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Merge branch 'main' of https://github.com/sisl/InteractionImitation into main

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Arec Jamgochian
2022-03-03 20:20:48 -08:00
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@@ -19,53 +19,28 @@ The INTERACTION dataset contains a two folders which should be copied into a fol
- the contents of `recorded_trackfiles` should be copied to `./InteractionSimulator/datasets/trackfiles`
- the contents of `maps` should be copied to `./InteractionSimulator/datasets/maps`
## Processing, saving, and loading expert demos
Once the repository has been set up, you can process and save expert track demonstrations with:
## 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, saving them in `expert_data/`. The second command generates trajectory rollouts according to individual agent observations, which is later used as expert data for the learning models.
```
python src/expert_data.py --loc [LOCNUM] --track [TRACKNUM]
```
You can (and should) process all tracks at once at location 0 with:
```
python src/expert_data.py --all-tracks
```
You can then train a default behavior cloning policy with the following. Be sure to check help for main.py for running options.
```
python src/main.py --train
```
You can run tensorboard by running the following and opening `localhost:6006` (or alternatively port-forwarding 6006 from the remote server)
```
tensorboard --logdir output/
```
You can then test the learned policy with the following, and see the animation file in `output/`:
```
python src/main.py --test
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]'
```
You can load the experts actions manually
```
from src import expert_data
observations, actions = expert_data.load_expert_data(loc = [LOCNUM], track = [TRACKNUM])
for (s, a) in zip (observations, actions):
# do some imitation learning
```
## 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`, as well as the hyperparameters we search over in `bc-experiment.py`, `gail-experiment.py`, and `shail-experiment.py`. After training the models, configurations get saved in `best_configs/` (the best SHAIL confg gets copied to a HAIL config, with the appropriate environment parameters changed for ablation). However, upon manual inspection of the training runs, we note some better performance than the automatically-set configs at earlier epochs, so we adjust the `best_configs` manually.
After the `best_configs/` are set, we rerun each configuration with multiple seeds. The commands to do so are in the bottom half of `train_models.sh`. This saves different learned 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
|- demos
|- algorithms
|- BC
|- AdVIL
|- nets
|- Encoder
|- DeepSet
|- Decoder
|- policies
|- discriminators
|- demo_generators
|- TODO
```
## Type Definitions