it seems our competitors with their learning algorithms have to...

it seems our competitors with their learning algorithms have to go a long way still as they cant even navigate through the merger traffic conditions may be they should contact BRN as they can help them !!!


But the setting where you will probably most notice this software’s remarkably human like behavior is in self-driving cars. Today’s driverless vehicles often falter in complex situations that involve interacting with human drivers, such as traffic circles or four-way stops. If we don’t want them to take unnecessary risks, or to clog the roads by being overly hesitant, they will need to acquire more nuanced driving skills, like jostling for position in a crowd of other cars.
The highway merging software was demoed in Barcelona by Mobileye, an Israeli automotive company that makes vehicle safety systems used by dozens of car makers, including Tesla Motors (see “50 Smartest Companies 2016”). After screening the merging clip, Shai Shalev-Shwartz, Mobileye’s vice president for technology, shows some of the challenges self-driving cars will face: a bustling roundabout in Jerusalem; a frenetic intersection in Paris; and a hellishly chaotic scene from a road in India. “If a self-driving car follows the law precisely, then during rush hour I might wait in a merge situation for an hour,” Shalev-Shwartz says.
Mobileye plans to test the software on a fleet of vehicles in collaboration with BMW and Intel later this year. Both Google and Uber say they are also testing reinforcement learning for their self-driving vehicles.

Reinforcement learning led to AlphaGo’s stunning victory over a human Go champion last year.
Reinforcement learning is being applied in a growing number of areas, says Emma Brunskill, an assistant professor at Stanford University who specializes in the approach. But she says it is well suited to automated driving because it enables “good sequences of decisions.” Progress would proceed much more slowly if programmers had to encode all such decisions into cars in advance.
But there are challenges to overcome, too. Andrew Ng, chief scientist at the Chinese company Baidu, warns that the approach requires a huge amount of data, and that many of its successes have come when a computer could practice relentlessly in simulations. Indeed, researchers are still figuring out just how to make reinforcement learning work in complex situations in which there is more than one objective. Mobileye has had to tweak its protocols so a self-driving car that is adept at avoiding accidents won’t be more likely to cause one for someone else.
When you watch the outlandish merging demo, it looks as though the company has succeeded, at least so far. But later this year, perhaps on a highway near you, reinforcement learning will get its most dramatic and important tests to date.
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