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 Robot

estimate: Estimate Methods

algorithm: Algorithm Packages

control: Control Algorithm Behavior

Models

TD(): TD Model

RSTD(): RSTD 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

estimate_0_ENV(): estimate_0_ENV

estimate_1_LBI(): estimate_1_LBI

estimate_1_MLE(): estimate_1_MLE

estimate_1_MAP(): estimate_1_MAP

estimate_2_SBI(): estimate_2_SBI

estimate_2_ABC(): estimate_2_ABC

engine_ABC(): engine_ABC

estimate_2_RNN(): estimate_2_RNN

engine_RNN(): engine_RNN

estimation_methods(): estimation_methods

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