adding options for regularization and relative state masking via interaction graphs during data processing and experiment running. found 0.002 regularization on actions gives up to 3m of deviation with no collisions. added shell script to run ray experiments overnight

src/expert_data.py

@@ -7,21 +7,28 @@ import numpy as np
 import intersim
 from intersim.utils import get_map_path, get_svt, SVT_to_stateactions
 from intersim import collisions
+from intersim.graphs import ConeVisibilityGraph
 import os
 opj = os.path.join
 
-def generate_expert_data(path: str='expert_data', loc: int = 0, track:int = 0, **kwargs):
+def generate_expert_data(path: str='expert_data', loc: int = 0, track:int = 0, 
+                            mask_relstate: bool = False, regularize_actions: bool = False, 
+                            **kwargs):
     """
     Function to save (joint) states and observations from simulated frame
     Args:
         path (str): directory to save data
         loc (int): location index
         track (int): track index
+        mask_relstate (bool): whether to mask the relative states from the cone visibility graph
+        regularize_actions (bool): whether to regularize the action selection
         kwargs: arguments for environment instantiation
     """
     
+    action_reg = 0.002 if regularize_actions else 0
+    
     if not os.path.isdir(path):
-        os.mkdir(path)
+        os.makedirs(path)
     filestr = opj(path,intersim.LOCATIONS[loc]+'_track%03i'%(track))
     
     svt, svt_path = get_svt(base='InteractionSimulator', loc=loc, track=track)
@@ -31,8 +38,13 @@ def generate_expert_data(path: str='expert_data', loc: int = 0, track:int = 0, *
     states, actions = SVT_to_stateactions(svt)
 
     # animate from environment
-    env = gym.make('intersim:intersim-v0', svt=svt, map_path=osm, **kwargs, 
-        min_acc=-np.inf, max_acc=np.inf)
+    if mask_relstate:
+        cvg = ConeVisibilityGraph(r=20, half_angle=120)
+        env = gym.make('intersim:intersim-v0', svt=svt, map_path=osm, 
+            min_acc=-np.inf, max_acc=np.inf, graph=cvg, mask_relstate=True, **kwargs)
+    else:
+        env = gym.make('intersim:intersim-v0', svt=svt, map_path=osm, **kwargs, 
+            min_acc=-np.inf, max_acc=np.inf)
     
     env.reset()
     done = False
@@ -47,7 +59,7 @@ def generate_expert_data(path: str='expert_data', loc: int = 0, track:int = 0, *
             max_devs.append(norms.max())
 
         # propagate environment
-        ob, r, done, info = env.step(env.target_state(svt.simstate[i+1]))
+        ob, r, done, info = env.step(env.target_state(svt.simstate[i+1], mu=action_reg))
         obs.append(ob)
         actions_taken.append(info['action_taken'])
         i += 1
@@ -165,9 +177,25 @@ if __name__ == '__main__':
                        help='track number (default 0)')
     parser.add_argument('--all-tracks', action='store_true',
                        help='whether to process all tracks at location')
+    parser.add_argument('--graph', action='store_true',
+                       help='whether to mask the relative states based on a ConeVisibilityGraph')
+    parser.add_argument('--reg', action='store_true',
+                       help='whether to regularize actions in the action targeter')
+    parser.add_argument('-o', default='./expert_data', type=str,
+                       help='output folder')
     args = parser.parse_args()
+
+    kwargs = {
+        'loc':args.loc, 
+        'track': args.track,
+        'path':args.o, 
+        'mask_relstate':args.graph, 
+        'regularize_actions': args.reg
+    }
+
     if args.all_tracks:
         for i in range(intersim.MAX_TRACKS):
-            generate_expert_data(loc=args.loc, track=i)
+            kwargs['track'] = i
+            generate_expert_data(**kwargs)
     else:
-        generate_expert_data(loc=args.loc,track=args.track)
+        generate_expert_data(**kwargs)