Results 121 to 130 of about 544,487 (316)
Unsupervised Meta-Learning for Reinforcement Learning [PDF]
arXiv, 2018 Meta-learning algorithms use past experience to learn to quickly solve new tasks. In the context of reinforcement learning, meta-learning algorithms acquire reinforcement learning procedures to solve new problems more efficiently by utilizing experience from prior tasks.
arxiv
The partial reinforcement effect and competing behavior in a latent learning situation [PDF]
, 1967 Elvis C. Jones, Cecil C. Bridges
openalex +1 more source
Advanced Materials, EarlyView.Protein can undergo liquid–liquid phase separation and liquid‐to‐solid transition to form liquid condensates and solid aggregates. These phase transitions can be influenced by post‐translational modifications, mutations, and various environmental factors.
Tianchen Li +3 more
wiley +1 more source
Research on predicting 2D-HP protein folding using reinforcement learning with full state space
BMC Bioinformatics, 2019 Background Protein structure prediction has always been an important issue in bioinformatics. Prediction of the two-dimensional structure of proteins based on the hydrophobic polarity model is a typical non-deterministic polynomial hard problem ...
Hongjie Wu +5 more
doaj +1 more source
Learning in an invertebrate with two types of negative reinforcement [PDF]
, 1966 Joseph E. Morrow
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Engineering CAR‐T Therapeutics for Enhanced Solid Tumor Targeting
Advanced Materials, EarlyView.CART cell therapy has proven effective for blood cancers but struggles with solid tumors due to diverse antigens and complex environments. Recent efforts focus on improving CAR design and validation platforms. Advances in protein engineering, machine learning, and organoid systems aim to enhance CAR‐T therapy against solid tumors.
Danqing Zhu +4 more
wiley +1 more source
Z-Score Experience Replay in Off-Policy Deep Reinforcement Learning
SensorsReinforcement learning, as a machine learning method that does not require pre-training data, seeks the optimal policy through the continuous interaction between an agent and its environment.
Yana Yang +4 more
doaj +1 more source
Probability learning in the goldfish: I. Aversive reinforcement [PDF]
, 1966 Forrest W. Young, Harman V.S. Peeke
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Advanced Materials, EarlyView.This article investigates the micromechanics of bamboo epidermis, focusing on how anisotropic silica particle distributions enhance toughness. By integrating experimental imaging, 3D printing, and generative AI, the study develops bio‐inspired particle‐reinforced composites with mechanical properties akin to bamboo.
Zhao Qin, Aymeric Pierre Destree
wiley +1 more source
Advanced Materials, EarlyView.Wood and cellulose are the most abundant and important sustainable materials on the planet at the disposal to solve major societal challenges. This perspective, written for all materials scientists, highlights how breakthroughs in cellulose nanotechnology combined with functional nanomaterials can revolutionize important areas like construction ...
Mahiar Max Hamedi +5 more
wiley +1 more source