Security researchers found that in less than 60 seconds, they could open a hatch on a plane's exterior, plug in a tiny device, and redirect the aircraft's autopilot or sabotage its flight plan. Even as the digital components of so many life-critical systems have proven susceptible to cybersabotage-- cars, medical devices, even water utilities and power grids --the computer systems of airplanes have, thankfully, remained uniquely inaccessible to hackers. But one group of academic researchers has spent years testing a different, devious approach to aviation cybersecurity. Perhaps, they suggest, a plane could be hacked the same way that spies and saboteurs have targeted other high-value, offline computers: by surreptitiously gaining physical access to one and plugging in a device designed to silently run the attackers' malicious code. Tomorrow at the Usenix Cybersecurity Conference, researchers from the University of California at San Diego and Oberlin College will present a hacking technique capable of commandeering the autopilot of a Boeing 737 to redirect its navigation or silently altering key values in the plane's takeoff and fuel calculations while spoofing the results on the pilot's screen--subtle changes the researchers say could potentially cause anything from runway overruns on takeoff to diversions to a different country's airspace to catastrophic crashes.

This sampling process is embodied as the policyπ, which is responsible for outputting an actiona conditioned on past environmental states{s}.

Lemma 2. Let Assumption 1 hold.

Temporal Difference methods inRL use bootstrapping to address this issue but often struggle when the time horizons are long and the reward is sparse.

A natural generalization is to replace this input setP of n points by a setP of n sets inX. The distance from such an input setP P to a setC of centers can then be defined as the distance between the closest point-center pair. This problem is calledk-mean for sets; see e.g.

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

In the reinforcement learning context, anOption means a temporally extended sequence of actions [30],andisregarded asuseful formanypurposes, such asspeeding uplearning, transferring skills across domains, and solving long-term planning problems.

Accurate prediction of likely 3D geometries of flexiblemolecules is along standing goal of computational chemistry, with broad implications for drug design, biopolymer research, and QSAR analysis.

We provide a novel method for lifelong policy gradient learning that trains lifelong function approximators directly via policygradients, allowing the agent to benefit from accumulated knowledge throughout the entire training process.