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

experiments/experiment.py

@@ -11,6 +11,9 @@ from ray.tune.schedulers import ASHAScheduler
 from hyperopt import hp
 from ray.tune.suggest.hyperopt import HyperOptSearch
 
+# get graphs
+import intersim
+from intersim.graphs import ConeVisibilityGraph
 
 
 from src.main import basestr, main
@@ -45,6 +48,10 @@ def parse_args():
         help='seed')    
     parser.add_argument('--nframes', default=500, type=int,
         help='frames for test animation') 
+    parser.add_argument('--graph', action='store_true',
+        help='whether to mask the relative states based on a ConeVisibilityGraph')
+    parser.add_argument('-d', default='./expert_data', type=str,
+                       help='data directory')
     args = parser.parse_args()
     kwargs = {
         'train':args.train, 
@@ -55,7 +62,11 @@ def parse_args():
         'seed':args.seed,
         'ray':args.ray,
         'nframes':args.nframes,
+        'datadir':os.path.abspath(args.d),
+        'graph':None
     }
+    if args.graph:
+        kwargs['graph'] = ConeVisibilityGraph(r=20, half_angle=120)
     return kwargs
 
 def get_full_config(ray_config:dict, method:str)->dict:
@@ -124,23 +135,21 @@ if __name__ == '__main__':
 
         def ray_train(config, datadir=None):
             full_config = get_full_config(config, kwargs['method'])
-            main(full_config, filestr='exp', datadir=datadir, **kwargs)
-        
-        datadir = os.path.abspath('./expert_data')
+            main(full_config, filestr='exp', **kwargs)
         
         ray_config = get_ray_config(kwargs['method'])
         search = HyperOptSearch(ray_config, max_concurrent=8, metric='cv_loss',mode="min",)
         custom_scheduler = ASHAScheduler(metric='cv_loss', mode="min", grace_period=15)
 
         analysis = tune.run(
-            partial(ray_train, datadir=datadir),
+            ray_train, 
             #config=ray_config,
             search_alg=search,
             scheduler=custom_scheduler,
             local_dir=outdir,
             #resources_per_trial={"cpu": 2},
             time_budget_s=120*60,
-            num_samples=100,
+            num_samples=200,
         )
     elif kwargs['ray'] and kwargs['test']:
         analysis = Analysis(outdir, default_metric="cv_loss", default_mode="min")