Man pages for binaryRL
Reinforcement Learning Tools for Two-Alternative Forced Choice Tasks
| binaryRL-package | binaryRL: Reinforcement Learning Tools for Two-Alternative... |
| 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 |
