Results 61 to 70 of about 122,392 (268)
Hierarchical Reinforcement Learning: A Survey and Open Research Challenges
Reinforcement learning (RL) allows an agent to solve sequential decision-making problems by interacting with an environment in a trial-and-error fashion. When these environments are very complex, pure random exploration of possible solutions often fails,
Matthias Hutsebaut-Buysse +2 more
doaj +1 more source
Imitation Learning by Reinforcement Learning
Imitation learning algorithms learn a policy from demonstrations of expert behavior. We show that, for deterministic experts, imitation learning can be done by reduction to reinforcement learning with a stationary reward. Our theoretical analysis both certifies the recovery of expert reward and bounds the total variation distance between the expert and
openaire +3 more sources
The impact of leukocyte‐associated RNA retention on platelet RNA‐based cancer detection
Platelets carry RNA signals that may help detect cancer, but platelet samples can also retain RNA from white blood cells. We found that this retained white blood cell signal did not significantly affect machine learning cancer classification, suggesting that the cancer‐related RNA signal in platelets is largely independent of leukocyte‐associated RNA ...
Michał Sieczczyński +12 more
wiley +1 more source
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
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 +9 more
wiley +1 more source
Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties
Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases.
S. M. Nahid Mahmud +3 more
doaj +1 more source
Clustering with Reinforcement Learning
We show how a previously derived method of using reinforcement learning for supervised clustering of a data set can lead to a sub-optimal solution if the cluster prototypes are initialised to poor positions. We then develop three novel reward functions which show great promise in overcoming poor initialization. We illustrate the results on several data
Barbakh, Wesam, Fyfe, Colin
openaire +3 more sources
Cell surface CD11c as a neutrophil aging marker molecule
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 +5 more
wiley +1 more source
Observational Learning by Reinforcement Learning
Observational learning is a type of learning that occurs as a function of observing, retaining and possibly replicating or imitating the behaviour of another agent. It is a core mechanism appearing in various instances of social learning and has been found to be employed in several intelligent species, including humans. In this paper, we investigate to
Diana Borsa +3 more
openaire +2 more sources
This pilot study investigates the potential of Generative AI to provide formative feedback to students. ChatGPT was prompted to provide feedback on Year 1 Bioscience essays, which were evaluated against established good feedback practices. GenAI‐authored feedback had useful elements, but was limited in scope. GenAI may have potential to provide instant,
Annabel Court +3 more
wiley +1 more source

