Understanding the concept and significance of Deep Reinforcement Learning
The field of reinforcement learning has exploded in recent years with the success of supervised deep learning continuing to pile up. People are now using deep neural nets to learn how to use intelligent behavior in complex dynamic environments. Deep reinforcement learning is one of the most exciting fields in artificial intelligence where we combine the power of deep neural networks to comprehend the world with the ability to act on that understanding.
In deep learning, we take samples of data and supervise the way we compress and code the data representation in a manner that you can reason about. Deep reinforcement learning is when we take this power and apply it to a world where sequential decisions are to be made.
We use deep reinforcement learning to solve tasks where an agent or an intelligent system has to make a sequence of decisions that directly affect the world around the agent. While trial-and-error is the fundamental process by which reinforcement learning agents learn, they do use neural networks to represent the world.
Types of learning
All types of machine learning– supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning are supervised by a loss function. Even in unsupervised learning, there is some kind of human intervention required to determine and provide inputs on what is good or bad. Only the cost of human labor required to obtain this supervision is low. Thus, the challenges and the exciting opportunities of reinforcement learning lie in how we get that supervision in the most efficient way possible.
In supervised learning, you take a bunch of data samples and use them to learn patterns to interpret similar samples in the future. However, in reinforcement learning, you teach an agent through experience. So the essential design step in reinforcement learning is to provide the environment in which the agent has to experience and gain rewards. In other words, a designer has to design not only the algorithm but also the environment where the agent is trying to solve a task.
The most difficult element in reinforcement learning is the reward – good vs bad. For example, when a baby learns to walk, success is the ability to walk across the room and failure is the inability to do so. Simple! Well, this is reinforcement learning in humans. How we learn from so few examples through trial-and-error is a mystery. It could be the hardware – 230 million years of bipedal movement data that is genetically encoded in us or it could be the ability to learn quickly through the few minutes or hours or years of observing other humans walking. So the idea is if there was no one around to observe, we would never be able to walk. Another possible explanation is the algorithm that our brain uses to learn which has not yet been understood.
The promise of deep learning is that it converts raw data into meaningful representations whereas the promise of deep reinforcement learning is that it builds an agent that uses this representation to achieve success in the environment.
Deep Q learning
Q-learning is a simple and powerful algorithm that helps an agent to take action without the need for a policy. Depending on the current state, it finds the best action on a trial-and-error basis. While this works for practical purposes, once the problem size starts increasing, maintaining a Q-value table becomes infeasible considering the amount of memory and time that would be required. This is where neural networks come in.
From a given input of action and state, a neural network approximates the Q-value function. Basically, you feed the initial state into the neural network to get the Q-value of all possible actions as the output. This neural network is called Deep Q-Network. However, DQN is not without challenges. The input and output undergo frequent changes in reinforcement learning with progress in exploration. The concepts of experience replay and target network help control these changes.
Read more: Top 10 Machine Learning Algorithms in 2020
Deep Reinforcement Learning Frameworks
Here are the three Deep Reinforcement Learning frameworks:
1. Tensorflow reinforcement learning
RL algorithms can be used to solve tasks where automation is required. However actual implementation is easier said than done. You can ease your pain by using TF-Agents, a flexible library for TensorFlow to build reinforcement learning models. TF-Agents makes it easy to use reinforced learning for TensorFlow. TF-Agents enables newbies to learn RL using Colabs, documentation, and examples as well as researchers who want to build new RL algorithms. TF-Agents is built on top of TensorFlow 2.0. It uses TF-Eagers to make development and debugging a lot easier, tf.keras to define networks and tf.function to make things faster. It is modular and extensible helping you to pick only those pieces that you need and extend them as required. It is also compatible with TensorFlow 1.14.
2. Keras reinforcement learning
Keras is a free, open-source, neural network Python library that implements modern deep reinforcement learning algorithms. Using Keras, you can easily assess and dabble with different algorithms as it works with OpenAI Gym out of the box. Keras offers APIs that are easy and consistent, thus reducing the cognitive load. These APIs can handle the building of models, defining of layers or implementation of multiple input and output models. Keras is fast to deploy, easy to learn, and supports multiple backends.
3. PyTorch Reinforcement learning
PyTorch is an open-source machine learning library for Python based on Torch and is used for applications such as natural language processing. It consists of a low-level API that focuses on array expressions. This framework is mostly used for academic research and deep learning applications that require optimized custom expressions. The PyTorch framework has a high processing speed with complex architecture.
All these frameworks have gained immense popularity and you can choose the one that suits your requirements.
While deep reinforcement learning holds immense potential for development in various fields, it is vital to focus on AI safety research as well. This is going to be fundamental in the coming years in order to tackle threats like autonomous weapons and mass surveillance. We should, therefore, ensure that there are no monopolies that can enforce their power with the malignant use of AI. So international laws need to keep up with the rapid progress in technology.
We have tried to brush across the basics of deep reinforcement learning and the top 3 frameworks that are in use currently. Want to know more about this amazing technology? Reach out to us at Fingent!
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Key Differences Between Machine Learning And Deep Learning Algorithms
Artificial Intelligence is on the rise in this digital era. According to IDC’s latest market report, global investment of businesses in AI and cognitive systems is increasing and will mount to $57.6 billion by the year 2021.
Artificial Intelligence holds a high-scope in implementing intelligent machines to perform redundant and time-consuming tasks without frequent human intervention. AI’s capability to impart a cognitive ability in machines has 3 different levels, namely, Active AI, General AI, and Narrow AI. Artificially intelligent systems use pattern matching to make critical decisions for businesses.
Related Reading: Know the different types of Artificial Intelligence.
