# Reinforcement Learning Tools for Two-Alternative Forced Choice Tasks

## Documentation for package ‘binaryRL’ version 0.9.9

- DESCRIPTION file

## Help Pages

fit_p | Step 3: Optimizing parameters to fit real data
func_epsilon | Function: Epsilon Related
func_eta | Function: Learning Rate
func_gamma | Function: Utility Function
func_logl | Function: Loss Function
func_pi | Function: Upper-Confidence-Bound
func_tau | Function: Soft-Max Function
Mason_2024_G1 | Group 1 from Mason et al. (2024)
Mason_2024_G2 | Group 2 from Mason et al. (2024)
optimize_para | Process: Optimizing Parameters
rcv_d | Step 2: Generating fake data for parameter and model recovery
recovery_data | Process: Recovering Fake Data
rpl_e | Step 4: Replaying the experiment with optimal parameters
RSTD | Model: RSTD
run_m | Step 1: Building reinforcement learning model
simulate_list | Process: Simulating Fake Data
summary.binaryRL | S3method summary
TD | Model: TD
Utility | Model: Utility