"Reinforcement learning is learning what to do – how to map situations to actions – so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them."
– Sutton, Richard S. and Andrew G. Barto. Reinforcement Learning: An Introduction. (1.1). MIT Press, Cambridge, MA, 1998.
My aim, as always, was to keep the projects as diverse as possible so you can pick the ones that fit into your data science journey. If you're a beginner, I would suggest starting with the PalmerPenguins dataset as most folks aren't even aware of it right now. A great chance to get a head start. I would love to hear your thoughts on which open source project you found the most useful. Or let me know if you want me to feature any other data science projects here or in next month's edition.
Jeff Clune is the former Loy and Edith Harris Associate Professor in Computer Science at the University of Wyoming, a Senior Research Manager and founding member of Uber AI Labs, and currently a Research Team Leader at OpenAI. Jeff focuses on robotics and training neural networks via deep learning and deep reinforcement learning. He has also researched open questions in evolutionary biology using computational models of evolution, including studying the evolutionary origins of modularity, hierarchy, and evolvability. Prior to becoming a professor, he was a Research Scientist at Cornell University, received a PhD in computer science and an MA in philosophy from Michigan State University, and received a BA in philosophy from the University of Michigan. More about Jeff's research can be found at JeffClune.com
Artificial Intelligence: Reinforcement Learning in Python 4.5 (7,241 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing – playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible.
Personalized marketing for retail consumers and account-based marketing for B2B customers now have proven value. Online interactions with customers generate large volumes of data for granular learning about consumer behavior for customization of product recommendations, messages, and content. The missing piece is a scalable and just-in-time way to gauge customer preferences and make product recommendations while visitors engage with websites. Deep reinforcement learning algorithms have been trained at the threshold level where they begin to achieve conversion rates to match the costs of data analysis. The touchstone of reinforcement learning (RL) is that it experiments with multiple pathways to achieve the objective of acquiring customers or any other goal.
Robots have been useful in environments that can be carefully controlled, such as those commonly found in industrial settings (e.g. assembly lines). However, in unstructured settings like the home, we need robotic systems that are adaptive to the diversity of the real world. Learning-based algorithms have the potential to enable robots to acquire complex behaviors adaptively in unstructured environments, by leveraging data collected from the environment. In particular, with reinforcement learning, robots learn novel behaviors through trial and error interactions. This is particularly important as we deploy robots in scenarios where the environment may not be known.
The brain of a human child is spectacularly amazing. Even in any previously unknown situation, the brain makes a decision based on its primal knowledge. Depending on the outcome, it learns and remembers the most optimal choices to be taken in that particular scenario. On a high level, this process of learning can be understood as a ’trial and error’ process, where the brain tries to maximise the occurrence of positive outcomes.
This is the second part of an article discussing new areas of game theory that are influencing deep reinforcement learning systems. The first part focused on types of games that we are actively seeing in multi-agent reinforcement learning systems. Today, I would like to cover three new areas of deep learning theory that can influence new generations of reinforcement learning systems. Game theory plays a fundamental factor in modern artificial intelligence(AI) solutions. Specifically, deep reinforcement learning(DRL) is an area of AI that embraced game theory as a first-class citize.
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Network science is an academic field that aims to unveil the structure and dynamics behind networks, such as telecommunication, computer, biological and social networks. One of the fundamental problems that network scientists have been trying to solve in recent years entails identifying an optimal set of nodes that most influence a network's functionality, referred to as key players. Identifying key players could greatly benefit many real-world applications, for instance, enhancing techniques for the immunization of networks, as well as aiding epidemic control, drug design and viral marketing. Due to its NP-hard nature, however, solving this problem using exact algorithms with polynomial time complexity has proved highly challenging. Researchers at National University of Defense Technology in China, University of California, Los Angeles (UCLA), and Harvard Medical School (HMS) have recently developed a deep reinforcement learning (DRL) framework, dubbed FINDER, that could identify key players in complex networks more efficiently.