techniques, RL can be a great tool for boundary cases, as it can learn from its own experiences and actions that lead to a reward. RL, in a way, closely mimics human decision making — it is like learning to ride a bicycle by trial and error. Mathematically, this state model is best explained with the Markov Decision Process (MDP).

Advancements in reinforcement learning are slowly addressing some of the challenges of huge training data requirements and intense computing needs. There are new advancements in the DQN (Deep Q Network where Q mathematically models the reward function), where an AI agent can learn to drive just by observing the synthetic scenes with virtually simulated miles. The amazing thing is that this learning can happen without much prior information about actual physically driven miles. DQNs currently do have some limitations especially when it comes to dealing with high-dimensional observation space like autonomous driving, which is a continuous domain. Significant progress is being made in this space with Google Deepmind’s innovations with the Deep Deterministic Policy Gradient (DDPG) algorithms to address these limitations.

#machine learning #artificial intelligence #deep learning #neural networks #self-driving cars #reinforcement learning #autonomous driving #agi

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Deep Reinforced Learning: Addressing Complex Enterprise Challenges
Marget D

Marget D

1618317562

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.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

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

Obie  Rowe

Obie Rowe

1599026700

Deep Reinforced Learning: Addressing Complex Enterprise Challenges

Current algorithms lean more towards narrow learning, meaning they are good at learning and solving specific types of problems under specific conditions. These algorithms take a humongous amount of data as compared to humans who can learn from relatively few learning encounters. The transfer process of these learnings from one problem domain to another domain is somewhat limited as well.

Recently, reinforcement learning (RL) has been gaining popularity compared to other deep learning techniques. The buzz around reinforcement learning started with the advent of AlphaGo by DeepMind. AlphaGo was built to play the very complex game of Go. The essence of RL is that it can train models through the interaction with the environment and learn and calibrate from their mistakes. Learning happens through a delayed and cumulative reward system where an agent deduces an action, which then acts on the environment to make a state change. The agent takes the next best action based on the optimized delayed reward. The system retains the learning and recalls the best action when a similar circumstance arises.

This feature of RL — to improve and evolve without constant human or programmatic intervention — makes it interesting to real-world problems like autonomous driving. The autonomous driving puzzle cannot be solved by conventional AI alone, which typically leverages computer vision using Convolutional Neural Networks (CNNs). Autonomous driving cannot be modeled as a supervised learning problem due to strong interaction with the environment, including other vehicles, pedestrians, driver behavior, and road infrastructures. At an abstract level, an autonomous driving agent is an implementation of three steps of sequential tasks: sense (recognize), plan, and control.

Figure 2

Figure 2: Autonomous Driving sequential tasks

The recognition problem has been solved with a high degree of accuracy with advancements in computer vision. We now have the capability to detect pedestrians, curb space, free space between vehicles, traffic signs with low computing power, and high accuracy. Path planning is the most difficult piece of the puzzle. One needs to take a series of environmental inputs and incorporate recognitions and predictions to chart the future driving actions that maneuver the vehicle safely to its destination (reward) by avoiding any accidents/delays (penalties). The control task is relatively easy, as it simply involves passing the signal to either speed (brake, accelerator) or direction control (steering).

What makes RL so attractive and suitable for autonomous driving is the fact that driving is a multi-player, multi-state problem that involves implicit negotiations and interactions. There can literally be thousands of combinations while entering or exiting a freeway ramp or negotiating a crowded roundabout. The driver’s temperament, skill level, and experience level cannot be programmed with supervised learning. Through exploration and exploitation techniques, RL can be a great tool for boundary cases, as it can learn from its own experiences and actions that lead to a reward. RL, in a way, closely mimics human decision making — it is like learning to ride a bicycle by trial and error. Mathematically, this state model is best explained with the Markov Decision Process (MDP).

Advancements in reinforcement learning are slowly addressing some of the challenges of huge training data requirements and intense computing needs. There are new advancements in the DQN (Deep Q Network where Q mathematically models the reward function), where an AI agent can learn to drive just by observing the synthetic scenes with virtually simulated miles. The amazing thing is that this learning can happen without much prior information about actual physically driven miles. DQNs currently do have some limitations especially when it comes to dealing with high-dimensional observation space like autonomous driving, which is a continuous domain. Significant progress is being made in this space with Google Deepmind’s innovations with the Deep Deterministic Policy Gradient (DDPG) algorithms to address these limitations.

#machine learning #artificial intelligence #deep learning #neural networks #self-driving cars #reinforcement learning #autonomous driving #agi

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.


#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020