MAB                     Simulated Multi-Arm Bandit Dataset
RSTD                    Risk Sensitive Model
TAB                     Group 2 from Mason et al. (2024)
TD                      Temporal Differences Model
Utility                 Utility Model
WMT                     Data from Collins and Frank (2012)
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
func_zeta               Function: Decay Rate
funcs                   Core Functions
layer                   Layers and Loss Functions (RNN)
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
run_m                   Step 1: Building reinforcement learning model
settings                Settings of Model
summary,multiRL.model-method
                        summary
system                  Cognitive Processing System
