diff --git a/scratch/arec/intersimple/commands.txt b/scratch/arec/intersimple/commands.txt
index 1e1285a..b706521 100644
--- a/scratch/arec/intersimple/commands.txt
+++ b/scratch/arec/intersimple/commands.txt
@@ -1 +1 @@
-python -m render_options --model_name='gail_options_image_longlong_nocollision' --env='NRasterizedRoute' --options=True --width=36 --height=36 --m_per_px=2 --agent=50 --stop_on_collision=False
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+python -m render_options --model_name='gail_options_image_mid_wcollision' --env='NRasterizedRoute' --options=True --width=36 --height=36 --m_per_px=2 --agent=50 --stop_on_collision=False
\ No newline at end of file
diff --git a/scratch/arec/intersimple/plan.txt b/scratch/arec/intersimple/plan.txt
index 8425d4b..dac52b6 100644
--- a/scratch/arec/intersimple/plan.txt
+++ b/scratch/arec/intersimple/plan.txt
@@ -36,8 +36,12 @@ Should train without stopping for collisions, however when doing so, end up with
 -- It seems safe at the start of each vehicles sim, but actually it isn't since a car will spawn and hit it
 Solutions:
     -- Hold cars from spawning if their spawn location is full
-    -- Start simulations a few seconds later (after cars clear their spawn places)
+    -- Start simulations a few seconds later (after cars clear their spawn places) <- Preferred
     
+Test could run indefinitely if stop_on_collision is off
+Solution:
+    -- Set maximum episode length in intersimple
+
 
 
 
diff --git a/scratch/arec/intersimple/test_model.py b/scratch/arec/intersimple/test_model.py
new file mode 100644
index 0000000..e4a5758
--- /dev/null
+++ b/scratch/arec/intersimple/test_model.py
@@ -0,0 +1,135 @@
+from tqdm import tqdm
+from copy import deepcopy
+
+ALL_OPTIONS =
+
+def load_model(model_path:str, method:str):
+    """
+    Load a model given a path and the method
+    
+    Args:
+        model_path (str): the path to the model
+        method (str): the method for the model
+    Returns:
+        model: the action model
+        is_heir (bool): whether the method is heirarchial
+    """
+    model = None
+    is_heir = False
+    if method == 'expert':
+        pass
+    elif method == 'bc':
+        raise NotImplementedError
+    elif method == 'gail':
+        raise NotImplementedError
+    elif method == 'rail':
+        raise NotImplementedError
+    elif method == 'hgail':
+        is_heir = True
+        raise NotImplementedError
+    elif method == 'hrail':
+        is_heir = True
+        raise NotImplementedError
+    else:
+        raise NotImplementedError
+    return model, is_heir
+
+def load_expert_states(roundabout, track):
+    """
+    Load expert states from roundabout/track info
+    Args:
+        roundabout (str): roundabout name
+        track (str): track id
+    Returns:
+        expert_states (torch.tensor): (nv, T, 5) expert states for track file
+    """
+    pass
+
+def test_model(
+    locations=[], 
+    model_name='gail_image_multiagent_nocollision', 
+    env='NRasterizedRouteIncrementingAgent', 
+    method='expert',
+    options_list=ALL_OPTIONS, 
+    **env_kwargs):
+    """
+    Test a particular model at different locations/tracks
+
+    Args:
+        locations (list of tuples): list of (roundabout, track) pairs
+        model_name (str): name of model to test
+        env (str): environment class
+        method (str): method (expert, bc, gail, rail, hgail, hrail)
+        options_list (list): list of options
+    """
+
+    # load policy
+    policy, is_heir = load_model(model_name, method)
+
+    # iterate through vehicles
+    all_vehicle_infos = []
+    for i, location in tqdm(enumerate(locations)):
+        
+        # load expert states
+        expert_states = load_expert_states(roundabout, track)
+
+        # add roundabout and track to environent 
+        roundabout, track = location
+        it_env_kwargs = deepcopy(env_kwargs)
+        it_env_kwargs.update({})
+        
+        # load expert states and get average velocities
+        expert_states = load_expert_states(roundabout, track)
+        expert_vavg = torch.nanmean(expert_states[:,:,3], dim=-1)
+
+        # initialize environment
+        if not is_heir:
+            pass
+        else: 
+            pass
+        s = env.reset()
+
+        # Iterate through every vehicle and time
+        vehicle_infos, done = [], False
+        for iv in range(env.nv):
+            v_number = env.agent
+            i_vehicle_infos = {'s':[], 'a':[], 'it':[]}
+            while not done:
+                a = policy(s)
+                sp, r, done, info   = env.step(a)
+                i_vehicle_infos['s'].append(env._env.state) # FIX
+                i_vehicle_infos['a'].append(a) 
+                i_vehicle_infos['it'].append(env._env.it) # FIX
+            i_vehicle_info.update({
+                'vehicle_id': env.agent,
+                'n_steps': len(i_vehicle_infos['a']),
+                'T': len(i_vehicle_infos['a'])*env._env.dt, # FIX
+                'n_collisions': collision.check(i_vehicle_infos['s'], env._env.lengths. env._env.widths), # FIX
+                'expert_vavg': expert_vavg[env.agent]
+            })
+            vehicle_infos.append(i_vehicle_info)
+            env.reset()
+
+        all_vehicle_infos.append({
+            'loc': location,
+            'track': track,
+            'stats': vehicle_infos
+            })
+        env.close()
+
+    # print and save model-specific metrics
+    outfolder = 'test_metrics'
+    print_and_save(all_vehicle_infos, method, model, outfolder)
+
+def print_and_save(stats, method, model, outfolder):
+    """
+    Print and save stats
+    """
+    pass
+
+def load_compare():
+    pass
+
+if __name__=='__main__':
+    import fire
+    fire.Fire()
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