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Reinforcement Learning: Theory and Applications in HEMS

Energies, 2022
The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article.
Omar Al-Ani, Sanjoy Das
doaj   +1 more source

How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review

Mayo Clinic Proceedings: Digital Health
To assess the current real-world applications of machine learning (ML) and artificial intelligence (AI) as functionality of digital behavior change interventions (DBCIs) that influence patient or consumer health behaviors.
Amy Bucher, PhD, E. Susanne Blazek, PhD, Christopher T. Symons, PhD   +2 more
doaj   +1 more source

Stochastic Reinforcement Learning

, 2019
In reinforcement learning episodes, the rewards and punishments are often non-deterministic, and there are invariably stochastic elements governing the underlying situation.
Kuang, Nikki Lijing, Leung, Clement H. C., Sung, Vienne W. K.   +2 more
core   +1 more source

Atari games and Intel processors

, 2017
The asynchronous nature of the state-of-the-art reinforcement learning algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes them exceptionally suitable for CPU computations.
Adamski, Robert, Grel, Tomasz, Klimek, Maciej, Michalewski, Henryk   +3 more
core   +1 more source

Adaptive Control with Approximated Policy Search Approach

ITB Journal of Engineering Science, 2010
Most of existing adaptive control schemes are designed to minimize error between plant state and goal state despite the fact that executing actions that are predicted to result in smaller errors only can mislead to non-goal states. We develop an adaptive
Agus Naba
doaj   +1 more source

Parallel model-based and model-free reinforcement learning for card sorting performance

Scientific Reports, 2020
The Wisconsin Card Sorting Test (WCST) is considered a gold standard for the assessment of cognitive flexibility. On the WCST, repeating a sorting category following negative feedback is typically treated as indicating reduced cognitive flexibility ...
Alexander Steinke, Florian Lange, Bruno Kopp   +2 more
doaj   +1 more source

Comfort or hesitancy: A cross-sectional study of modifiable factors associated with co-vaccination behavior among United States and Canadian adults

Preventive Medicine Reports
Objective: Co-vaccination, or receiving multiple vaccines at once, may improve vaccination uptake and reduce missed opportunities to vaccinate. Although generally considered safe and effective, co-vaccination is not well accepted outside of travel and ...
Emily Frith, Sarah Deedat, E. Susanne Blazek, Amy Bucher   +3 more
doaj   +1 more source

Hierarchical Reinforcement Learning: A Survey and Open Research Challenges

Machine Learning and Knowledge Extraction, 2022
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, Kevin Mets, Steven Latré   +2 more
doaj   +1 more source

Distributional Reinforcement Learning with Quantile Regression

, 2017
In reinforcement learning an agent interacts with the environment by taking actions and observing the next state and reward. When sampled probabilistically, these state transitions, rewards, and actions can all induce randomness in the observed long-term
Bellemare, Marc G., Dabney, Will, Munos, Rémi, Rowland, Mark   +3 more
core   +1 more source

Submodular Reinforcement Learning

Proc. International Conference on Learning Representations (ICLR), 2023
Spotlight paper at ICLR ...
Prajapat, Manish; id_orcid0000-0002-3867-4575, Mutný, Mojmír, Zeilinger, Melanie Nicole, Krause, Andreas   +3 more
openaire   +3 more sources