This repository contains a reference implementation DRRN as mentioned in Interactive Fiction Games: A Colossal Adventure, that has been modified for use with the ScienceWorld environment.
Install Dependencies:
# Clone repository
git clone https://github.com/cognitiveailab/drrn-scienceworld.git
cd drrn-scienceworld
# Create conda environment
conda create --name drrn1 python=3.8
conda activate drrn1
pip install -r requirements.txt
An example of training the DRRN model (using 8 threads, for 10k training steps, evaluating on dev every 1k steps):
cd drrn
python3 train-scienceworld.py --num_envs=8 --max_steps=10000 --task_idx=13 --simplification_str=easy --priority_fraction=0.50 --memory_size=100000 --env_step_limit=100 --eval_freq=1000 --eval_set=dev --historySavePrefix=drrn-task13-results-seed0-dev Here:
- max_steps: Maximum number of steps to train for (per environment thread)
- num_envs: The number of environment threads to simultaneously use during training (8 is a common number)
- task_idx: The ScienceWorld task index (0-29). See task list below
- env_step_limit: the maximum number of steps to run an environment for, before it times out and resets (100 typical)
- eval_freq: the number of steps between evaluations
- eval_set: which set to perform the evaluations on (dev or test)
- historySavePrefix: the filename prefix for saving the output history files, which contain full logs to calculate final scores, plot performance curves, examing action history, etc.
- priority_fraction and memory_size: Hyperparameters for the DRRN model (see paper for more information).
This configuration generally takes about 1-2 hours to run (to 10k steps).
TASK LIST:
0: task-1-boil (30 variations)
1: task-1-change-the-state-of-matter-of (30 variations)
2: task-1-freeze (30 variations)
3: task-1-melt (30 variations)
4: task-10-measure-melting-point-(known-substance) (436 variations)
5: task-10-measure-melting-point-(unknown-substance) (300 variations)
6: task-10-use-thermometer (540 variations)
7: task-2-power-component (20 variations)
8: task-2-power-component-(renewable-vs-nonrenewable-energy) (20 variations)
9: task-2a-test-conductivity (900 variations)
10: task-2a-test-conductivity-of-unknown-substances (600 variations)
11: task-3-find-animal (300 variations)
12: task-3-find-living-thing (300 variations)
13: task-3-find-non-living-thing (300 variations)
14: task-3-find-plant (300 variations)
15: task-4-grow-fruit (126 variations)
16: task-4-grow-plant (126 variations)
17: task-5-chemistry-mix (32 variations)
18: task-5-chemistry-mix-paint-(secondary-color) (36 variations)
19: task-5-chemistry-mix-paint-(tertiary-color) (36 variations)
20: task-6-lifespan-(longest-lived) (125 variations)
21: task-6-lifespan-(longest-lived-then-shortest-lived) (125 variations)
22: task-6-lifespan-(shortest-lived) (125 variations)
23: task-7-identify-life-stages-1 (14 variations)
24: task-7-identify-life-stages-2 (10 variations)
25: task-8-inclined-plane-determine-angle (168 variations)
26: task-8-inclined-plane-friction-(named-surfaces) (1386 variations)
27: task-8-inclined-plane-friction-(unnamed-surfaces) (162 variations)
28: task-9-mendellian-genetics-(known-plant) (120 variations)
29: task-9-mendellian-genetics-(unknown-plant) (480 variations)
This code generally runs best with at least num_threads+1 CPU cores (e.g. about 10 cores for an 8-thread environment).
The GPU memory requirements are variable, but generally stay below 8gb.
-
Many threads: If you are attempting to use a large number of threads (e.g. 20+), you may need to add an additional several-second delay after the threads spawn before the rest of the program runs. (The ScienceWorld API already adds a 5 second delay, which handles small numbers of threads well.)
-
Model saving with manys steps: Very occassionally, on very long runs (generally 1M+ steps), the periodic pickling the model when saving checkpoints runs into issues and freezes. The cause is unknown, but as a workaround the save has been wrapped in a timeout, so that if it takes longer than 2 minutes to save the model, the checkpoint is not saved and training continues. Subsequent checkpoints usually save without issue.
If this DRRN agent is helpful in your work, please cite the following:
@article{hausknecht19colossal,
title={Interactive Fiction Games: A Colossal Adventure},
author={Matthew Hausknecht and Prithviraj Ammanabrolu and Marc-Alexandre C{\^{o}}t{\'{e}} and Xingdi Yuan},
journal={CoRR},
year={2019},
volume={abs/1909.05398}
}