Just in time for summer, Beatbot's pool-cleaning robots are on sale through the end of the month. If you're on the hunt for smarter pool care, these are some of the best pool-cleaning robots on the market, and we haven't seen them sell for less. Whether you're tired of paying the pool guy or just don't want to deal with whatever scary stuff is floating in the water, these robots can help. Be sure to check out our related buying guides for more summer outdoors coverage, including the best bug sprays, the best sunscreens, and the best fitness trackers . This surface skimmer is slow and methodical with its approach, which means it won't slam into your pool's walls while doing its job.

Lidl shoppers say they'll miss monthly freebies. Can bonus points win them over? For 10 years, Lizi Hall has been doing most of her shopping at Lidl - and she's learned how to get the best value from its rewards scheme. We've got it down to a bit of an art, Lizi says. The loyalty system for me really did work.

Insurance apps often collect driving, location and health data in exchange for premium discounts. Adjusting app permissions can help limit what information is shared.

Get ready for summer with discounts on robot pool cleaners from Beatbot. National Pool Opening Day is tomorrow, April 25, and summer is almost here, which means pool owners everywhere are getting ready to unveil the horrors of whatever happened during the off-season. Most of the Beatbot lineup is on sale at Amazon and Beatbot's own storefront, with prices starting at $499. Beatbot makes many of the best pool-cleaning robots we've tested, and we've highlighted our top picks below. Note that the discounts are scheduled to end on April 26, though items may sell out sooner.

It is a variant of shuffle coding that is many orders of magnitude faster than the original and enables'one-shot' compression of single

In this paper, we provide a simple, model-free algorithm for stabilizing fully observed dynamical systems.

Neural Information Processing Systems http://nips.cc/

The original MuZero did not use sticky actions (Machado et al., 2017) (a 25% chance that the selected action is ignored and that instead the previous action is repeated) for Atari experiments. For all experiments in this work we used a network architecture based on the one introduced by MuZero(Schrittwieser etal.,2020), To implement the network, we used the modules provided by the Haiku neural network library (Henniganetal.,2020). We did not observe any benefit from using a Gaussian mixture, so instead inallourexperiments weusedasingle Gaussian withdiagonal covariance. All experiments used the Adam optimiser (Kingma & Ba, 2015) with decoupled weight decay (Loshchilov & Hutter, 2017) for training.