diff --git a/README.md b/README.md
index da3af1c..dee4764 100644
--- a/README.md
+++ b/README.md
@@ -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