Deep reinforcement learning (deep RL)
Deep reinforcement learning (deep RL) is an advanced approach to training artificial intelligence (AI) that merges reinforcement learning with deep learning techniques. In reinforcement learning, AI systems learn to make decisions by exploring different actions within an environment, gradually refining their strategies based on rewards received for achieving specific goals. Deep RL enhances this process by incorporating deep neural networks with multiple layers, enabling the AI to efficiently analyze vast datasets and recognize complex patterns.
This method has led to significant breakthroughs in various domains, particularly in games where deep RL algorithms, such as AlphaGo, have outperformed top human players in complex board games and video games. While deep RL offers promising capabilities, it also raises concerns about job displacement and ethical implications, as its increasing efficiency could lead to significant changes in the workforce. Proponents believe that, if developed responsibly, AI could enhance human productivity and open new avenues for scientific advancement, potentially leading to a more automated and efficient society. Overall, deep RL represents a significant evolution in AI, combining the strengths of learning from both large datasets and strategic decision-making.
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Deep reinforcement learning (deep RL)
Deep reinforcement learning, also known as deep RL, is a specialized method for training artificial intelligence (AI). It is a subset of both machine learning and neural networks. Machine learning encompasses all methods by which scientists train modern algorithms to carry out tasks; neural networks teach computers to process data like the human brain. In reinforcement learning, the AI works to move from a starting state to a goal state by experimenting with various options. When it achieves the goal state, it may be rewarded by the data scientists managing the program, letting the algorithm know that its result was correct. This helps the AI learn faster. Deep RL includes neural networks with more than three layers, allowing the network to process large amounts of data quickly. Deep RL combines the problem-solving abilities of reinforcement learning with the ability to find trends in large datasets quickly, creating a capable AI.
In recent years, several prominent AIs trained through deep RL have become more skilled at their chosen tasks than the most skilled humans. These include video games and board games. Though some scientists worry about this trend, arguing that it may remove too many jobs from the economy faster than they can be replaced, others think AI will accelerate the pace of human development and allow individuals to do less work overall.
Background
Neural networks are also known as simulated neural networks and artificial neural networks. They are an important component of the modern machine learning process. Machine learning is a branch of AI and computer science that uses data and algorithms to allow an AI to gradually learn over time. These programs are not taught the correct answer immediately and are not created with any inherent correct answer. Instead, they learn to associate various steps with an intended outcome, gradually making themselves more efficient over time.
Machine learning algorithms have become commonplace in many industries. Computer algorithms are often better at analyzing massive amounts of data for trends than human data scientists. For example, machine learning algorithms analyze the media that individuals consume on various streaming services. From this, they can identify similarities in different types of media and make effective recommendations to individual users.
Machine learning algorithms do not all use the same method to refine results. Classical machine learning involves using human intervention to guide the results of the algorithm. Humans carefully determine what datasets are entered into the algorithm and structure the data in a way that will help refine the algorithm over time. Neural networks use node layers to gradually refine their algorithm. Nodes are artificial neurons that are comprised of an input layer, a hidden layer, and an output layer. All nodes connect to one another within the network, generating more complicated and often more effective results as the network grows. Deep learning neural networks have more than three layers. These networks are responsible for many modern advances in AI, such as speech recognition and computer vision. As neural networks advanced, they became more skilled than humans in several areas. These specialized AIs can beat the most skilled humans at certain games, such as chess, as well as predict the stock market, recognize faces, and analyze social media data. Some nations utilize neural networks for adaptive defensive strategies, helping them maximize efficiency in logistics, patrols, and potential attack and defense patterns.
Overview
Machine learning algorithms are commonly divided into three categories: supervised, unsupervised, and reinforcement learning. Supervised learning algorithms are given continual feedback from data scientists, letting the algorithm know if its conclusion is correct or incorrect. These algorithms grow reliably, but more slowly than scientists would eventually prefer. Unsupervised learning algorithms are given no feedback from data scientists and instead are left to determine whether they reach correct or incorrect conclusions based on their initial programming. These algorithms grow rapidly, as they have an innate ability to refine themselves. However, they may not grow as effectively as data scientists would like them to.
Reinforcement learning algorithms are similar to supervised learning algorithms. Data scientists provide feedback to the algorithm, letting it know if it is moving in the correct direction. However, data scientists do not provide feedback after every input. This makes a reinforcement learning algorithm an attractive algorithmic model for data scientists.
Reinforcement learning functions in the context of environmental states and possible actions in any given state. As the algorithm learns, it randomly explores its environment, testing the various actions that are viable within a current state and building a virtual state-action pair table. When it is created, the algorithm is given some kind of goal state to try to create. After testing all variable actions within a current state, the algorithm will choose an action-state pair that it believes will move it closer to the theoretical goal state. In most cases, data scientists only reinforce the algorithm when it reaches the goal state. Over time, the algorithm learns what types of actions lead to the goal state as it seeks reinforcement from the data scientists. Because algorithms are capable of processing massive amounts of data in small amounts of time, reinforcement learning algorithms can quickly learn new information.
Some reinforcement learning algorithms utilize a process called q-learning, which incorporates a specialized q-value for each state-pair action, indicating an anticipated reward for following any given state path. This allows the algorithm to learn in smaller stages within a given environmental state. Q-learning allows the algorithm a greater level analysis for each state-based action as the algorithm attempts to reach the goal state, eventually resulting in a faster path there.
During the early 2010s, data scientists and researchers began combining deep learning techniques with reinforcement learning algorithms. This led to the creation of increasingly complex reinforcement learning algorithmic networks. These networks were particularly efficient in learning to achieve victory in both board and video games. In 2015, the deep RL network AlphaGo defeated the highest-ranked professional player of the game Go. Similar success was achieved in chess, as well as the video games StarCraft II and Dota-2. As these games are far more complicated than traditional board games, they were considered significant achievements for the field of AI.
Deep learning excels at the analysis of large quantities of data as well as discovering patterns in large quantities of data. However, traditional deep learning struggles with decision-making processes and problem solving. By combining deep learning with reinforcement learning, data scientists have created AI algorithms with the strengths of both archetypes, creating algorithms that can be fed large quantities of data, analyze it quickly, find patterns, and use those patterns to solve a predefined problem or achieve a desired condition.
As scientists continue to create deep RL neural networks, these networks will become more efficient and effective, continuing to surpass the most skilled humans in the completion of logical tasks, large-scale data analysis, and many other types of problem solving. As this process contributes to the advancement of AI, many scientists worry that AI may be used unethically. They worry that the economy and legal world may be unprepared for the full ramifications of successfully generated AI programs. For example, the creation of safe, efficient, AI-driving programs may eliminate a significant number of jobs from the market. It may be safer and more economical for businesses to install driving software in their freight trucks and transportation vehicles than to hire human drivers who are prone to human errors. If resources are not available for the multitudes of people whose jobs may be eliminated by AI, or additional forms of employment do not become available on the post-AI job market, opponents of the widespread adoption of AI argue that a significant portion of the workforce may be thrust into poverty.
While some researchers fear the impact that AI will have on modern society, others argue that its impact could be positive. AI, when combined with robotics, could remove the need for difficult or unpleasant jobs. It could also help advance important scientific fields, such as computing, engineering, robotics, medicine, and even agriculture. They argue that AI is another tool for humans to use, and that, if used responsibly, could improve the lives of humanity. Some researchers argue that AI could help humanity usher in a post-scarcity society in which resources are produced with such ease and efficiency that all humans’ basic needs are met.
Bibliography
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