Reinforcement Learning Tools for Two-Alternative Forced Choice Tasks

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Documentation for package ‘binaryRL’ version 0.9.0

• DESCRIPTION file.

Help Pages

fit_p	Step 3: Optimizing parameters to fit real data
func_epsilon	Function: Exploration Strategy
func_eta	Function: Learning Rate
func_gamma	Function: Utility Function
func_pi	Function: Upper-Confidence-Bound
func_tau	Function: Soft-Max Function
Mason_2024_Exp1	Experiment 1 from Mason et al. (2024)
Mason_2024_Exp2	Experiment 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