Deep Reinforcement Learning, New AI Algorithms

Deep Reinforcement Learning, New AI Algorithms

Deep reinforcement learning is a technique that combines the framework of reinforcement learning with the power of artificial neural networks (ANNs) to help software agents achieve their goals in new and improved ways. As reinforcement learning is contingent upon training a software agent to behave in a certain way within the confines of a particular environment in accordance with positive reinforcement that takes the form of a reward, software engineers have been able to train reinforcement learning algorithms to perform certain niche tasks in an extremely effective way, such as playing certain video games at a higher level than a human being, among other things. Nevertheless, the applications of these methods are somewhat limited with respect to real-world business applications.

This being the case, reinforcement learning is currently a prime example of narrow artificial intelligence, or a form of AI that is focused on achieving a very specific or particular task or objective. On the other hand, the structure of ANNs has enabled software developers to create a wide range of products and services, as these neural networks are based upon the functionality of the human brain. Likewise, deep reinforcement learning combines the huge level of inputs that are associated with deep neural networks with the sequential decision-making capabilities of reinforcement learning algorithms to leverage artificial intelligence in a multitude of contexts that were previously thought to be impossible.

How does deep reinforcement learning work?

Put in the simplest of terms, the goal of reinforcement learning is to pick or map the best possible action for any given situation or state. In turn, this means that these actions must be first ranked, and then assigned values that are relative to one another. For example, if the action is learning to speak another language, learning to speak such a language would entail something different for a 10-year-old school child than it would for a 40-year-old business owner, due to the time constraints that these two respective people would have to deal with, as well as the ways in which the cognitive functions of the human brain develop over time, among other things. Alternatively, walking into a bank and screaming the phrase “get down on your knees” would be different from repeating this same phrase in an instructional exercise class at a local gym, as actions cannot be predicted without proper context.

Taking all of this into account, reinforcement learning can be extremely effective at predicting limited outcomes, such as the number of moves that a chess player will initiate during a match, or the number of turns that a user will make when driving a car in a video game. However, when these principles are applied on a broader level, these algorithms can struggle to maintain their accuracy. This is where the application of deep learning comes into play, particularly as it pertains to artificial neural networks. To this point, ANNs essentially work as function approximators, and as such, can be extremely useful when combined with reinforcement learning, where the action space of a particular problem can be too expansive to truly know or grasp.

To accomplish this, a neural network will be trained to approximate a policy or value function, which can then be used to train the model on a set of said functions, with the goal of uncovering how valuable these functions are in regard to a particular target as it relates to reinforcement learning. This is in contrast to the ways in which reinforcement learning works without the use of deep learning, as a lookup table will instead be used to index, store, and update all potential scenarios and their associated values, which is all but impossible when dealing with large scale problems in the realm of artificial intelligence and machine learning. With all this being said, deep reinforcement learning is particularly effective at training algorithms to recognize visual images and data.

Image recognition

One of the most common applications of deep reinforcement learning in the current technological landscape is image recognition. Subsequently, convolutional neural networks can be trained to recognize a specific set of inputs in visual form, such as the movements of a video game character on a television screen or computer monitor, or the occurrence of a muscle strain within a medical image, in addition to many others. In doing so, the neural network will rank the possible actions that can be performed within a particular scenario as it pertains to a visual image, such as predicting whether a video game character will run, jump, or hide. To take it a step further, correctly predicting each of these actions would grant a reward, such as 5 point reward for running, a 7-point reward for jumping, and a 9-point reward for hiding. Through this process, the agent will be able to determine the best action to take in regard to identifying a specific image.

Due to the fact that so many different types of machine learning and artificial intelligence algorithms have been developed in the past decade alone, many of the techniques that have been used to fuel this development can be combined to discover newfound techniques and abilities. Deep reinforcement learning is an example of such a combination of ideas, as this method has given rise to new applications of both reinforcement learning and artificial neural networks that had not been previously considered. Furthermore, these algorithms will continue to be used to create cutting-edge technological solutions in the years to come.

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