[adroit ]Minor Cleanup: assignment of goal_achieved

gymnasium_robotics/envs/adroit_hand/adroit_door.py

@@ -282,7 +282,7 @@ def step(self, a):
 
         # compute the sparse reward variant first
         goal_distance = self.data.qpos[self.door_hinge_addrs]
-        goal_achieved = True if goal_distance >= 1.35 else False
+        goal_achieved = goal_distance >= 1.35
         reward = 10.0 if goal_achieved else -0.1
 
         # override reward if not sparse reward

gymnasium_robotics/envs/adroit_hand/adroit_hammer.py

@@ -296,7 +296,7 @@ def step(self, a):
 
         # compute the sparse reward variant first
         goal_distance = np.linalg.norm(nail_pos - goal_pos)
-        goal_achieved = True if goal_distance < 0.01 else False
+        goal_achieved = goal_distance < 0.01
         reward = 10.0 if goal_achieved else -0.1
 
         # override reward if not sparse reward

gymnasium_robotics/envs/adroit_hand/adroit_pen.py

@@ -301,9 +301,7 @@ def step(self, a):
         # compute the sparse reward variant first
         goal_distance = np.linalg.norm(obj_pos - desired_loc)
         orien_similarity = np.dot(obj_orien, desired_orien)
-        goal_achieved = (
-            True if (goal_distance < 0.075 and orien_similarity > 0.95) else False
-        )
+        goal_achieved = goal_distance < 0.075 and orien_similarity > 0.95
         reward = 10.0 if goal_achieved else -0.1
 
         # goal_failed = obj_pos[2] < 0.075

gymnasium_robotics/envs/adroit_hand/adroit_relocate.py

@@ -286,7 +286,7 @@ def step(self, a):
 
         # compute the sparse reward variant first
         goal_distance = float(np.linalg.norm(obj_pos - target_pos))
-        goal_achieved = True if goal_distance < 0.1 else False
+        goal_achieved = goal_distance < 0.1
         reward = 10.0 if goal_achieved else -0.1
 
         # override reward if not sparse reward