estimate_2_ABC

This function takes a large set of simulated data to train an Approximate 
 Bayesian Computation (ABC) model and then uses the trained model to 
 estimate optimal parameters for the target data.

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_ABC function

<dl><dt>data</dt>
<dd>A data frame in which each row represents a single trial,
see data</dd>
<dt>colnames</dt>
<dd>Column names in the data frame,
see colnames</dd>
<dt>behrule</dt>
<dd>The agent’s implicitly formed internal rule,
see behrule</dd>
<dt>ids</dt>
<dd>The Subject ID of the participant whose data needs to be fitted.</dd>
<dt>models</dt>
<dd>Reinforcement Learning Models</dd>
<dt>funcs</dt>
<dd>The functions forming the reinforcement learning model,
see funcs</dd>
<dt>priors</dt>
<dd>Prior probability density function of the free parameters,
see priors</dd>
<dt>settings</dt>
<dd>Other model settings, 
see settings</dd>
<dt>lowers</dt>
<dd>Lower bound of free parameters in each model.</dd>
<dt>uppers</dt>
<dd>Upper bound of free parameters in each model.</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

Estimation Method: Approximate Bayesian Computation (ABC) — estimate_2_ABC

<dl>

<dt>data</dt>
<dd>A data frame in which each row represents a single trial,
see data</dd>


<dt>colnames</dt>
<dd>Column names in the data frame,
see colnames</dd>


<dt>behrule</dt>
<dd>The agent’s implicitly formed internal rule,
see behrule</dd>


<dt>ids</dt>
<dd>The Subject ID of the participant whose data needs to be fitted.</dd>


<dt>models</dt>
<dd>Reinforcement Learning Models</dd>


<dt>funcs</dt>
<dd>The functions forming the reinforcement learning model,
see funcs</dd>


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


<dt>settings</dt>
<dd>Other model settings, 
see settings</dd>


<dt>lowers</dt>
<dd>Lower bound of free parameters in each model.</dd>


<dt>uppers</dt>
<dd>Upper bound of free parameters in each model.</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_ABC: Estimation Method: Approximate Bayesian Computation (ABC)

Description

Usage

Value

Arguments