Over the last several years deep learning algorithms have met with dramatic successes across a wide range of application areas. The recently introduced deep Q-learning algorithm represents the first convincing combination of deep learning with reinforcement learning. The algorithm is able to learn policies for Atari 2600 games that approach or exceed human performance. The work presented here i...

The combination of modern reinforcement learning and deep learning approaches brings significant breakthroughs to a variety of domains requiring both rich perception of high-dimensional sensory inputs and policy selection. A recent significant breakthrough in using deep neural networks as function approximators, termed Deep Q-Networks (DQN), proves to be very powerful for solving problems appro...

Reinforcement learning is concerned with learning to interact with environments that are initially unknown. State-of-the-art reinforcement learning approaches, such as DQN, are model-free and learn to act effectively across a wide range of environments such as Atari games, but require huge amounts of data. Modelbased techniques are more data-efficient, but need to acquire explicit knowledge abo...

Spiking neural networks (SNNs) have great potential for energy-efficient implementation of Deep Neural Networks (DNNs) on dedicated neuromorphic hardware. Recent studies demonstrated competitive performance SNNs compared with DNNs image classification tasks, including CIFAR-10 and ImageNet data. The present work focuses using in combination deep reinforcement learning ATARI games, which involve...

We explore the use of Evolution Strategies, a class of black box optimization algorithms, as an alternative to popular RL techniques such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs available: By using hundreds to thousands of parallel workers, ES can solve 3D humanoid walking i...

The combination of modern Reinforcement Learning and Deep Learning approaches holds the promise of making significant progress on challenging applications requiring both rich perception and policy-selection. The Arcade Learning Environment (ALE) provides a set of Atari games that represent a useful benchmark set of such applications. A recent breakthrough in combining model-free reinforcement l...

Virtuelle Gemeinschaften eröffnen im Gesundheitswesen das Potenzial, die Informationsund Kommunikationsbedürfnisse von Krebspatienten zu erfüllen. Nach einem Überblick über das Gesundheitssystem und möglichen Anknüpfungspunkten für virtuelle Gemeinschaften, fokussieren wir uns auf Krebspatienten. Durch eine Analyse ihrer Situation mit Hilfe von Feldstudien versuchen wir die speziellen Informati...

BACKGROUND Ventilator practices in patients at risk for acute lung injury (ALI) and ARDS are unclear. We examined factors associated with choice of set tidal volumes (VT), and whether VT < 8 mL/kg predicted body weight (PBW) relates to the development of ALI/ARDS. METHODS We performed a secondary analysis of a multicenter cohort of adult subjects at risk of lung injury with and without ALI/AR...