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View article: Model-Based Reinforcement Learning under Random Observation Delays
Model-Based Reinforcement Learning under Random Observation Delays Open
Delays frequently occur in real-world environments, yet standard reinforcement learning (RL) algorithms often assume instantaneous perception of the environment. We study random sensor delays in POMDPs, where observations may arrive out-of…
View article: Novel Strategies and Therapeutic Advances for Bladder Cancer
Novel Strategies and Therapeutic Advances for Bladder Cancer Open
Background/Objectives: To summarize the relevant trials relating to novel strategies and therapeutic advances in the treatment of bladder cancer. Methods: A comprehensive review of the literature and recent/ongoing clinical trials was cond…
View article: Make the Pertinent Salient: Task-Relevant Reconstruction for Visual Control with Distractions
Make the Pertinent Salient: Task-Relevant Reconstruction for Visual Control with Distractions Open
Recent advancements in Model-Based Reinforcement Learning (MBRL) have made it a powerful tool for visual control tasks. Despite improved data efficiency, it remains challenging to train MBRL agents with generalizable perception. Training i…
View article: Characterisation of pomegranate juice polyphenols identifies oenothein B as an endothelium-dependent vasodilator and regulator of endothelial function that enhances actions of oligomeric procyanidins
Characterisation of pomegranate juice polyphenols identifies oenothein B as an endothelium-dependent vasodilator and regulator of endothelial function that enhances actions of oligomeric procyanidins Open
Background Cardiovascular disease mortality is reduced in individuals with high daily consumption of dietary polyphenols. Optimal daily intakes, choice of polyphenol, and mechanism of action have yet to be fully defined. Pomegranate juice …
View article: Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors
Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors Open
Large language models (LLMs) are being applied as actors for sequential decision making tasks in domains such as robotics and games, utilizing their general world knowledge and planning abilities. However, previous work does little to expl…
View article: Niclosamide does not modulate airway epithelial function through blocking of the calcium activated chloride channel, TMEM16A
Niclosamide does not modulate airway epithelial function through blocking of the calcium activated chloride channel, TMEM16A Open
Niclosamide and benzbromarone have been described as inhibitors of the calcium activated chloride channel, TMEM16A, and on this basis have been considered and tested as clinical candidates for the treatment of airway diseases. However, bot…
View article: Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling Open
Reinforcement learning (RL) agents typically learn tabula rasa, without prior knowledge of the world. However, if initialized with knowledge of high-level subgoals and transitions between subgoals, RL agents could utilize this Abstract Wor…
View article: Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks Open
In temporal-difference reinforcement learning algorithms, variance in value estimation can cause instability and overestimation of the maximal target value. Many algorithms have been proposed to reduce overestimation, including several rec…
View article: Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments Open
Robust reinforcement learning (RL) considers the problem of learning policies that perform well in the worst case among a set of possible environment parameter values. In real-world environments, choosing the set of possible values for rob…
View article: Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Games
Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Games Open
In competitive two-agent environments, deep reinforcement learning (RL) methods based on the \emph{Double Oracle (DO)} algorithm, such as \emph{Policy Space Response Oracles (PSRO)} and \emph{Anytime PSRO (APSRO)}, iteratively add RL best …
View article: Learning to Query Internet Text for Informing Reinforcement Learning Agents
Learning to Query Internet Text for Informing Reinforcement Learning Agents Open
Generalization to out of distribution tasks in reinforcement learning is a challenging problem. One successful approach improves generalization by conditioning policies on task or environment descriptions that provide information about the…
View article: Anytime PSRO for Two-Player Zero-Sum Games
Anytime PSRO for Two-Player Zero-Sum Games Open
Policy space response oracles (PSRO) is a multi-agent reinforcement learning algorithm that has achieved state-of-the-art performance in very large two-player zero-sum games. PSRO is based on the tabular double oracle (DO) method, an algor…
View article: Target Entropy Annealing for Discrete Soft Actor-Critic
Target Entropy Annealing for Discrete Soft Actor-Critic Open
Soft Actor-Critic (SAC) is considered the state-of-the-art algorithm in continuous action space settings. It uses the maximum entropy framework for efficiency and stability, and applies a heuristic temperature Lagrange term to tune the tem…
View article: Independent Natural Policy Gradient Always Converges in Markov Potential\n Games
Independent Natural Policy Gradient Always Converges in Markov Potential\n Games Open
Multi-agent reinforcement learning has been successfully applied to\nfully-cooperative and fully-competitive environments, but little is currently\nknown about mixed cooperative/competitive environments. In this paper, we focus\non a parti…
View article: Potentiating TMEM16A does not stimulate airway mucus secretion or bronchial and pulmonary arterial smooth muscle contraction
Potentiating TMEM16A does not stimulate airway mucus secretion or bronchial and pulmonary arterial smooth muscle contraction Open
The calcium‐activated chloride channel (CaCC) TMEM16A enables chloride secretion across several transporting epithelia, including in the airways. Additional roles for TMEM16A have been proposed, which include regulating mucus production an…
View article: Potentiating TMEM16A channel function has no effect on airway goblet cells or bronchial and pulmonary vascular smooth muscle function
Potentiating TMEM16A channel function has no effect on airway goblet cells or bronchial and pulmonary vascular smooth muscle function Open
The calcium-activated chloride channel TMEM16A enables chloride secretion across several transporting epithelia, including in the airway where it represents a therapeutic target for the treatment of cystic fibrosis. Additional roles for TM…
View article: TMEM16A Potentiation: A Novel Therapeutic Approach for the Treatment of Cystic Fibrosis
TMEM16A Potentiation: A Novel Therapeutic Approach for the Treatment of Cystic Fibrosis Open
Rationale: Enhancing non-CFTR (cystic fibrosis transmembrane conductance regulator)-mediated anion secretion is an attractive therapeutic approach for the treatment of cystic fibrosis (CF) and other mucoobstructive diseases.Objectives: To …
View article: Identification of a Novel Allosteric Inhibitory Site on Tryptophan Hydroxylase 1 Enabling Unprecedented Selectivity Over all Related Hydroxylases
Identification of a Novel Allosteric Inhibitory Site on Tryptophan Hydroxylase 1 Enabling Unprecedented Selectivity Over all Related Hydroxylases Open
Pulmonary arterial hypertension (PAH) has demonstrated multi-serotonin receptor dependent pathologies, characterized by increased tone (5-HT1B receptor) and complex lesions (SERT, 5-HT1B, 5-HT2B receptors) of the pulmonary vasculature toge…
View article: Chronic Granulomatous Disease Presenting as Aspergillus Fumigatus Pneumonia in a Previously Healthy Young Woman
Chronic Granulomatous Disease Presenting as Aspergillus Fumigatus Pneumonia in a Previously Healthy Young Woman Open
BACKGROUND Chronic Granulomatous Disease (CGD) is a rare immunodeficiency disease caused by a genetic defect in the NADPH (nicotinamide adenine dinucleotide phosphate) oxidase enzyme, resulting in increased susceptibility to bacterial and …