Deep Ordinal Reinforcement Learning

Reinforcement learning usually makes use of numerical rewards, which have nice properties but also come with drawbacks and difficulties. Using rewards on an ordinal scale (ordinal rewards) is an alternative to numerical rewards that has received more attention in recent years. In this paper, a general approach to adapting reinforcement learning problems to the use of ordinal rewards is presented and motivated. We show how to convert common reinforcement learning algorithms to an ordinal variation by the example of Q-learning and introduce Ordinal Deep Q-Networks, which adapt deep reinforcement learning to ordinal rewards. Additionally, we run evaluations on problems provided by the OpenAI Gym framework, showing that our ordinal variants exhibit a performance that is comparable to the numerical variations for a number of problems. We also give first evidence that our ordinal variant is able to produce better results for problems with less engineered and simpler-to-design reward signals.Comment: replaced figures for better visibility, added github repository, more details about source of experimental results, updated target value calculation for standard and ordinal Deep Q-Networ

Crawling in Rogue's dungeons with (partitioned) A3C

Rogue is a famous dungeon-crawling video-game of the 80ies, the ancestor of its gender. Rogue-like games are known for the necessity to explore partially observable and always different randomly-generated labyrinths, preventing any form of level replay. As such, they serve as a very natural and challenging task for reinforcement learning, requiring the acquisition of complex, non-reactive behaviors involving memory and planning. In this article we show how, exploiting a version of A3C partitioned on different situations, the agent is able to reach the stairs and descend to the next level in 98% of cases.Comment: Accepted at the Fourth International Conference on Machine Learning, Optimization, and Data Science (LOD 2018

The Dreaming Variational Autoencoder for Reinforcement Learning Environments

Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.Comment: Best Student Paper Award, Proceedings of the 38th SGAI International Conference on Artificial Intelligence, Cambridge, UK, 2018, Artificial Intelligence XXXV, 201

Systematic review and meta-analysis. small intestinal bacterial overgrowth in chronic pancreatitis

BACKGROUND: Evidence on small intestinal bacterial overgrowth (SIBO) in patients with chronic pancreatitis (CP) is conflicting. AIM: The purpose of this study was to perform a systematic review and meta-analysis on the prevalence of SIBO in CP and to examine the relationship of SIBO with symptoms and nutritional status. METHODS: Case-control and cross-sectional studies investigating SIBO in CP patients were analysed. The prevalence of positive tests was pooled across studies, and the rate of positivity between CP cases and controls was calculated. RESULTS: In nine studies containing 336 CP patients, the pooled prevalence of SIBO was 36% (95% confidence interval (CI) 17-60%) with considerable heterogeneity (I2 = 91%). A sensitivity analysis excluding studies employing lactulose breath test gave a pooled prevalence of 21.7% (95% CI 12.7-34.5%) with lower heterogeneity (I2 = 56%). The odds ratio for a positive test in CP vs controls was 4.1 (95% CI 1.6-10.4) (I2 = 59.7%). The relationship between symptoms and SIBO in CP patients varied across studies, and the treatment of SIBO was associated with clinical improvement. CONCLUSIONS: One-third of CP patients have SIBO, with a significantly increased risk over controls, although results are heterogeneous, and studies carry several limitations. The impact of SIBO and its treatment in CP patients deserve further investigation

Visualising gas heating from an RF plasma loudspeaker

In an electro-acoustic transduction mechanism, an ac modulation (here in the audio frequency range) of the electric field in an atmospheric pressure air plasma gives rise to a rapid increase in the gas temperature and dimensions of the gas volume. As in natural lightning, the rapid expansion in the ionised column though the air produces external pressure variations at the modulation frequency.  \ud Spatial and temporal measurement of the gas temperature can identify the nature of the thermal expansion and provide a direct approach to understanding its relationship to the sound pressure wave that is generated. However, the established method through spectroscopic measurement of rotational line emission from nitrogen molecules is limited to the main current channel where relaxation and subsequent optical emission of the excited nitrogen molecules occurs. The wider picture is revealed through the use of the Schlieren method where the refractive index gradients caused by gas heating in the plasma are imaged