reinforcement learning


Artificial Intelligence: Reinforcement Learning in Python

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Facebook has trained an AI to navigate without needing a map

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The algorithm lets robots find the shortest route in unfamiliar environments, opening the door to robots that can work inside homes and offices. The news: A team at Facebook AI has created a reinforcement learning algorithm that lets a robot find its way in an unfamiliar environment without using a map. Using just a depth-sensing camera, GPS, and compass data, the algorithm gets a robot to its goal 99.9% of the time along a route that is very close to the shortest possible path, which means no wrong turns, no backtracking, and no exploration. This is a big improvement over previous best efforts. Why it matters: Mapless route-finding is essential for next-gen robots like autonomous delivery drones or robots that work inside homes and offices.


Roberto G.E. Martín on LinkedIn: #AI #ReinforcementLearning

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Using just a depth-sensing camera, GPS, and compass data, the AI Agent gets its goal 99.9% of the time along a route that is very close to the shortest possible path, which means no wrong turns, no backtracking, and no exploration.


Facebook AI gives maps the brushoff in helping robots find the way

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Facebook has scored an impressive feat involving AI that can navigate without any map. Facebook's wish for bragging rights, although they said they have a way to go, were evident in its blog post, "Near-perfect point-goal navigation from 2.5 billion frames of experience." Long story short, Facebook has delivered an algorithm that, quoting MIT Technology Review, lets robots find the shortest route in unfamiliar environments, opening the door to robots that can work inside homes and offices." And, in line with the plain-and-simple, Ubergizmo's Tyler Lee also remarked: "Facebook believes that with this new algorithm, it will be capable of creating robots that can navigate an area without the need for maps...in theory, you could place a robot in a room or an area without a map and it should be able to find its way to its destination." Erik Wijmans and Abhishek Kadian in the Facebook Jan. 21 post said that, well, after all, one of the technology key challenges is "teaching these systems to navigate through complex, unfamiliar real-world environments to reach a specified destination--without a preprovided map." Facebook has taken on the challenge. The two announced that Facebook AI created a large-scale distributed reinforcement learning algorithm called DD-PPO, "which has effectively solved the task of point-goal navigation using only an RGB-D camera, GPS, and compass data," they wrote. DD-PPO stands for decentralized distributed proximal policy optimization. This is what Facebook is using to train agents and results seen in virtual environments such as houses and office buildings were encouraging. The bloggers pointed out that "even failing 1 out of 100 times is not acceptable in the physical world, where a robot agent might damage itself or its surroundings by making an error." Beyond DD-PPO, the authors gave credit to Facebook AI's open source AI Habitat platform for its "state-of-the-art speed and fidelity." AI Habitat made its open source announcement last year as a simulation platform to train embodied agents such as virtual robots in photo-realistic 3-D environments. Facebook said it was part of "Facebook AI's ongoing effort to create systems that are less reliant on large annotated data sets used for supervised training." InfoQ had said in July that "The technology was taking a different approach than relying upon static data sets which other researchers have traditionally used and that Facebook decided to open-source this technology to move this subfield forward." Jon Fingas in Engadget looked at how the team worked toward AI navigation (and this is where that 25 billion number comes in). "Previous projects tend to struggle without massive computational power.


RLgraph: Modular Computation Graphs for Deep Reinforcement Learning

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Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communication patterns. We argue for the separation of logical component composition, backend graph definition, and distributed execution. To this end, we introduce RLgraph, a library for designing and executing reinforcement learning tasks in both static graph and define-by-run paradigms. The resulting implementations are robust, incrementally testable, and yield high performance across different deep learning frameworks and distributed backends.


How may quantum computing affect Artificial Intelligence?

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The processing power required to extract value from the unmanageable swaths of data currently being collected, and especially to apply artificial intelligence techniques such as machine learning, keeps increasing. Researchers have been trying to figure out a way to expedite these processes applying quantum computing algorithms to artificial intelligence techniques, giving rise in the process to a new discipline that's been dubbed Quantum Machine Learning (QML). The race to make good on quantum computing is well underway. Millions of dollars have been allocated to developing machines that could cause current computers to become obsolete. But, what is the difference between quantum and classical computing?


Complete Intelligence Superforecasting Streamlined

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The Complete Intelligence Global Cognitive System (GCS) was developed using Basic AI in 2015, and subsequently moved to a ML environment in 2016. In 2018, we expanded our analytic processes to harness the power of Deep Learning. At present, we are moving forward into the area of Reinforcement Learning to further improve our predictive efficiency. We begin our analytics with one of the world's largest global trade models that looks at more than 1,400 different industries and over 100 reporting countries. This is combined with thousands of commodities, equity indices, currencies and economic indicators to create a comprehensive model.


What are Important AI & Machine Learning Trends for 2020?

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Companies ranging from high tech startups to global multinationals see artificial intelligence as a key competitive advantage in an increasingly competitive and technical market. But, the AI industry moves so quickly that it's often hard to follow the latest research breakthroughs and achievements, and even harder to apply scientific results to achieve business outcomes. To help you develop a robust AI strategy for your business in 2020, I've summarized the latest trends across different research areas, including natural language processing, conversational AI, computer vision, and reinforcement learning. I've also included external education you can follow to further your expertise. In 2018, pre-trained language models pushed the limits of natural language understanding and generation.


New Research Hints at How Your Smiles Could One Day Teach Artificial Intelligence

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We live in an era when humans are busy training a new intelligence on this planet. Every once in a while, researchers come up with a novel way to speed up that teaching process. That's what happened at Microsoft Research where computer scientists recently developed a new approach to using human emotion to train machines how to learn.[i] The research used virtual agents to facilitate learning various tasks in a simulated environment. What is most significant about this research is that it trained those agents by exposing them to the smiles of human subjects as they interacted with the system.


Microsoft Introduces Project Petridish to Find the Best Neural Network for your Problem

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Neural architecture search(NAS) is one of the hottest trends in modern deep learning technologies. Conceptually, NAS methods focus on finding a suitable neural network architecture for a given problem and dataset. Think about it as making machine learning architecture a machine learning problem by itself. In recent years, there have been an explosion in the number of NAS techniques that are making inroads into mainstream deep learning frameworks and platforms. However, the first generation of NAS models have encountered plenty of challenges adapting neural networks that were tested on one domain to another domain.