Introduction
The training of Machine Learning models helps in making a series of decisions as the agents learn how to accomplish a goal in a vague and potentially complex environment. Whenever an Artificial Intelligence faces a situation in Reinforcement Learning, which is similar to a game learning, then efforts are made to find a solution to the problem by the computer employing trials and errors. Also, rewards or penalties are given to the AI system for the action it performs to be sure that the machine follows the instructions of its commander or the programmer. Although the reward policy is set by the designer with no suggestions or hints provided by the designer for solving the game, it is totally on the model to find out how to perform the task to increase the reward, beginning with no skill and finishing it with sophisticated tactics and excellent skills. With the power of search and many trials, Reinforcement Learning has become the most productive way to hint at machine creativity. In polarity to human beings, Artificial Intelligence can collect the experience from thousands of aligned game plays when the Reinforcement Learning algorithm is made to run on adequately robust computer infrastructure.
Examples of Reinforcement Learning
Applications of the reinforcement learning were earlier limited by the less efficient computer infrastructure. As the early progress is now swiftly changing with the powerful new computational technologies, which is now introducing the way to entirely new inspiring applications.
In reinforcement learning training, the models that control autonomous cars can be considered as an excellent example of a potential application. In perfect situations, the computer shall get no instruction on driving the car. Hardwiring anything connected with the task would be avoided by the programmer, and it will allow the machine to learn from its errors. In the accurate situation, the hard-wired element would be the reward function.
For example, in regular conditions, we would need an autonomous vehicle to put safety first, reduce travelling time, decrease pollution, obey the rules made by the law and offer the rider a comfortable ride. Speed much more than the comfort of the driver will be emphasized with an autonomous race car. Anything which could happen on the road cannot be predicted by the programmer. The programmer develops the reinforcement learning agent in such a way that it is capable of learning from the system of rewards and penalties, instead of building lengthy “if-then” instructions. Rewards are given to the agent (another name for reinforcement learning algorithms performing the task) for reaching a certain goal.
Challenges with Reinforcement Learning
Preparing the simulation environment is the main challenge in reinforcement learning, which is extremely dependant on the task to be done. Developing the simulation environment is comparatively simple when the model has to go superhuman in chess, go, or Atari games. In the matter of building a model capable of driving an autonomous car, making a realistic simulator is important before letting the car ride on the road. It is important for the model to learn how to break or avoid a crash in a safe environment, where giving up even a thousand cars come at least cost. Shifting the model out from the training environment and into the real world where things get problematic help in improving the capability and skills of the learners to apply solutions and innovative ideas in resolving problems.
The system of rewards and penalties are the only way to communicate with the network; there is no other way to do so. This is specific, may lead to catastrophic forgetting, in which gaining some new knowledge causes some of the old one to be erased from the network.
Reaching a local optimum is yet another challenge – the task is performed by the agent in the way it is, but not in the optimal or required way. A “jumper” jumping like a kangaroo and not doing the thing which was expected that is walking can be considered as a great example, and this can be found in our recent blog post.
In the end, there are agents that will reform the prize without doing the task it was designed for. The OpenAI video given below can be considered as an interesting example; in this, the agent learns the way to gain rewards, but not to finish the race.
Reinforcement Learning beginner to master - AI in Python
Machine Learning vs Deep Learning vs Reinforcement Learning
Beyond doubt, there shall be no clear division between machine learning, deep learning, and reinforcement learning. It can be considered as a relationship between a parallelogram – rectangle – square, in which machine learning is in the broadcast category, whereas the deep reinforcement learning is the most narrow one. In a similar manner, reinforcement learning is a form of specialized application of machine and deep learning techniques, designed in such a way to solve problems in a certain way.
Though the ideas appear to differ, there is no sharp divide visible between these subtypes. Nonetheless, they combine themselves within projects; this is because models are prepared in such a way that it does not stick to a “pure type” in order to perform the task in the much more effective way possible. So “what precisely distinguishes machine learning, deep learning and reinforcement learning” is basically a twisted question to answer.
What is Machine Learning?
It is a form of an AI in which computers are provided with an ability to gradually improve the performance of a certain task with the data, without being directly controlled. When a controller can provide a label for every training input into the machine learning system, then supervised machine learning takes place.
Example: by surveying the historical data taken from coal mines, deepsense.ai builds an automated system for anticipating seismic threat events up to 8 hours before it takes place. From 24 coal mines, the records of seismic events were taken that had combined data for several months. The model had the capability to acknowledge the probability of an explosion by analyzing the readings from the previous 24 hours.
From the point of view of AI, on a clarified and normalized dataset, only a single model was performing a single task.
When the model is provided only with input data, then unsupervised learning takes place. It has to find the hidden structure or relationships within it by digging through the data. The designer might not be aware of what the structure is or what the machine learning model is going to find. An example we employed was an agitate prediction. We have gone through customer data and developed an algorithm for similar group customers. However, the groups were not chosen by us. With time, we could recognize high-risk groups (those with high agitate rate), and our client knew which customer should they approach first.
The other example of unsupervised learning is anomaly detection; the element that doesn’t fit in with the group has to be spotted by the algorithm. It might be a potentially fraudulent transaction or any other event associated with breaking the norm or a flawed product.
What is Deep Learning?
Deep learning has several layers of neural networks, which are made to perform much more sophisticated tasks. The construction of a deep learning model was inspired by the structure of the human brain but simplified. Deep learning models have some neural network layers which are, in principle, responsible for moderate learning more abstract features about certain data. Although amazing results are provided by deep learning solutions, in the terns of scale, they are nothing in front of the human brain. The whole network is trained as a single whole, and each layer uses the outcome of a previous one as an input. Creating an artificial neural network is not a new core concept. Extended adoption has got frameworks like TensorFlow, Keras, and PyTorch, all of these have made constructing machine learning models much more convenient.
Example: deepsense.ai developed a deep learning-based model for the National Ocean and Atmospheric Administration (NOAA). It was developed to identify Right whales from aerial photos clicked by researchers. Deepsense.ai work with the NOAA for further information about the endangered species. From a technical viewpoint, identifying a particular specimen of whales from aerial photos is absolute deep learning. The solution has some of the machine learning models which carry out separate tasks. The first one had the task of finding the head of the whale in the photo, whereas the second one balances the photo by cutting and turning it, which in the end gave a passport size photo (a unified view) of a single whale.
The third model had the responsibility for identifying certain whales from the photograph that had been made and processed before. At the blow head bonnet – tip a network of 5 million neurons are located. More than 941,000 neurons were looking for the head of the whale, and more than 3 million neurons were used to distinguish certain whales. Which are more than 9 million neurons doing the task, which may seem to be a lot, but is quite less as compared to more than 100 billion neurons which are working in the human brain? Later on, we used a familiar deep learning-based solution to treat diabetic retinopathy using the images of retinas of patients.
What is Reinforcement Learning?
Reinforcement learning, as said it is the system of rewards and penalties to force the computer to solve a problem itself. Human involvement in changing the environment and tweaking the system of rewards and penalties is very less. As the computer increases the reward, it is liable to seek unexpected ways of doing it. Prevention from exploiting the system and motivating the machine to do the task in the way expected is the focus of human involvement. When there is no proper way to perform a task, then reinforcement learning is useful. Still, there are rules the model has to obey to perform its duties correctly. For example, take the road code.
Example: by tweaking and seeking the optimal policy for deep reinforcement learning, we make an agent that reaches the superhuman level in playing Atari games only in 20 minutes. The same algorithms, in principle, can be utilized to make AI for an autonomous car or a prosthetic leg. Giving the model an Atari video game to play, such as Arkanoid or space invaders, is, in fact, one of the best ways to evaluate the reinforcement learning approach. As claimed by Google Brain’s Marc G. Bellemare, who introduced Atari video games as a reinforcement learning benchmark, “although challenging, these environments remain simple enough that we can hope to achieve, measurable progress as we attempt to solve them.”
In certain if artificial intelligence is going to drive a car and learn how to play some Atari classics can be referred to as a meaningful intervening milestone. In an autonomous vehicle, a potential application of reinforcement learning is an interesting case. A designer is unable to guess all situations which could happen on the road, so leaving the model to learn itself with the system of penalties and rewards in a different environment is practically the most efficient way for the AI to expand the experience of both that it has and collects.
Discriminating Factor of Reinforcement Learning
The main discriminate factor of reinforcement learning is how the agent is taught. Rather than checking the data given, the model interconnects with the environment, finding out ways to increase the reward. When taking the case of deep reinforcement learning, a neural network is in charge of storing the experiences and thus enhance the way the task is done.
Conclusion
Even though reinforcement learning, machine learning, and deep learning are interrelated, no one of them, in particular, is going to replace the other. The respected French scientist and the head of research at Facebook “Yann LeCun,” jokes that reinforcement learning is the cherry on the top of the cake where machine learning is the cake itself and deep learning the icing. Without the previous repetition, the cherry would be nothing on the top. In many of the cases, using classical machine learning techniques will be sufficient. Purely algorithmic ways that do not involve machine learning seem to be useful in business data processing or managing the database.
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