Fix docs and tests

docs/graphics/index.md

@@ -9,11 +9,13 @@ Environment rendering is done with [pygame](https://www.pygame.org/news), which
 A window is created at the first call of `env.render()`. Its dimensions can be configured:
 
 ```python
-env = gym.make("roundabout-v0")
-env.configure({
-    "screen_width": 640,
-    "screen_height": 480
-})
+env = gym.make(
+    "roundabout-v0",
+    config={
+        "screen_width": 640,
+        "screen_height": 480
+    }
+)
 env.reset()
 env.render()
 ```

docs/make_your_own.md

@@ -43,8 +43,8 @@ See {ref}`vehicle behaviors <vehicle_behavior>` for reference, and existing envi
 
 To make a part of your environment configurable, overload the {py:meth}`~highway_env.envs.common.abstract.AbstractEnv.default_config`
 method to define new `{"config_key": value}` pairs with default values. These configurations then be accessed in your
-environment implementation with `self.config["config_key"]`, and once the environment is created, it can be configured with
-`env.configure({"config_key": other_value})` followed by `env.reset()`.
+environment implementation with `self.unwrapped.config["config_key"]`, and once the environment is created, it can be configured with
+`env.unwrapped.config["config_key"] = other_value` followed by `env.reset()`.
 
 ## Register the environment
 

docs/multi_agent.md

@@ -11,12 +11,17 @@ To that end, update the {ref}`environment configuration <Configuring an environm
 ```{eval-rst}
 .. jupyter-execute::
 
-  import gymnasium as gym
-
-  env = gym.make('highway-v0', render_mode='rgb_array')
-
-  env.configure({"controlled_vehicles": 2})  # Two controlled vehicles
-  env.configure({"vehicles_count": 1})  # A single other vehicle, for the sake of visualisation
+  import gymnasium
+  import highway_env
+
+  env = gymnasium.make(
+    "highway-v0",
+    render_mode="rgb_array",
+    config={
+      "controlled_vehicles": 2,  # Two controlled vehicles
+      "vehicles_count": 1,       # A single other vehicle, for the sake of visualisation
+    }
+  )
   env.reset(seed=0)
 
   from matplotlib import pyplot as plt
@@ -35,7 +40,7 @@ The type of actions contained in the tuple must be described by a standard {ref}
 ```{eval-rst}
 .. jupyter-execute::
 
-  env.configure({
+  env.unwrapped.config.update({
     "action": {
       "type": "MultiAgentAction",
       "action_config": {
@@ -70,14 +75,18 @@ The type of observations contained in the tuple must be described by a standard
 ```{eval-rst}
 .. jupyter-execute::
 
-  env.configure({
-    "observation": {
-      "type": "MultiAgentObservation",
-      "observation_config": {
-        "type": "Kinematics",
+  env = gymnasium.make(
+    "highway-v0",
+    render_mode="rgb_array",
+    config={
+      "observation": {
+        "type": "MultiAgentObservation",
+        "observation_config": {
+          "type": "Kinematics",
+        }
       }
     }
-  })
+  )
   obs, info = env.reset()
 
   import pprint
@@ -92,7 +101,7 @@ Here is a pseudo-code example of how a centralized multi-agent policy could be t
 .. jupyter-execute::
 
   # Multi-agent environment configuration
-  env.configure({
+  env.unwrapped.config.update({
     "controlled_vehicles": 2,
     "observation": {
       "type": "MultiAgentObservation",

docs/observations/index.md

@@ -13,23 +13,25 @@ Each environment comes with a *default* observation, which can be changed or cus
 import gymnasium as gym
 import highway_env
 
-env = gym.make('highway-v0')
-env.configure({
-    "observation": {
-        "type": "OccupancyGrid",
-        "vehicles_count": 15,
-        "features": ["presence", "x", "y", "vx", "vy", "cos_h", "sin_h"],
-        "features_range": {
-            "x": [-100, 100],
-            "y": [-100, 100],
-            "vx": [-20, 20],
-            "vy": [-20, 20]
-        },
-        "grid_size": [[-27.5, 27.5], [-27.5, 27.5]],
-        "grid_step": [5, 5],
-        "absolute": False
+env = gym.make(
+    'highway-v0', 
+    config={
+        "observation": {
+            "type": "OccupancyGrid",
+            "vehicles_count": 15,
+            "features": ["presence", "x", "y", "vx", "vy", "cos_h", "sin_h"],
+            "features_range": {
+                "x": [-100, 100],
+                "y": [-100, 100],
+                "vx": [-20, 20],
+                "vy": [-20, 20]
+            },
+            "grid_size": [[-27.5, 27.5], [-27.5, 27.5]],
+            "grid_step": [5, 5],
+            "absolute": False
+        }
     }
-})
+)
 env.reset()
 ```
 
@@ -124,8 +126,7 @@ vehicle and 0 for placeholders.
             "order": "sorted"
         }
     }
-    env = gym.make('highway-v0')
-    env.configure(config)
+    env = gym.make('highway-v0', config=config)
     obs, info = env.reset()
     print(obs)
 
