Reinforcement Learning Tools for Multi-Armed Bandit

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Documentation for package ‘multiRL’ version 0.2.3

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algorithm Algorithm Packages
behrule Behavior Rules
colnames Column Names
control Control Algorithm Behavior
data Dataset Structure
engine_ABC The Engine of Approximate Bayesian Computation (ABC)
engine_RNN The Engine of Recurrent Neural Network (RNN)
estimate Estimate Methods
estimate_0_ENV Tool for Generating an Environment for Models
estimate_1_LBI Likelihood-Based Inference (LBI)
estimate_1_MAP Estimation Method: Maximum A Posteriori (MAP)
estimate_1_MLE Estimation Method: Maximum Likelihood Estimation (MLE)
estimate_2_ABC Estimation Method: Approximate Bayesian Computation (ABC)
estimate_2_RNN Estimation Method: Recurrent Neural Network (RNN)
estimate_2_SBI Simulated-Based Inference (SBI)
estimation_methods Estimate Methods
fit_p Step 3: Optimizing parameters to fit real data
funcs Core Functions
func_alpha Function: Learning Rate
func_beta Function: Soft-Max
func_delta Function: Upper-Confidence-Bound
func_epsilon Function: epsilon–first, Greedy, Decreasing
func_gamma Function: Utility Function
func_zeta Function: Decay Rate
MAB Simulated Multi-Arm Bandit Dataset
params Model Parameters
plot.multiRL.replay plot.multiRL.replay
policy Policy of Agent
priors Density and Random Function
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
rcv_d Step 2: Generating fake data for parameter and model recovery
rpl_e Step 4: Replaying the experiment with optimal parameters
RSTD Risk Sensitive Model
run_m Step 1: Building reinforcement learning model
settings Settings of Model
summary-method summary
system Cognitive Processing System
TAB Group 2 from Mason et al. (2024)
TD Temporal Differences Model
Utility Utility Model