We’re releasing Spinning Up in Deep RL, an educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning. Spinning Up consists of crystal-clear examples of RL code, educational exercises, documentation, and tutorials.
We’ve developed an energy-based model that can quickly learn to identify and generate instances of concepts, such as near, above, between, closest, and furthest, expressed as sets of 2d points. Our model learns these concepts after only five demonstrations. We also show cross-domain transfer: we use concepts learned in a 2d particle environment to solve tasks on a 3-dimensional physics-based robot.
Introduction This paper was published in CCS 2015. With the introduction of the various mitigation deployed in the user space, especially sandbox, the vulnerability in the linux kernel has become a target of the attacker. However, the memory space in the kernel is hard to predict since the kernel space are used by various tasks. […]