estimate_1_LBI

This function provides a unified interface to multiple algorithm packages, 
 allowing different optimization algorithms to be selected for estimating 
 optimal model parameters. The entire optimization framework is based on 
 the log-likelihood returned by the model (or object function), making 
 this function a collection of likelihood-based inference (LBI) methods. 
 By abstracting over algorithm-specific implementations, the function 
 enables flexible and consistent parameter estimation across different 
 optimization backends.

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_1_LBI function

<dl><dt>model</dt>
<dd>Reinforcement Learning Model</dd>
<dt>env</dt>
<dd>multiRL.env</dd>
<dt>algorithm</dt>
<dd>Algorithm packages that multiRL supports, 
see algorithm</dd>
<dt>lower</dt>
<dd>Lower bound of free parameters</dd>
<dt>upper</dt>
<dd>Upper bound of free parameters</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

Likelihood-Based Inference (LBI) — estimate_1_LBI

<dl>

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


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


<dt>algorithm</dt>
<dd>Algorithm packages that multiRL supports, 
see algorithm</dd>


<dt>lower</dt>
<dd>Lower bound of free parameters</dd>


<dt>upper</dt>
<dd>Upper bound of free parameters</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_1_LBI: Likelihood-Based Inference (LBI)

Description

Usage

Value

Arguments