Update tutorials

tutorials/envs/__init__.py

@@ -1,7 +1,7 @@
 
 # import envs and necessary gym packages
 from envs.simple_env import SimpleWalkerEnvClass
-from gym.envs.registration import register
+from gymnasium.envs.registration import register
 
 # register the env using gym's interface
 register(

tutorials/envs/simple_env.py

@@ -1,27 +1,34 @@
-from gym import spaces
+from gymnasium import spaces
 from evogym import EvoWorld
 from evogym.envs import EvoGymBase
 
+from typing import Optional, Dict, Any, Tuple
 import numpy as np
 import os
 
 class SimpleWalkerEnvClass(EvoGymBase):
 
-    def __init__(self, body, connections=None):
+    def __init__(
+        self,
+        body: np.ndarray,
+        connections: Optional[np.ndarray] = None,
+        render_mode: Optional[str] = None,
+        render_options: Optional[Dict[str, Any]] = None,
+    ):
 
         # make world
         self.world = EvoWorld.from_json(os.path.join('world_data', 'simple_walker_env.json'))
         self.world.add_from_array('robot', body, 1, 1, connections=connections)
 
         # init sim
-        EvoGymBase.__init__(self, self.world)
+        EvoGymBase.__init__(self, world=self.world, render_mode=render_mode, render_options=render_options)
 
         # set action space and observation space
         num_actuators = self.get_actuator_indices('robot').size
-        obs_size = self.reset().size
+        obs_size = self.reset()[0].size
 
-        self.action_space = spaces.Box(low= 0.6, high=1.6, shape=(num_actuators,), dtype=np.float)
-        self.observation_space = spaces.Box(low=-100.0, high=100.0, shape=(obs_size,), dtype=np.float)
+        self.action_space = spaces.Box(low= 0.6, high=1.6, shape=(num_actuators,), dtype=float)
+        self.observation_space = spaces.Box(low=-100.0, high=100.0, shape=(obs_size,), dtype=float)
 
         # set viewer to track objects
         self.default_viewer.track_objects('robot')
@@ -58,17 +65,17 @@ def step(self, action):
             self.get_relative_pos_obs("robot"),
             ))
 
-        # observation, reward, has simulation met termination conditions, debugging info
-        return obs, reward, done, {}
+        # observation, reward, has simulation met termination conditions, truncated, debugging info
+        return obs, reward, done, False, {}
 
-    def reset(self):
+    def reset(self, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) -> Tuple[np.ndarray, Dict[str, Any]]:
 
-        super().reset()
+        super().reset(seed=seed, options=options)
 
         # observation
         obs = np.concatenate((
             self.get_vel_com_obs("robot"),
             self.get_relative_pos_obs("robot"),
             ))
 
-        return obs
+        return obs, {}

tutorials/rendering_options.py

@@ -2,6 +2,7 @@
 import os
 import numpy as np
 import cv2
+import argparse
 
 ### CREATE A SIMPLE ENVIRONMENT ###
 
@@ -27,13 +28,18 @@
 
 ### SELECT A RENDERING OPTION ###
 
-options = ['to-debug-screen', 'to-numpy-array', 'special-options', 'very-fast']
-option = options[0]
-
-print(f'\nUsing rendering option {option}...\n')
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    '--render-option', 
+    choices=['to-debug-screen', 'to-numpy-array', 'special-options', 'very-fast'],
+    help='Select a rendering option from: to-debug-screen, to-numpy-array, special-options, very-fast',
+    default='to-debug-screen',
+)
+args = parser.parse_args()
+print(f'\nUsing rendering option {args.render_option}...\n')
 
 # if the 'very-fast' option is chosen, set the rendering speed to be unlimited
-if option == 'very-fast':
+if args.render_option == 'very-fast':
     viewer.set_target_rps(None)
 
 for i in range(1000):
@@ -48,19 +54,19 @@
     sim.step()
 
     # step and render to a debug screen
-    if option == 'to-debug-screen':
+    if args.render_option == 'to-debug-screen':
         viewer.render('screen')
 
     # step and render to a numpy array
     # use open cv to visualize output
-    if option == 'to-numpy-array':
+    if args.render_option == 'to-numpy-array':
         img = viewer.render('img')
         img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
         cv2.waitKey(1)
         cv2.imshow("Open CV Window", img)
 
     # rendering with more options
-    if option == 'special-options':   
+    if args.render_option == 'special-options':   
         img = viewer.render(
             'screen', 
             verbose = True,
@@ -70,7 +76,8 @@
             hide_voxels = False)
 
     # rendering as fast as possible
-    if option == 'very-fast':
+    if args.render_option == 'very-fast':
         viewer.render('screen', verbose=True)
 
 cv2.destroyAllWindows()
+viewer.close()

tutorials/visualize_simple_env.py

@@ -1,4 +1,4 @@
-import gym
+import gymnasium as gym
 from evogym import sample_robot
 
 # import envs from the envs folder and register them
@@ -10,17 +10,21 @@
     body, connections = sample_robot((5,5))
 
     # make the SimpleWalkingEnv using gym.make and with the robot information
-    env = gym.make('SimpleWalkingEnv-v0', body=body)
+    env = gym.make(
+        'SimpleWalkingEnv-v0',
+        body=body,
+        render_mode='human',
+        render_options={'verbose': True}
+    )
     env.reset()
 
     # step the environment for 500 iterations
     for i in range(500):
 
         action = env.action_space.sample()
-        ob, reward, done, info = env.step(action)
-        env.render(verbose=True)
+        ob, reward, terminated, truncated, info = env.step(action)
 
-        if done:
+        if terminated or truncated:
             env.reset()
 
     env.close()