This repo contains the code base of the paper Language as a Cognitive Tool to Imagine Goals inCuriosity-Driven Exploration:
Colas, C., Karch, T., Lair, N., Dussoux, J. M., Moulin-Frier, C., Dominey, P. F., & Oudeyer, P. Y. (2020). Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration, Part of Advances in Neural Information Processing Systems 33 (NeurIPS 2020).
Learning open-ended repertoire of skills requires agents that autonomously explore their environments. To do so, they need to self-organize their exploration by generating and selecting their goals (IMGEP). In this framework, how can agents make creative discoveries?
In this paper, we propose to equip agents with language grounding capabilities in order to represent goals as language. We then leverage language compositionality and systematic generalization as a means to perform out-of-distribution goal generation.
We follow a developmental approach inspired by the role of egocentric language in child development (Piaget and Vygotsky) and generative expressivity (Chomsky).
We propose a Google Colab Notebook to walk you through the IMAGINE learning algorithm. The notebook contains:
- a full decomposition of the IMAGINE architecture
- visualizations of the modules' behavior during inference
- interactive generations of rollouts conditioned on goal sentences
The dependencies are listed in the requirements.txt file. Our conda environment can be cloned with:
conda env create -f environment.yml
The demo script is /src/imagine/experiments/play.py. It can be used as such:
python play.py
The main running script is /src/imagine/experiments/train.py. It can be used as such:
python train.py --num_cpu=6 --architecture=modular_attention --imagination_method=CGH --reward_function=learned_lstm --goal_invention=from_epoch_10 --n_epochs=167
Note that the number of cpu is an important parameter. Changing it is not equivalent to reducing/increasing training time. One epoch is 600 episodes. Other parameters can be found in train.py. The config.py file contains all parameters and is overriden by parameters defined in train.py.
Logs and results are saved in /src/data/expe/PlaygroundNavigation-v1/trial_id/. It contains policy and reward function checkpoints, raw logs (log.txt), a csv containing main metrics (progress.csv) and a json file with the parameters (params.json).
Results for one run can be plotted using the script /src/analyses/new_plot.py
- Playground Environment code base: https://github.com/flowersteam/playground_env
- Website: https://sites.google.com/view/imagine-drl
@article{colas2020language,
title={Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration},
author={Colas, Cédric and Karch, Tristan and Lair, Nicolas and Dussoux, Jean-Michel and Moulin-Frier, Clément and Dominey, F Peter and Oudeyer, Pierre-Yves},
journal={NeurIPS 2020},
year={2020}
}