updating function to process all expert data from track files, starting processing options policy from file

scratch/arec/intersimple/data/expert.py

@@ -111,9 +111,7 @@ def demonstrations(expert='NormalizedIntersimpleExpert', env='NRasterizedIncreme
         env_args (dict): dictionary of kwargs when instantiating environment class
         policy_args (dict): dictionary of kwargs when instantiating Expert policy
     """
-    import pdb
-    pdb.set_trace()
-
+    
     Env = intersim.envs.intersimple.__dict__[env]
     Expert = globals()[expert]
 

scratch/arec/intersimple/data/generate.sh

@@ -5,4 +5,5 @@
 # python -m expert --env=NRasterizedRandomAgent --min_timesteps=10000 --env_args='{width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N10000_NRasterizedRandomAgentw36h36mppx2.pkl'
 #python -m expert --env=NRasterized --min_timesteps=200 --env_args='{agent:51,width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl'
 #python -m expert --env=NRasterized --min_timesteps=3000 --video --env_args='{width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001_NRasterizedRandomAgentw36h36mppx2.pkl'
-python -m expert --env=NRasterizedIncrementingAgent --env_args='{width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001_NRasterizedIncrementingAgentw36h36mppx2.pkl'
+#python -m expert --env=NRasterizedIncrementingAgent --env_args='{width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001_NRasterizedIncrementingAgentw36h36mppx2.pkl'
+python -m process_all_experts --env_args='{width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}'

scratch/arec/intersimple/data/process_all_experts.py

@@ -1,28 +1,34 @@
 import tqdm
 import expert
 import copy
-import sys, os
+import os
+import intersim
+from tqdm import tqdm
 
-def process_all_experts(env_args={}, policy_args={}):
+def process_all_experts(filename='expert.pkl',env_args={}, policy_args={}):
     """
     Process all experts in the Interaction Dataset
     For now, using NormalizedIntersimpleExpert with NRasterizedIncrementingAgent environment
 
     Args:
+        filename (str): name for track file
         env_args (dict): default environment kwargs
         policy_args (dict): default policy kwargs
     """
-
-    for loc in LOCATIONS:
-        for track in TRACKS:
+    I, J = len(intersim.LOCATIONS), intersim.MAX_TRACKS
+    pbar = tqdm(total=I*J)
+    for loc in range(I):
+        for track in range(J):
             
             it_env_args = copy.deepcopy(env_args)
             it_env_args.update({
                 'loc':loc,
                 'track':track,
             })
-
-            it_path = 'newpathname'
+            out_folder = os.path.join(intersim.LOCATIONS[loc], 'track%04i'%(track))
+            if not os.path.isdir(out_folder):
+                os.makedirs(out_folder) 
+            it_path = os.path.join(out_folder,filename)
 
             expert.demonstrations(
                 expert='NormalizedIntersimpleExpert', 
@@ -31,6 +37,8 @@ def process_all_experts(env_args={}, policy_args={}):
                 env_args=it_env_args, 
                 policy_args=policy_args,
                 )
+            pbar.update(1)
+    pbar.close()
     
 
 if __name__=='__main__':

scratch/arec/intersimple/render_env_from_model.py

@@ -28,6 +28,31 @@ def render_env(model_name='gail_image_multiagent_nocollision', agent=51, environ
 
     env.close(filestr='render/'+model_name+'_agent%i'%(agent)) 
 
+def render_options_env(model_name='gail_image_multiagent_nocollision', agent=51, environment=NRasterized):
+    """
+    Render a video from an model, agent, and environment
+    Args:
+        model_name (str): name of the model
+        agent (int): agent to start the video from
+        environment (gym.Env): gym environment class to render environment on
+    """
+
+    model = sb3.PPO.load(model_name)
+
+    env = environment(stop_on_collision=False, width=36, height=36, m_per_px=2, agent=agent)
+
+    obs = env.reset()
+    i=0
+    while True and i < 600:
+        i+=1
+        action, _states = model.predict(obs)
+        obs, rewards, done, info = env.step(action)
+        env.render(mode='post')
+        if done:
+            break
+
+    env.close(filestr='render/'+model_name+'_agent%i'%(agent)) 
+
 if __name__ == '__main__':
     import fire
     fire.Fire(render_env)