# Reinforcement Learning Tools for Multi-Armed Bandit

## Documentation for package ‘multiRL’ version 0.2.3

- DESCRIPTION file.

## Help Pages

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