diff --git a/ergodic_search/erg_metric.py b/ergodic_search/erg_metric.py
index 62efb47..f81a1c6 100644
--- a/ergodic_search/erg_metric.py
+++ b/ergodic_search/erg_metric.py
@@ -150,9 +150,13 @@ def update_pdf(self, pdf, fourier_freqs=None, freq_wts=None):
         else:
 
             if fourier_freqs is not None:
+                if not isinstance(fourier_freqs, torch.Tensor):
+                    fourier_freqs = torch.tensor(fourier_freqs)
                 self.fourier_freqs = fourier_freqs
             
             if freq_wts is not None:
+                if not isinstance(freq_wts, torch.Tensor):
+                    freq_wts = torch.tensor(freq_wts)
                 self.freq_wts = freq_wts
             
             self.pdf = pdf
@@ -248,6 +252,8 @@ def get_lambdak(self, k, freq_wts=None):
             # however MOES uses (-4/2) and that seems to produce better results, at least for this implementation
             lambdak = (1. + torch.linalg.norm(k / torch.pi, dim=1)**2)**(-4./2.)
         else:
+            if not isinstance(freq_wts, torch.Tensor):
+                freq_wts = torch.tensor(freq_wts)
             lambdak = freq_wts
         return lambdak
 
diff --git a/ergodic_search/erg_planner.py b/ergodic_search/erg_planner.py
index 5deeb8a..19a7bd8 100644
--- a/ergodic_search/erg_planner.py
+++ b/ergodic_search/erg_planner.py
@@ -34,6 +34,7 @@ def ErgArgs():
     parser.add_argument('--debug', action='store_true', help='Whether to print loss components for debugging')
     parser.add_argument('--outpath', type=str, help='File path to save images to, None displays them in a window', default=None)
     parser.add_argument('--replan_type', type=str, default='full', help='Type of replanning to perform (accepts partial or full)')
+    parser.add_argument('--seed', type=str, default=687456, help='Seed to set for PyTorch stochastic optimization')
     args = parser.parse_args()
     print(args)
 
@@ -63,6 +64,9 @@ def __init__(self, args, pdf=None, init_controls=None, dyn_model=None, fourier_f
         self.args = args
         self.pdf = pdf
 
+        # set seed
+        torch.random.manual_seed(args.seed)
+
         # get device
         self.device = torch.device("cuda") if args.gpu else torch.device("cpu")
 
diff --git a/example_replanning.py b/example_replanning.py
index d7c772e..28265b3 100644
--- a/example_replanning.py
+++ b/example_replanning.py
@@ -100,6 +100,6 @@ def create_map(dim):
             planner.update_pdf(new_map)
 
         # "take a step" along the trajectory
-        # this will increment the controls such that the planner will start at the first point in the trajectory and 
+        # this will increment the controls such that the planner will start at the first point in the trajectory
         planner.take_step()