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
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
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
funcs                   Core Functions
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
run_m                   Step 1: Building reinforcement learning model
settings                Settings of Model
summary,multiRL.model-method
                        summary
system                  Cognitive Processing System
