Package index • multiRL

Steps

run_m(): Step 1: Building reinforcement learning model

rcv_d(): Step 2: Generating fake data for parameter and model recovery

fit_p(): Step 3: Optimizing parameters to fit real data

rpl_e(): Step 4: Replaying the experiment with optimal parameters

Document

data: Dataset Structure

colnames: Column Names

behrule: Behavior Rules

funcs: Core Functions

params: Model Parameters

priors: Density and Random Function

settings: Settings of Model

policy: Policy of Agent

system: Cognitive Processing System

estimate: Estimate Methods

algorithm: Algorithm Packages

control: Control Algorithm Behavior

Models

TD(): Temporal Differences Model

RSTD(): Risk Sensitive Model

Utility(): Utility Model

Functions

func_alpha(): Function: Learning Rate

func_beta(): Function: Soft-Max

func_gamma(): Function: Utility Function

func_delta(): Function: Upper-Confidence-Bound

func_epsilon(): Function: \(\epsilon\)–first, Greedy, Decreasing

func_zeta(): Function: Decay Rate

Processes

process_1_input(): multiRL.input

process_2_behrule(): multiRL.behrule

process_3_record(): multiRL.record

process_4_output_cpp(): multiRL.output

process_4_output_r(): multiRL.output

process_5_metric(): multiRL.metric

Estimation

estimation_methods(): Estimate Methods

estimate_0_ENV(): Tool for Generating an Environment for Models

estimate_1_LBI(): Likelihood-Based Inference (LBI)

estimate_1_MLE(): Estimation Method: Maximum Likelihood Estimation (MLE)

estimate_1_MAP(): Estimation Method: Maximum A Posteriori (MAP)

estimate_2_SBI(): Simulated-Based Inference (SBI)

estimate_2_ABC(): Estimation Method: Approximate Bayesian Computation (ABC)

estimate_2_RNN(): Estimation Method: Recurrent Neural Network (RNN)

engine_ABC(): The Engine of Approximate Bayesian Computation (ABC)

engine_RNN(): The Engine of Recurrent Neural Network (RNN)

Datasets

TAB: Group 2 from Mason et al. (2024)

MAB: Simulated Multi-Arm Bandit Dataset

Summary

summary(<multiRL.model>): summary

Plot

plot(<multiRL.replay>): plot.multiRL.replay