Radio Observations of New Galactic Bulge Planetary Nebulae

We observed 64 newly identified galactic bulge planetary nebulae in the radio continuum at 3 and 6 cm with the Australia Telescope Compact Array. We present their radio images, positions, flux densities, and angular sizes. The survey appears to have detected a larger ratio of more extended planetary nebulae with low surface brightness than in previous surveys. We calculated their distances according to Van de Steene & Zijlstra (1995). We find that most of the new sample is located on the near side around the galactic center and closer in than the previously known bulge PNe. Based on H-alpha images and spectroscopic data, we calculated the total H-alpha flux. We compare this flux value with the radio flux density and derive the extinction. We confirm that the distribution of the extinction values around the galactic center rises toward the center, as expected.Comment: accepted for publication in A&

Reusable Options through Gradient-based Meta Learning

Hierarchical methods in reinforcement learning have the potential to reduce the amount of decisions that the agent needs to perform when learning new tasks. However, finding a reusable useful temporal abstractions that facilitate fast learning remains a challenging problem. Recently, several deep learning approaches were proposed to learn such temporal abstractions in the form of options in an end-to-end manner. In this work, we point out several shortcomings of these methods and discuss their potential negative consequences. Subsequently, we formulate the desiderata for reusable options and use these to frame the problem of learning options as a gradient-based meta-learning problem. This allows us to formulate an objective that explicitly incentivizes options which allow a higher-level decision maker to adjust in few steps to different tasks. Experimentally, we show that our method is able to learn transferable components which accelerate learning and performs better than existing prior methods developed for this setting. Additionally, we perform ablations to quantify the impact of using gradient-based meta-learning as well as other proposed changes