# NeuMoSync: End-to-End Neuromodulatory Control for Plasticity and Adaptability in Continual Learning


## Requirements

To install requirements:

```setup
conda env create -n neumosync -f environment.yml
conda activate neumosync
```

## Running Benchmarks and Hyperparameter Tuning

To initiate the plasticity benchmarks experiments and perform hyperparameter tuning, please execute the relevant scripts located within the ./train directory for each specific benchmark.

## Pretraining with MAML

For pretraining using Model-Agnostic Meta-Learning (MAML), utilize the maml.py script. An example command is provided below:

```bash
python maml.py agent=baseline arch=mlp task=random_MNIST meta_train_epoch=1000
```

## Continual Training from MAML Pretrained Models

To conduct continual training subsequent to MAML pretraining, execute the maml_sl.py script. This script accepts the same arguments as maml.py.


## Configuration Details

For a comprehensive review of the default configurations for each method, please refer to the files located in the ./configs/agent directory.
