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SARSA specifications


Unique identifier OMICS_08367
Interface Web user interface
Restrictions to use None
Computer skills Basic
Stability No
Maintained No


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Publication for SARSA

SARSA in publications

PMCID: 5859020
PMID: 29555972
DOI: 10.1038/s41598-018-23215-7

[…] purpose of control and supervision of experiments on animals (827/go/re/s/04/cpcsea). fertilized eggs (55 ± 2.1 g) of white leghorn (gallus gallus domesticus) were obtained from shakti hatcheries, sarsa, gujarat, stored for 2 days at 12 °c and then incubated under standard conditions (37.5 °c, humidity 60%) for 48 hours and guidelines of committee for the purpose of control and supervision […]

PMCID: 5846819
PMID: 29569637
DOI: 10.1186/s40649-018-0052-z

[…] controller has successfully controlled the system., to learn the control signal to insert into the network at any time step, we use reinforcement learning. more precisely, we use a gradient-descent sarsa [] algorithm with a cmac tiling [] for function approximation of the real-valued distribution parameters. these are both commonly used solutions within the reinforcement domain. […]

PMCID: 5797660
PMID: 29441027
DOI: 10.3389/fpsyg.2018.00005

[…] constraint is assumed for computing rewards. the models are learned by maximizing the expected reward using reinforcement learning algorithms [i.e., table-based algorithms: q-learning, sarsa, sarsa-λ, and neural network-based algorithms: q-learning for neural network (q-nn), neural-fitted q-network (nfq), and deep q-network (dqn)]. neural network-based reinforcement learning models […]

PMCID: 5663920
PMID: 29089575
DOI: 10.1038/s41598-017-14740-y

[…] is often used as a technique to reduce the size of the percept space, which is potentially very large. two useful recent summaries can be found in refs,. for example, in the q-learning and sarsa algorithms, it is common to use function approximation methods–,, realized by e.g. tile coding (cmac),, neural networks–, decision trees,, constructive function approximation, or support vector […]

PMCID: 5628940
PMID: 28945743
DOI: 10.1371/journal.pcbi.1005768

[…] expected number of times you will encounter each other state/action pair: h(sa,s′a′)=e[∑t=0∞γti(sat=s′a′)|sa0=sa].(14), h can then be used as a linear basis for learning q(s,a), using the sarsa td algorithm to learn a weight for each column of h. in particular, when state-action s’a’ is performed after state action sa, a prediction error is calculated and used to update w: […]

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SARSA institution(s)
Institute of Bioinformatics, National Chioa Tung University, Department of Computer Science, National Tsing Hua University and Department of Biological Science and Technology, National Chioa Tung University, Hsinchu, Taiwan

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