1,780 research outputs found
The Dominant Eigenvalue of an Essentially Nonnegative Tensor
It is well known that the dominant eigenvalue of a real essentially
nonnegative matrix is a convex function of its diagonal entries. This convexity
is of practical importance in population biology, graph theory, demography,
analytic hierarchy process and so on. In this paper, the concept of essentially
nonnegativity is extended from matrices to higher order tensors, and the
convexity and log convexity of dominant eigenvalues for such a class of tensors
are established. Particularly, for any nonnegative tensor, the spectral radius
turns out to be the dominant eigenvalue and hence possesses these convexities.
Finally, an algorithm is given to calculate the dominant eigenvalue, and
numerical results are reported to show the effectiveness of the proposed
algorithm
Virtual to Real Reinforcement Learning for Autonomous Driving
Reinforcement learning is considered as a promising direction for driving
policy learning. However, training autonomous driving vehicle with
reinforcement learning in real environment involves non-affordable
trial-and-error. It is more desirable to first train in a virtual environment
and then transfer to the real environment. In this paper, we propose a novel
realistic translation network to make model trained in virtual environment be
workable in real world. The proposed network can convert non-realistic virtual
image input into a realistic one with similar scene structure. Given realistic
frames as input, driving policy trained by reinforcement learning can nicely
adapt to real world driving. Experiments show that our proposed virtual to real
(VR) reinforcement learning (RL) works pretty well. To our knowledge, this is
the first successful case of driving policy trained by reinforcement learning
that can adapt to real world driving data
- …