Reinforcement Learning

The eight technologies added to the Hype Cycle this year include 5G, Artificial General Intelligence, Deep Learning, Deep Reinforcement Learning, Digital Twin, Edge Computing, Serverless PaaS and Cognitive Computing. Ten technologies not included in the hype cycle for 2017 include 802.11ax, The three most dominant trends include Artifical Intelligence (AI) Everywhere, Transparently Immersive Experiences, and Digital Platforms. Gartner believes that key platform-enabling technologies are 5G, Digital Twin, Edge Computing, Blockchain, IoT Platforms, Neuromorphic Hardware, Quantum Computing, Serverless PaaS and Software-Defined Security. Key takeaways from this year's Hype Cycle include the following:

As you may have gathered, the families of two-class classification, multi-class classification, anomaly detection, and regression are all closely related. Entirely different sets of data science questions belong in the extended algorithm families of unsupervised and reinforcement learning. Another family of unsupervised learning algorithms are called dimensionality reduction techniques. These are called reinforcement learning (RL) algorithms.

According to Gartner's 2017 Hype Cycle for Emerging Technologies, augmented reality and virtual reality have begun to climb the'plateau of productivity' as they approach mainstream adoption. Technologies such as deep learning, autonomous learning and cognitive computing are just crossing the peak. Enterprises that are seeking leverage in this theme should consider the following technologies: deep learning, deep reinforcement learning, artificial general intelligence, autonomous vehicles, cognitive computing, commercial UAVs (drones), conversational user interfaces, enterprise taxonomy and ontology management, machine learning, smart dust, smart robots and smart workspace. "In addition to the potential impact on businesses, these trends provide a significant opportunity for enterprise architecture leaders to help senior business and IT leaders respond to the digital business opportunities and threats by creating signature-ready actionable and diagnostic deliverables that guide investment decisions."

Sitting inside OpenAI's San Francisco offices on a recent afternoon, the researcher Dario Amodei showed off an autonomous system that taught itself to play Coast Runners, an old boat-racing video game. Many specialists in the AI field believe a technique called reinforcement learning -- a way for machines to learn specific tasks through extreme trial and error -- could be a primary path to artificial intelligence. All this is why Amodei and Christiano are working to build reinforcement learning algorithms that accept human guidance along the way. Researchers like Google's Ian Goodfellow, for example, are exploring ways that hackers could fool AI systems into seeing things that aren't there.

From this large base of technologies, the technologies that show the most potential for delivering a competitive advantage over the next five to 10 years are included in the Hype Cycle. The eight technologies added to the Hype Cycle this year include 5G, Artificial General Intelligence, Deep Learning, Deep Reinforcement Learning, Digital Twin, Edge Computing, Serverless PaaS and Cognitive Computing. Ten technologies not included in the hype cycle for 2017 include 802.11ax, The three most dominant trends include Artifical Intelligence (AI) Everywhere, Transparently Immersive Experiences, and Digital Platforms. Gartner believes that key platform-enabling technologies are 5G, Digital Twin, Edge Computing, Blockchain, IoT Platforms, Neuromorphic Hardware, Quantum Computing, Serverless PaaS and Software-Defined Security.

Hello, I'm looking for some advice on school choices for someone from a non-traditional background (undergrad and current master in chemical engineering, focused on controls) for getting into the ML field. Currently doing 1st year of 2 in Master in chemical engineering, my research topic is applying reinforcement learning to optimal control problems in smart grid energy management/demand-side management. Continue a PhD in chem eng, focused on controls, continue working on RL related research. I guess I'm curious as to how viable the first option is, as in how "employable" it is for internships for a PhD from a non-traditional chemical engineering background, but with research in related ML/RL area.

To allow for greater flexibility, I will then describe how to build a class of reinforcement learning agents, which can optimize for various goals called "direct future prediction" (DFP). Reinforcement learning involves agents interacting in some environment to maximize obtained rewards over time. Q-learning and other traditionally formulated reinforcement learning algorithms learn a single reward signal, and as such, can only pursue a single "goal" at a time. If we want our drone to learn to deliver packages, we simply provide a positive reward of 1 for successfully flying to a marked location and making a delivery.

With the rapid increases in computing power, it's easy to get seduced into thinking that raw computing power can solve problems like smart edge devices (e.g., cars, trains, airplanes, wind turbines, jet engines, medical devices). In chess, the complexity of the chess piece only increases slightly (rooks can move forward and sideways a variable number of spaces, bishops can move diagonally a variable number of spaces, etc. Now think about the number and breadth of "moves" or variables that need to be considered when driving a car in a nondeterministic (random) environment: weather (precipitation, snow, ice, black ice, wind), time of day (day time, twilight, night time, sun rise, sun set), road conditions (pot holes, bumpy, slick), traffic conditions (number of vehicles, types of vehicles, different speeds, different destinations). It's nearly impossible for an autonomous car manufacturer to operate enough vehicles in enough different situations to generate the amount of data that can be virtually gathered by playing against Grand Theft Auto.

Two Minute Papers reviews how DeepMind learned how to play the Atari classic "Break Out" simply by observing the game being played from a video feed. The original paper "Imagination-Augmented Agents for Deep Reinforcement Learning" is online at https://arxiv.org/abs/1707.06203

In an upcoming screencast I'm doing with O'Reilly I'll be discussing what reinforcement learning is and how it applies. I figured I'd give you all a little behind the scenes look. Here's a quick intro in 8 slides: