Reinforcement Learning in Patients With Mood and Anxiety Disorders vs Control Individuals: A Systematic Review and Meta-analysis.
Sign Up to like & getrecommendations! Published in 2022 at "JAMA psychiatry"
DOI: 10.1001/jamapsychiatry.2022.0051
Abstract: Importance Computational psychiatry studies have investigated how reinforcement learning may be different in individuals with mood and anxiety disorders compared with control individuals, but results are inconsistent. Objective To assess whether there are consistent differences… read more here.
Keywords: reinforcement learning; control individuals; meta analysis;
Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Applied Clinical Medical Physics"
DOI: 10.1002/acm2.13898
Abstract: Abstract Motivation Medical image analysis involves a series of tasks used to assist physicians in qualitative and quantitative analyses of lesions or anatomical structures which can significantly improve the accuracy and reliability of medical diagnoses… read more here.
Keywords: reinforcement learning; medical image; learning medical; analysis ... See more keywords
A Reconfigurable Two‐WSe2‐Transistor Synaptic Cell for Reinforcement Learning
Sign Up to like & getrecommendations! Published in 2022 at "Advanced Materials"
DOI: 10.1002/adma.202107754
Abstract: Reward‐modulated spike‐timing‐dependent plasticity (R‐STDP) is a brain‐inspired reinforcement learning (RL) rule, exhibiting potential for decision‐making tasks and artificial general intelligence. However, the hardware implementation of the reward‐modulation process in R‐STDP usually requires complicated Si complementary… read more here.
Keywords: cell; reinforcement learning; reinforcement; reconfigurable two ... See more keywords
A reinforcement learning‐based hybrid modeling framework for bioprocess kinetics identification
Sign Up to like & getrecommendations! Published in 2022 at "Biotechnology and Bioengineering"
DOI: 10.1002/bit.28262
Abstract: Constructing predictive models to simulate complex bioprocess dynamics, particularly time‐varying (i.e., parameters varying over time) and history‐dependent (i.e., current kinetics dependent on historical culture conditions) behavior, has been a longstanding research challenge. Current advances in… read more here.
Keywords: time; bioprocess; reinforcement learning; framework ... See more keywords
A reinforcement learning‐based approach for modeling and coverage of an unknown field using a team of autonomous ground vehicles
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22331
Abstract: Precision maps are useful in agricultural farm management for providing farmers (and field researchers) with locational information. Having an environmental model that includes geo‐referenced data would facilitate the deployment of multi‐robot systems, that has emerged… read more here.
Keywords: ground vehicles; field; reinforcement learning; coverage ... See more keywords
Consciousness‐driven reinforcement learning: An online learning control framework
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22647
Abstract: As a powerful tool for solving nonlinear complex system control problems, the model‐free reinforcement learning hardly guarantees system stability in the early stage of learning, especially with high complicity learning components applied. In this paper,… read more here.
Keywords: framework; control; online learning; reinforcement learning ... See more keywords
Multiagent reinforcement learning for strictly constrained tasks based on Reward Recorder
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22945
Abstract: Multiagent reinforcement learning (MARL) has been widely applied in engineering problems. However, many strictly constrained problems such as distributed optimization in engineering applications are still a great challenge to MARL. Especially for strict global constraints… read more here.
Keywords: reinforcement learning; reinforcement; strictly constrained; reward recorder ... See more keywords
Reinforcement learning method for target hunting control of multi‐robot systems with obstacles
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.23042
Abstract: Aiming at the target encirclement problem of multi‐robot systems, a target hunting control method based on reinforcement learning is proposed. First, the Markov game modeling for the multi‐robot system is carried out. According to the… read more here.
Keywords: reinforcement learning; target hunting; control; multi robot ... See more keywords
Conformer‐RL: A deep reinforcement learning library for conformer generation
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Computational Chemistry"
DOI: 10.1002/jcc.26984
Abstract: Conformer‐RL is an open‐source Python package for applying deep reinforcement learning (RL) to the task of generating a diverse set of low‐energy conformations for a single molecule. The library features a simple interface to train… read more here.
Keywords: conformer generation; conformer; reinforcement learning; deep reinforcement ... See more keywords
Multigradient recursive reinforcement learning NN control for affine nonlinear systems with unmodeled dynamics
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Robust and Nonlinear Control"
DOI: 10.1002/rnc.4843
Abstract: In this paper, an adaptive reinforcement learning approach is developed for a class of discrete‐time affine nonlinear systems with unmodeled dynamics. The multigradient recursive (MGR) algorithm is employed to solve the local optimal problem, which… read more here.
Keywords: systems unmodeled; nonlinear systems; reinforcement learning; unmodeled dynamics ... See more keywords
Branes with brains: exploring string vacua with deep reinforcement learning
Sign Up to like & getrecommendations! Published in 2019 at "Journal of High Energy Physics"
DOI: 10.1007/jhep06(2019)003
Abstract: A bstractWe propose deep reinforcement learning as a model-free method for exploring the landscape of string vacua. As a concrete application, we utilize an artificial intelligence agent known as an asynchronous advantage actor-critic to explore… read more here.
Keywords: string vacua; reinforcement learning; reinforcement; deep reinforcement ... See more keywords