Context dependent intrinsic motivation allows diverse social dynamics to emerge from different environment.
Create virtual environment
conda create -n {ENV_NAME} python=3.8
Install this repo on a remote cluster without full previlige, first install SWIG from source:
Download SWIG
wget http://prdownloads.sourceforge.net/swig/swig-4.1.1.tar.gz
tar -xvf swig-4.1.1.tar.gz && cd swig-4.1.1/
Configure the makefile for SWIG. This is where you specify the prefix to a directory that you have write access to:
./configure --prefix={} --without-pcre
Build and install SWIG
make && make install
Then add the bin directory to your ~/.bashrc
echo 'export PATH={PATH_TO_YOUR_SWIG_DIR}/bin:$PATH' >> ~/.bashrc
source ~/.bashrc
Install repo and dependency (using venv or conda env with python=3.8)
pip install -e .
Experiments are seperated into different folders inside experiments/. The suggestion is to create a folder for each model and have
all the checkpoints and experiment configuration and parameter sweeping related stuff stored inside an outputs/ folder. As an example, to run
an experiment, go to one of the folders, e.g., experiments/icm_5_agents_gru/ and run it using
cd experiments/icm_5_agents_gru/
CUDA_VISIBLE_DEVICES={} python main.py
To run hyper-parameter search with wandb, go to an experiment folder and start the sweep with this command
wandb sweep --project {PROJECT_NAME} --name {SWEEP_NAME} config/sweep.yaml
Once the command started, you will get the search run command, and run many wandb agents in parallel using
CUDA_VISIBLE_DEVICES={GPU_NUM} wandb agent stanford_autonomous_agent/{SWEEP_NAME}/{SWEEP_ID}
To run evaluation with default setting, go to an experiment folder:
CUDA_VISIBLE_DEVICES={GPU_NUM} python main.py exp_run.eval_only=True exp_run.ckpt_dir={CKPT_DIR}
You can also change evaluation setting (anything in the config is modifiable) using commands similar to the following
- change number of tested episode, steps in an episode and result directory, e.g., change number of test_eps. Changing model to what you want to save the model result as
CUDA_VISIBLE_DEVICES=1 python main.py exp_run.eval_only=True exp_run.ckpt_dir={} exp_run.result_dir={} trainer.test_eps=5 model.name=social_influence_visible
This will add step-wise reward for each agent per-episode to new .pkl with other evaluation saved in the same directory, and save frames for all the episodes inside a folder called frames. The frames for each episode will be saved in the one folder.
To get evaluation plots, the the plotting functions are inside plot_utils.py
python analysis.py
Once the frames are created, u can copy create_video.sh into the frames/ folder and create videos
bash create_video.sh