updated for new mp-pytorch version

fancy_gym/examples/examples_movement_primitives.py

@@ -33,7 +33,7 @@ def example_mp(env_name="HoleReacherProMP-v0", seed=1, iterations=1, render=True
             # Just make sure the correct mode is set before executing the step.
             env.render(mode="human")
         else:
-            env.render(mode=None)
+            env.render()
 
         # Now the action space is not the raw action but the parametrization of the trajectory generator,
         # such as a ProMP
@@ -155,7 +155,7 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
 
 
 if __name__ == '__main__':
-    render = True
+    render = False
     # DMP
     example_mp("HoleReacherDMP-v0", seed=10, iterations=5, render=render)
 

fancy_gym/examples/examples_open_ai.py

@@ -22,7 +22,7 @@ def example_mp(env_name, seed=1, render=True):
         if render and i % 2 == 0:
             env.render(mode="human")
         else:
-            env.render(mode=None)
+            env.render()
         ac = env.action_space.sample()
         obs, reward, done, info = env.step(ac)
         returns += reward