Categories Of Artificial Intelligence
Machine learning and Deep learning are 2 categories of AI used for statistical modeling of data. The paradigms for the 2 models vary from each other. Let us walk through the key differences between the two:
Machine Learning: Process Involved
Machine learning is a tool or a statistical learning method by which various patterns in data are analyzed and identified. In machine learning, each instance in a data set is characterized by a set of attributes. Here, the computer or the machine is trained to perform automated tasks with minimal human intervention.
To train a model in a machine learning process, a classifier is used. The classifier makes use of characteristics of an object to identify the class it belongs to. For instance, if an object is a car, the classifier is trained to identify its class by feeding it with input data and by assigning a label to the data. This is called Supervised Learning.
To train a machine with an algorithm, the following are the standard steps involved:
- Data collection
- Training the Classifier
- Analyze Predictions
While gathering data, it is critical to choose the right set of data. This is because it is the data that decides the success or failure of the algorithm. This data that is chosen to train the algorithm is called feature. This training data is then used to classify the object type. The next step involves choosing an algorithm for training the model. Once the model is trained, it is used to predict the class it belongs to.
For instance, when an image of a car is given to a human, he can identify it belongs to the class vehicle. But a machine requires to be trained via an algorithm to predict that it is a car through its previous knowledge.
Various machine learning algorithms include Decision trees, Random forest, Gaussian mixture model, Naive Bayes, Linear regression, Logistic regression, and so on.
Deep Learning: Process Involved
Deep learning can be defined as a subcategory of machine learning. Inspired by ANN (Artificial Neural Networks), deep learning is all about various ways in which machine learning can be executed. Deep learning is performed through a neural network, which is an architecture having its layers, one stacked on top of the other.
A neural network has an input layer that can be pixels of an image or even data of a particular time series. The next layer comprises of a hidden layer that is commonly known as weights and learns while the neural network is trained. The final layer or the third layer is that predicts the result based on the input fed into the network.
The neural network thus makes use of a mathematical algorithm to predict the weights of the neurons. Additionally, it provides an output close to the most accurate value.
Automate Feature Extraction is a way in which process performed to find a relevant set of features. It is performed by combining an existing set of features using algorithms such as PCA, T-SNE, etc. For instance, to extract features manually from an image while processing it, the practitioner requires to identify features on the image such as nose, lips, eyes, etc. These extracted features are fed into the classification model.
The process of feature extraction is performed automatically by the Feature Extraction process in Deep Learning by identifying matches.
Related Reading: AI and ML are revolutionizing software development. Here’s how!
Key Differences Between Machine Learning And Deep Learning Algorithms
Though both Machine Learning and Deep Learning are statistical modeling techniques under Artificial Intelligence, each has its own set of real-life use cases to depict how one is different from the other. Let us walk through the major differences between the modeling techniques.
1. Data Dependencies
Machine learning algorithms are employed mostly when it comes to small data sets. Even though both machine learning and deep learning can handle massive amounts of data sets, deep learning employs a deep neural network on the data as they are ‘data-hungry’. The more data there is, the more will be the number of layers, that is the network depth. This increases the computation as well and thus employs deep learning for better performance when the data set sizes are huge.
Interpretability in Machine Learning refers to the degree to which a human can understand and relate to the reason and rationale behind a specific model’s output. The major objective of Interpretability in machine learning is to provide accountability to model predictions.
Certain algorithms under machine learning are easily interpretable, such as the Logistic and Decision Tree algorithms. On the other hand, Naive Bayes, SVM, XGBoost algorithms are difficult to interpret.
Interpretability for deep learning algorithms can be referred to as difficult to nearly impossible. If it is possible to reason about similar instances, such as in the case of Decision Trees, the algorithm is interpretable. For instance, the k-Nearest Neighbors is a machine learning algorithm that has high interpretability.
3. Feature Extraction
When it comes to extracting meaningful features from raw data, deep learning algorithms are the most suitable method. Deep learning does not depend on binary patterns or a histogram of gradients, etc., but it extracts hierarchically in a layer-wise manner.
Machine learning algorithms, on the other hand, depend on handcrafted features as inputs to extract features.
4. Training And Inference/ Execution Time
Machine learning algorithms can train very fast as compared to deep learning algorithms. It takes a few minutes to a couple of hours to train. On the other hand, deep learning algorithms deploy neural networks and consumes a lot of inference time as it passes through a multitude of layers.
Machine learning algorithms can be decoded easily. Deep learning algorithms, on the other hand, are a black box. Machine learning algorithms such as linear regression and decision trees are made use of in banks and other financial organizations for predicting stocks etc.
Deep learning algorithms are not fully reliable when it comes to deploying them in industries.
Both machine learning and deep learning algorithms are used by businesses to generate more revenue. To know more about how your business can benefit from artificially intelligent systems and which algorithms can be leveraged for a positive business outcome, call our strategists right away!
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What Machine Learning is and why it is important in business
Given the increasing prevalence of data today, machine learning has evolved rapidly to advance computing a step forward. Enabling computers to automatically learn and improve performance using data, ML is viewed as a disruptive technology with the potential to transform areas like enterprise, manufacturing, healthcare, e-commerce etc. Global ML adoption rates points to huge investments and diverse use cases in the coming years making it one key technology to focus your efforts.
This infographic focuses on the essentials of machine learning together with how it helps in uncovering insights from huge data stacks. Besides, its applications, global adoption rates, and future developments are also dealt with to give you a basic understanding of machine learning so that it can be put to use for uplifting your business processes and operations.
Deploying proper machine learning systems can uplift your business and maximize the ROI. Get in touch with us today to discover and implement the right machine learning solution that will transform your business altogether.