InteractionImitation

Imitation Learning with the INTERACTION Dataset

Getting started

Clone InteractionSimulator and pip install the module.

git clone https://github.com/sisl/InteractionSimulator.git
cd InteractionSimulator
pip install -e .
cd ..
export PYTHONPATH=$(pwd):$PYTHONPATH

Install additional requirements

pip install -r requirements.txt

Copy INTERACTION Dataset files: The INTERACTION dataset contains a two folders which should be copied into a folder called ./InteractionSimulator/datasets:

• the contents of recorded_trackfiles should be copied to ./InteractionSimulator/datasets/trackfiles
• the contents of maps should be copied to ./InteractionSimulator/datasets/maps

Processing and saving expert demos

Once the repository has been set up, you need to generate two separate sets of expert demos for tracks 0-4. The first command generates true joint and individual states and actions, necessary for evaluating. The second command generates trajectory rollouts according to individual agent observations, which will be used in behavior cloning, along with gail/hail/shail discriminators.

python -m src.expert --locs='[DR_USA_Roundabout_FT]' --tracks='[0,1,2,3,4]'
python -m intersimple-expert-rollout-setobs2 --tracks='[0,1,2,3,4]'

Tuning hyperparameters and training finalized models

To tune models, we use ray[tune] grid searches. You can run see the commands we used to train in the top half of train_models.sh. After training the models, configurations get saved in best_configs/. However, we note some better performance manually at earlier epochs of training, so we adjusted our best_configs manually.

After the best_configs/ are set, we rereun each configuration with multiple seeds, the commands to do so are in the bottom half of train_models.sh. This saves different policy files to test_policies/.

Evaluating models

To evaluate the learned policies, we rerun each model in particular setting, evaluate all our metrics, and average over different trained model seeds. The commands to do so are in evaluate_models.sh.

Package Structure

InteractionImitation
|- TODO

Type Definitions

Demo: List[Trajectory]
Trajectory: List[Tuple[Observation, Action]] # single expert
Observation: Dict[
  'own_state': [x, y, v, psi, psidot],
  'relative_states': List[[xr, yr, vr, psir, psidotr]],
  'own_path': List[[xr, yr]], # fixed length, constant dt
  'map': Map, # relative
]
Action: Range[0, 1]
Policy: Union[
  Callable[[Observation], Action],
  Callable[[Observation, Action], probability],
]
Discriminator: Callable[[Observation, Action], value]
Map: Dictionary[...]