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Author: torontoai

[P] Full Chainer Implementation OpenAI Random Network Distillation

I released my implementation of OpenAI’s Reinforcement Learning using Random Network Distillation. The implementation is fairly complete, done almost exactly as laid out in the paper. Check it out at https://github.com/AdeelMufti/RL-RND.

Interestingly, I tried it on PLE’s PixelCopter where I turned off the extrinsic rewards altogether, and it got roughly the same results with the extrinsic rewards. I wrote about it here: http://blog.adeel.io/2019/04/13/reinforcement-learning-using-intrinsic-rewards-through-random-network-distillation-in-chainer/

Someone with a free GPU sitting around mind spinning it up in Montezuma’s Revenge for a while? I’m curious to see what this implementation will achieve.

submitted by /u/ThisIsMySeudonym
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Dig In: Startup Tills Satellite Data to Harvest Farm AI

The farm-to-fork movement is getting a taste of AI.

Startup OneSoil cultivates AI to help farmers boost their bounty. The company offers a GPU-enabled platform that turns satellite data into farm analytics for soil and crop conditions.

The Belarus-based company interprets satellite feeds to show how plants reflect different light waves, and it rates the state of plant growth based off this information for plots of land.

OneSoil’s free platform displays how areas of land measure up on the standard known as the NDVI (Normalized Difference Vegetation Index). Farmers can use this vegetation score to spot unhealthy crop areas that need inspection and to plan watering needs and the application of fertilizers.

The field monitoring platform is available as an Android app and on the web.

OneSoil has developed its platform to cover North America, most of western Europe and some of central Europe.  It aims to have coverage of the entire world by year’s end. The satellite data visualizations are updated every three to five days.

Satellite to Sprouts

OneSoil taps into free satellite data from the European Union’s Copernicus Earth observation program. The company manually marked out boundaries on nearly 400,000 fields for training data used on its convolutional neural networks. Now its algorithms can now enable its algorithms to automatically create boundaries from the satellite data.

It processed about 50 terabytes of Sentinel 2 satellite data using NVIDIA GPUs in Microsoft Azure to build out its boundaries of land for the map spanning much of the world.

“With Sentinel images, we need a lot of processing power to analyze those,” said Clement Matyuhov, director of business development at OneSoil.

OneSoil can automatically detect more than 20 different crop types.

Dig the Sensors

OneSoil has developed sensors to work on its platform. Customer can dig a hole and stick in one of its battery-powered sensors that packs a SIM card to start sending data.

The sensors measure air humidity, soil moisture, the temperature of air and soil, and the level of light intensity for the nearby area.

The company has also developed a modem that can transfer data between agricultural equipment and the OneSoil platform over a mobile network.

OneSoil users can enter data, as well. They can make such entries as date of harvest, crop type, average yield, field boundaries and files documenting field work. They can use the app, which tracks location and provides field data, to go examine areas.

Prescriptive Agriculture

On the analytics side, OneSoil Maps makes it easy for farmers to make adjustments on their land. The maps provide a productivity rating of low, medium or high for different areas of the land.

“We can say there is a low productivity zone there, so go check it out. Within one field, the productivity can vary dramatically,” said Matyuhov.

Farmers can use the maps for the vegetation on their land to create prescription maps for fertilizer. These prescription maps, downloadable as a file from app.onesoil.ai, can be uploaded into compatible tractors from John Deere and steering systems from Trimble, allowing tractors to go to the specific GPS coordinates and treat the area as prescribed.

“It’s really an expert assessment for the farmer. The results for the yield can be substantial,” he

Image and credit: Corn harvest with an IHC International combine harvester, Jones County, Iowa, U.S., by Bill Whittaker under Creative Commons license.

The post Dig In: Startup Tills Satellite Data to Harvest Farm AI appeared first on The Official NVIDIA Blog.

[D] Any Papers that criticize Deep Reinforcement Learning?

Is anybody aware of literature that criticizes Deep Reinforcement Learning? I sometimes see a few points mentioned in some papers’ introductions about how data-hungry Deep RL is, how it is not applicable in the real world, and that there’s nothing human-like about it. But I’m not aware of any papers that criticize Deep Reinforcement Learning heavily.

Any help is appreciated.

Thanks!

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[D] Preparing a deep learning course for computer vision

I am a PhD student in a small computer vision group at our university. I am the only person in the team with experience in deep learning and I initiated a creation of a new course for our students which should cover deep learning approaches to computer vision. The structure of the lectures will probably be very similar to Standford CS231n course. I won’t be able to be the lecturer since this requires a PhD, but it is my responsibility to prepare lab exercises for the students. Since deep learning is computationally expensive I hope to use some cloud service which offers free credits to academia for educational purposes. I was thinking about using Google cloud. However, I feel that this area is developing so fast that it would be valuable to ask for some tips regarding this, especially since creating a new course takes so much time and committing to a wrong platform or choosing a bad approach might lead to many lost hours.

tldr: I am preparing lab exercises for deep learning in computer vision course. Which cloud service (or other approach) should the course use for lab exercises?

submitted by /u/finite-difference
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[P] js-gym: JavaScript environment for training reinforcement learning agents.

js-gym

JavaScript environment for training reinforcement learning agents.

They created an environment that wraps over a few JS games for DeepRL (MK and tetris examples included). Can visualize over the browser on localhost port 8000

Sample algorithms

  1. Random Play
  2. Random Search
  3. HillClimbing
  4. Augmented Random Search
  5. Deep Deterministic Policy Gradient

https://github.com/bobiblazeski/js-gym

submitted by /u/milaworld
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