Enable GUI rollout

Signed-off-by: Arjo Chakravarty <[email protected]>

examples/scripts/reinforcement_learning/simple_cart_pole/README.md

@@ -1,6 +1,6 @@
 # Example for Reinforcement Learning (RL) With Gazebo
 
-This demo world shows you an example of how you can use SDFormat, Ray-RLLIB and Gazebo to perform RL with python.
+This demo world shows you an example of how you can use SDFormat, Stable Baselines 3 and Gazebo to perform RL with python.
 We start with a very simple cart-pole world. This world is defined in our sdf file `cart_pole.sdf`. It is analogous to
 the
 
@@ -10,7 +10,7 @@ First create a virtual environment using python,
 ```
 python3 -m venv venv
 ```
-Lets activate it and install rayrllib and pytorch.
+Lets activate it and install stablebaselines3 and pytorch.
 ```
 . venv/bin/activate
 ```

examples/scripts/reinforcement_learning/simple_cart_pole/cart_pole_env.py

@@ -4,7 +4,7 @@
 import numpy as np
 
 from gz.common6 import set_verbosity
-from gz.sim9 import TestFixture, World, world_entity, Model, Link, run_gui
+from gz.sim10 import TestFixture, World, world_entity, Model, Link, get_install_prefix
 from gz.math8 import Vector3d
 from gz.transport14 import Node
 from gz.msgs11.world_control_pb2 import WorldControl
@@ -13,9 +13,17 @@
 
 from stable_baselines3 import PPO
 import time
+import subprocess
 
 file_path = os.path.dirname(os.path.realpath(__file__))
 
+def run_gui():
+    if os.name == 'nt':
+        base = os.path.join(get_install_prefix(), "libexec", "runGui.exe")
+    else:
+        base = os.path.join(get_install_prefix(), "libexec", "runGui")
+    subprocess.Popen(base)
+
 class GzRewardScorer:
     def __init__(self):
         self.fixture = TestFixture(os.path.join(file_path, 'cart_pole.sdf'))
@@ -99,13 +107,13 @@ def step(self, action):
         obs, reward, done, truncated, info = self.env.step(action)
         return  obs, reward, done, truncated, info
 
-
 env = CustomCartPole({})
 model = PPO("MlpPolicy", env, verbose=1)
 model.learn(total_timesteps=25_000)
 
 vec_env = model.get_env()
 obs = vec_env.reset()
+
 run_gui()
 time.sleep(10)
 for i in range(50000):