RNN Volatility Project (P2)

This repository contains code for the following paper:

A meta reinforcement learning account of behavioral adaptation to volatility in recurrent neural networks

• R is used in scripts (.R files), cognitive models (.stan files) and to save/load data (.RData files)
• Python is used in all .py files (main.py, classes) to train/test RNNs and in notebooks (Jupyter Notebooks) for analysis and plotting

Install Requirements

Python

Install Anaconda, then open Anaconda Prompt and navigate to this repository.

Then recreate the environment of this project by typing the following in Anaconda Prompt:

conda env create -f environment.yml

You should now have a virtual environment called RNNExplore with all the necessary dependancies regarding Python.

R

Install R (This work is based on version 4.1.1) and R Studio (This work is based on version 2021.09.1)

Then double click on p1_generalization.Rproj, this will open R Studio, then in the console type the following to install all R dependancies:

renv::restore()

Alternatively, you can load the necessary packages from an .RData file and install them manually.

load("r_packages.RData")
install.packages(r_pckgs)

Directory Structure

p2_volatility
│   README.md
│   main.py
│   helpers.py
|   .Rprofile
|   .gitignore
|   environment.yaml
|   helpers.py
|   p2_volatility.RData
|   r_packages.RData
|   renv.lock  
│
└───classes
│   │
│   └───bandits
│   │   
│   └───neural_networks
|       |
|       └───rnns
│   
└───cognitive_models
│
└───data
│   │   
│   └───intermediate_data
│   │   │   
│   │   └───fixed_bandits
│   │   │ 
│   │   └───modeling
│   │   │   │ 
│   │   │   └───modeling_fits
│   │   │   │    
│   │   │   └───preprocessed_data_for_modeling
│   │   │
│   │   └───jasp_analysis
│   │      
│   └───rnn_raw_data     
│
└───doc
│
└───notebooks
│
└───plots
│
└───renv
│
└───saved_models
│
└───scripts
│
└───tensorboard

Content

The repository contains:

• main.py: This is the main script, which can be used for training, testing and simulating the RNNs.
• helpers.py: contains classes/functions to handle .zip, .feather files and other helper functions
• classes: contains classes for creating bandit tasks and RNNs
• cognitive_models: contains bayesian cognitive models written in STAN
• data: contains RNN and human behavioural data, pregenerated bandits to test the RNNs, data to conduct bayesian analysis with JASP, preprocessed data for cognitive modeling and posterior model fits.
• saved_models: contains trained RNN weights and biases
• scripts: contains scripts used in this project (see scripts_explanations.md)
• notebooks: contains jupyter notebooks used in this project (see notebooks_explanations.md)
• tensorboard: contains saved files to plot RNN training logs in tensorboard
• doc: contains documentation and how-to guides for this project