binaryRL-package {binaryRL} | R Documentation |
binaryRL: Reinforcement Learning Tools for Two-Alternative Forced Choice Tasks
Description
Tools for building reinforcement learning (RL) models specifically tailored for Two-Alternative Forced Choice (TAFC) tasks, commonly employed in psychological research. These models build upon the foundational principles of model-free reinforcement learning detailed in Sutton and Barto (2018) <ISBN:9780262039246>. The package allows for the intuitive definition of RL models using simple if-else statements. Our approach to constructing and evaluating these computational models is informed by the guidelines proposed in Wilson & Collins (2019) doi:10.7554/eLife.49547. Example datasets included with the package are sourced from the work of Mason et al. (2024) doi:10.3758/s13423-023-02415-x.
Example Data
-
Mason_2024_Exp1
: Experiment 1 of Mason et al. (2024) -
Mason_2024_Exp2
: Experiment 2 of Mason et al. (2024)
Steps
-
run_m
: Step 1: Building reinforcement learning model -
rcv_d
: Step 2: Generating fake data for parameter and model recovery -
fit_p
: Step 3: Optimizing parameters to fit real data -
rpl_e
: Step 4: Replaying the experiment with optimal parameters
Models
Functions
-
func_gamma
: Utility Function -
func_eta
: Learning Rate -
func_epsilon
: Exploration Strategy -
func_pi
: Upper-Confidence-Bound -
func_tau
: Soft-Max
Processes
-
optimize_para
: optimizing free parameters -
simulate_list
: simulating fake datasets -
recovery_data
: parameter and model recovery
Summary
-
summary.binaryRL
: summary(binaryRL.res)
Author(s)
Maintainer: YuKi hmz1969a@gmail.com (ORCID)
See Also
Useful links:
Report bugs at https://github.com/yuki-961004/binaryRL/issues