Continual Reinforcement Learning in 3D Non-stationary Environments

High-dimensional always-changing environments constitute a hard challenge for current reinforcement learning techniques. Artificial agents, nowadays, are often trained off-line in very static and controlled conditions in simulation such that training observations can be thought as sampled i.i.d. from the entire observations space. However, in real world settings, the environment is often non-stationary and subject to unpredictable, frequent changes. In this paper we propose and openly release CRLMaze, a new benchmark for learning continually through reinforcement in a complex 3D non-stationary task based on ViZDoom and subject to several environmental changes. Then, we introduce an end-to-end model-free continual reinforcement learning strategy showing competitive results with respect to four different baselines and not requiring any access to additional supervised signals, previously encountered environmental conditions or observations.Comment: Accepted in the CLVision Workshop at CVPR2020: 13 pages, 4 figures, 5 table

The genesis of organisational aesthetics

Organisational aesthetics is a burgeoning field with a growing community of scholars engaged in arts-based approaches to research. Recent developments in this field have their origins in the works of early Enlightenment writers such as Vico, Baumgarten and Kant. This paper examines the contributions of these three philosophers and in particular focuses on Vico’s awareness of history and myth; Baumgarten’s notion of sensation and its relationship to rationality; and Kant’s investigations into form and content. By drawing on these ideas, the contemporary aesthetic researcher is informed by qualities such as an alert imagination, comfort with the chaotic, backward thinking, and attention to inner sensations and perceptions, which all work together to provide a coherent view of the organisation as a gestalt

Swift-UVOT Observations of the X-Ray Flash 050406

We present Swift-UVOT data on the optical afterglow of the X-ray flash of 2005 April 6 (XRF 050406) from 88s to \sim 10^5s after the initial prompt gamma-ray emission. Our observations in the V, B and U bands are the earliest that have been taken of an XRF optical counterpart. Combining the early -time optical temporal and spectral properties with \gamma- and simultaneous X-ray data taken with the BAT and XRT telescopes on-board Swift, we are able to constrain possible origins of the XRF. The prompt emission had a FRED profile (fast-rise, exponential decay) with a duration of T_90 = 5.7\pm 0.2s, putting it at the short end of the long-burst duration distribution. The absence of photoelectric absorption red-ward of 4000 \AA in the UV/optical spectrum provides a firm upper limit of z\leq 3.1 on the redshift, thus excluding a high redshift as the sole reason for the soft spectrum. The optical light curve is consistent with a power-law decay with slope alpha = -0.75\pm 0.26 (F_{\nu}\propto t^{\alpha}), and a maximum occurring in the first 200s after the initial gamma-ray emission. The softness of the prompt emission is well described by an off-axis structured jet model, which is able to account for the early peak flux and shallow decay observed in the optical and X-ray bands.Comment: 14 pages, 4 figures, accepted for publication in ApJ; typos corrected and upper limits in table 1 changed from background subtracted count rate in extraction region to the error associated with thi

Attentive Single-Tasking of Multiple Tasks

In this work we address task interference in universal networks by considering that a network is trained on multiple tasks, but performs one task at a time, an approach we refer to as "single-tasking multiple tasks". The network thus modifies its behaviour through task-dependent feature adaptation, or task attention. This gives the network the ability to accentuate the features that are adapted to a task, while shunning irrelevant ones. We further reduce task interference by forcing the task gradients to be statistically indistinguishable through adversarial training, ensuring that the common backbone architecture serving all tasks is not dominated by any of the task-specific gradients. Results in three multi-task dense labelling problems consistently show: (i) a large reduction in the number of parameters while preserving, or even improving performance and (ii) a smooth trade-off between computation and multi-task accuracy. We provide our system's code and pre-trained models at http://vision.ee.ethz.ch/~kmaninis/astmt/.Comment: CVPR 2019 Camera Read