Results 51 to 60 of about 37,169,246 (294)

From energy provision to protein synthesis: Tunnelling nanotubes as mediators of intercellular metabolic cooperation in cancer

FEBS Open Bio, EarlyView.
The cytoskeleton‐mediated transport of mitochondria via tunnelling nanotubes restores respiration, increases ATP production, rescues cells from apoptosis, activates the AKT/mTOR signalling pathway, promotes cell migration and invasiveness, contributes to cancer progression and treatment resistance.
Stanislava Martínková, Jan Trnka
wiley   +1 more source

Reinforcement learning-based statistical search strategy for an axion model from flavor

Journal of High Energy Physics
We propose a reinforcement learning-based search strategy to explore new physics beyond the Standard Model. The reinforcement learning, which is one of machine learning methods, is a powerful approach to find model parameters with phenomenological ...
Satsuki Nishimura, Coh Miyao, Hajime Otsuka   +2 more
doaj   +1 more source

An Adaptive Energy Management Strategy for Off-Road Hybrid Tracked Vehicles

Energies
Conventional energy management strategies based on reinforcement learning often fail to achieve their intended performance when applied to driving conditions that significantly deviate from their training conditions.
Lijin Han, Wenhui Shi, Ningkang Yang
doaj   +1 more source

Model Imitation for Model-Based Reinforcement Learning

CoRR, 2019
Model-based reinforcement learning (MBRL) aims to learn a dynamic model to reduce the number of interactions with real-world environments. However, due to estimation error, rollouts in the learned model, especially those of long horizons, fail to match the ones in real-world environments. This mismatching has seriously impacted the sample complexity of
Yueh-Hua Wu, Ting-Han Fan, Peter J. Ramadge, Hao Su 0001   +3 more
openaire   +2 more sources

From patient advocacy to patient‐driven research: Building active partnerships beginning at the bench to reach the bedside

FEBS Open Bio, EarlyView.
Research is strongest when conducted alongside patients, not just about them. Patient research organizations help integrate patient perspectives into research priorities, study design, and scientific meetings, leading to meaningful patient outcomes and development of relevant therapies.
Jenica H. Kakadia, Rachel Heilmann, Maximiliano Barrientos, Laura MacNeill, Ingrid Vallee, Sarah K. Schultz, Patrick O'Donoghue, Angelica A. Moresco, Victoria M. Siu, Ilka U. Heinemann   +9 more
wiley   +1 more source

A Contraction Approach to Model-based Reinforcement Learning

CoRR, 2020
Despite its experimental success, Model-based Reinforcement Learning still lacks a complete theoretical understanding. To this end, we analyze the error in the cumulative reward using a contraction approach. We consider both stochastic and deterministic state transitions for continuous (non-discrete) state and action spaces.
Ting-Han Fan, Peter J. Ramadge
openaire   +4 more sources

Cell surface CD11c as a neutrophil aging marker molecule

FEBS Open Bio, EarlyView.
Cell surface CD11chi neutrophils were more aged and had better phagocytic function than CD11c−/lo neutrophils. Transcriptomic analysis of CD11chi neutrophils and CD11c−/lo neutrophils in pediatric population showed that the most difference was seen in infants.
Sophia Koutsogiannaki, Fahd Alhamdan, Erik Malm, Marie Bermudez, Samuel Y. Kim, Koichi Yuki   +5 more
wiley   +1 more source

Exploring the limits of hierarchical world models in reinforcement learning

Scientific Reports
Hierarchical model-based reinforcement learning (HMBRL) aims to combine the sample efficiency of model-based reinforcement learning with the abstraction capability of hierarchical reinforcement learning.
Robin Schiewer, Anand Subramoney, Laurenz Wiskott   +2 more
doaj   +1 more source

A Reinforcement Learning Model Based on Temporal Difference Algorithm

IEEE Access, 2019
In some sense, computer game can be used as a test bed of artificial intelligence to develop intelligent algorithms. The paper proposed a kind of intelligent method: a reinforcement learning model based on temporal difference (TD) algorithm. And then the
Xiali Li, Zhengyu Lv, Song Wang, Zhi Wei, Licheng Wu   +4 more
doaj   +1 more source

Discriminator Augmented Model-Based Reinforcement Learning

CoRR, 2021
By planning through a learned dynamics model, model-based reinforcement learning (MBRL) offers the prospect of good performance with little environment interaction. However, it is common in practice for the learned model to be inaccurate, impairing planning and leading to poor performance. This paper aims to improve planning with an importance sampling
Behzad Haghgoo, Allan Zhou, Archit Sharma, Chelsea Finn   +3 more
openaire   +2 more sources