Bugfix: L2M2019Env seeding during __init__

Author: skbly7

envs/target/v_tgt_field.py

@@ -57,12 +57,15 @@ class VTgtField(object):
     vtgt_space = np.array([ [-10] * 2*11*11, [10] * 2*11*11 ])
 
 # -----------------------------------------------------------------------------------------------------------------
-    def __init__(self, visualize=True, version=1, dt=.01, dt_visualize=0.5):
+    def __init__(self, visualize=True, version=1, dt=.01, dt_visualize=0.5, seed=None):
         self.dt = dt
         self.visualize = visualize
         self.dt_visualize = dt_visualize
         self.di_visualize = int(dt_visualize/dt)
 
+        if seed:
+            np.random.seed(seed)
+
 # -----------------------------------------------------------------------------------------------------------------
     def reset(self, version=1, seed=None, pose_agent=np.array([0, 0, 0])):
         self.t = 0

osim/env/osim.py

@@ -459,7 +459,7 @@ def set_difficulty(self, difficulty):
             print("difficulty 3 for Round 2")
         self.spec.timestep_limit = self.time_limit    
 
-    def __init__(self, visualize=True, integrator_accuracy=5e-5, difficulty=3, seed=0, report=None):
+    def __init__(self, visualize=True, integrator_accuracy=5e-5, difficulty=3, seed=None, report=None):
         if difficulty not in [0, 1, 2, 3]:
             raise ValueError("difficulty level should be in [0, 1, 2, 3].")
         self.model_paths = {}
@@ -491,7 +491,7 @@ def __init__(self, visualize=True, integrator_accuracy=5e-5, difficulty=3, seed=
 
         # create target velocity field
         from envs.target import VTgtField
-        self.vtgt = VTgtField(visualize=visualize, version=self.difficulty, dt=self.osim_model.stepsize)
+        self.vtgt = VTgtField(visualize=visualize, version=self.difficulty, dt=self.osim_model.stepsize, seed=seed)
         self.obs_vtgt_space = self.vtgt.vtgt_space
 
     def reset(self, project=True, seed=None, init_pose=None, obs_as_dict=True):