estimate_2_SBI

Since both Approximate Bayesian Computation (ABC) and Recurrent Neural 
 Network (RNN) are simulation-based inference methods, they require a 
 model built from a large amount of simulated data before running. This 
 model is then used to predict the most likely input parameters 
 corresponding to the real data. Therefore, this function generates random 
 input parameters using user-specified distributions and produces 
 simulated data based on these parameters.

A flexible general-purpose toolbox for implementing Rescorla-Wagner models
in multi-armed bandit tasks.
As the successor and functional extension of the 'binaryRL' package,
'multiRL' modularizes the Markov Decision Process (MDP) into six core
components. This framework enables users to construct custom models via
intuitive if-else syntax and define latent learning rules for agents.
For parameter estimation, it provides both likelihood-based
inference (MLE and MAP) and simulation-based inference (ABC and
RNN), with full support for parallel processing across subjects.
The workflow is highly standardized, featuring four main functions
that strictly follow the four-step protocol (and ten rules)
proposed by Wilson & Collins (2019) <doi:10.7554/eLife.49547>.
Beyond the three built-in models (TD, RSTD, and Utility), users
can easily derive new variants by declaring which variables are
treated as free parameters.

YuKi 

multiRL

Reinforcement Learning Tools for Multi-Armed Bandit

Xinyu 

estimate_2_SBI function

<dl><dt>model</dt>
<dd>Reinforcement Learning Model</dd>
<dt>env</dt>
<dd>multiRL.env</dd>
<dt>priors</dt>
<dd>Prior probability density function of the free parameters,
see priors</dd>
<dt>control</dt>
<dd>Settings manage various aspects of the iterative process,
see control</dd>
<dt>...</dt>
<dd>Additional arguments passed to internal functions.</dd></dl>

Arguments

Simulated-Based Inference (SBI) — estimate_2_SBI

<dl>

<dt>model</dt>
<dd>Reinforcement Learning Model</dd>


<dt>env</dt>
<dd>multiRL.env</dd>


<dt>priors</dt>
<dd>Prior probability density function of the free parameters,
see priors</dd>


<dt>control</dt>
<dd>Settings manage various aspects of the iterative process,
see control</dd>


<dt>...</dt>
<dd>Additional arguments passed to internal functions.</dd>

</dl>

estimate_2_SBI: Simulated-Based Inference (SBI)

Description

Usage

Value

Arguments