Man pages for multiRL
Reinforcement Learning Tools for Multi-Armed Bandit

algorithm	Algorithm Packages (MLE, MAP)
behrule	Behavior Rules
colnames	Column Names
control	Controls of Estimation Methods
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
func_alpha	Function: Learning Rate
func_beta	Function: Probability
func_delta	Function: Bias
func_epsilon	Function: Exploration or Exploitation
func_gamma	Function: Utility
funcs	Core Functions
func_zeta	Function: Decay Rate
layer	Layers and Loss Functions (RNN)
MAB	Simulated Multi-Arm Bandit Dataset
multiRL-package	multiRL: Reinforcement Learning Tools for Multi-Armed Bandit
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
reduction	Dimension Reduction Methods (ABC)
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-multiRL.model-method	summary
system	Cognitive Processing System
TAB	Group 2 from Mason et al. (2024)
TD	Temporal Differences Model
Utility	Utility Model