exploring-through-rcgvf

This repository supplements the paper "Exploring through Random Curiosity with General Value Functions". The code for the MiniGrid experiments is available in this repository.

Code for the Diabolical Lock experiments is available here.

The implementation focuses on clarity and flexibility rather than computational efficiency.

Instructions

Run an individual experiment with RC-GVF from the root directory:

python3 -m scripts.train_rcgvf

# Or with RND
python3 -m scripts.train_rnd

# Or with NovelD
python3 -m scripts.train_noveld

# Or with AGAC
python3 -m scripts.train_agac

The settings for environment and algorithm can be modified in the config_defaults dictionary in each training file, or through a Weights & Biases (wandb) sweep. Details of baselines and the environment specific hyperparameters are available in Appendix C of our paper.

In case you don't want to utilise Weights & Biases:

export WANDB_MODE=disabled

Dependencies

• gymnasium
• minigrid
• torch-ac
• numpy
• torch
• wandb

Acknowledgements

The code follows the structure used in rl_starter_files.