@@ -146,7 +147,7 @@ The RGB to grayscale conversion is a weighted sum, configured by the `weights` p
 
     from matplotlib import pyplot as plt
     %matplotlib inline
- config = {
+    config = {
         "observation": {
             "type": "GrayscaleObservation",
             "observation_shape": (128, 64),
@@ -156,7 +157,7 @@ The RGB to grayscale conversion is a weighted sum, configured by the `weights` p
         },
         "policy_frequency": 2
     }
-    env.configure(config)
+    env = gym.make('highway-v0', config=config)
     obs, info = env.reset()
 
     fig, axes = plt.subplots(ncols=4, figsize=(12, 5))
@@ -173,7 +174,7 @@ We illustrate the stack update by performing three steps in the environment.
 .. jupyter-execute::
 
     for _ in range(3):
-        obs, reward, done, truncated, info = env.step(env.action_type.actions_indexes["IDLE"])
+        obs, reward, done, truncated, info = env.step(env.unwrapped.action_type.actions_indexes["IDLE"])
 
         fig, axes = plt.subplots(ncols=4, figsize=(12, 5))
         for i, ax in enumerate(axes.flat):

docs/quickstart.md

@@ -8,14 +8,15 @@ Here is a quick example of how to create an environment:
 ```{eval-rst}
 .. jupyter-execute::
 
-  import gymnasium as gym
+  import gymnasium
+  import highway_env
   from matplotlib import pyplot as plt
   %matplotlib inline
 
-  env = gym.make('highway-v0', render_mode='rgb_array')
+  env = gymnasium.make('highway-v0', render_mode='rgb_array')
   env.reset()
   for _ in range(3):
-      action = env.action_type.actions_indexes["IDLE"]
+      action = env.unwrapped.action_type.actions_indexes["IDLE"]
       obs, reward, done, truncated, info = env.step(action)
       env.render()
 
@@ -53,21 +54,33 @@ After environment creation, the configuration can be accessed using the
 
   import pprint
 
-  env = gym.make("highway-v0", render_mode='rgb_array')
-  pprint.pprint(env.config)
+  env = gymnasium.make("highway-v0", render_mode='rgb_array')
+  pprint.pprint(env.unwrapped.config)
 ```
 
 For example, the number of lanes can be changed with:
 
 ```{eval-rst}
 .. jupyter-execute::
 
-  env.config["lanes_count"] = 2
+  env.unwrapped.config["lanes_count"] = 2
   env.reset()
   plt.imshow(env.render())
   plt.show()
 ```
 
+or directly at creation time with:
+
+```{eval-rst}
+.. jupyter-execute::
+
+  env = gymnasium.make(
+    "highway-v0",
+    render_mode='rgb_array',
+    config={"lanes_count": 2}
+  )
+```
+
 ```{note}
 The environment must be {py:meth}`~highway_env.envs.common.abstract.AbstractEnv.reset` for the change of configuration
 to be effective.
@@ -87,11 +100,11 @@ Here is an example of SB3's DQN implementation trained on `highway-fast-v0` with
 [![Colab][colab-badge]][highway_dqn]
 
 ```python
-import gymnasium as gym
+import gymnasium
 import highway_env
 from stable_baselines3 import DQN
 
-env = gym.make("highway-fast-v0")
+env = gymnasium.make("highway-fast-v0")
 model = DQN('MlpPolicy', env,
               policy_kwargs=dict(net_arch=[256, 256]),
               learning_rate=5e-4,

scripts/sb3_highway_dqn.py

@@ -40,7 +40,7 @@
         env, video_folder="highway_dqn/videos", episode_trigger=lambda e: True
     )
     env.unwrapped.set_record_video_wrapper(env)
-    env.configure({"simulation_frequency": 15})  # Higher FPS for rendering
+    env.unwrapped.config["simulation_frequency"] = 15  # Higher FPS for rendering
 
     for videos in range(10):
         done = truncated = False

scripts/sb3_highway_dqn_cnn.py

@@ -6,9 +6,9 @@
 
 
 def train_env():
-    env = gym.make("highway-fast-v0")
-    env.configure(
-        {
+    env = gym.make(
+        "highway-fast-v0",
+        config={
             "observation": {
                 "type": "GrayscaleObservation",
                 "observation_shape": (128, 64),
@@ -24,7 +24,7 @@ def train_env():
 
 def test_env():
     env = train_env()
-    env.configure({"policy_frequency": 15, "duration": 20})
+    env.unwrapped.config.update({"policy_frequency": 15, "duration": 20})
     env.reset()
     return env
 

scripts/sb3_highway_ppo_transformer.py

@@ -272,8 +272,7 @@ def forward(self, observations: th.Tensor) -> th.Tensor:
 
 
 def make_configure_env(**kwargs):
-    env = gym.make(kwargs["id"])
-    env.configure(kwargs["config"])
+    env = gym.make(kwargs["id"], config=kwargs["config"])
     env.reset()
     return env
 

tests/envs/test_actions.py

@@ -15,8 +15,7 @@
     ],
 )
 def test_action_type(action_config):
-    env = gym.make("highway-v0").unwrapped
-    env.configure({"action": action_config})
+    env = gym.make("highway-v0", config={"action": action_config})
     env.reset()
     for _ in range(3):
         action = env.action_space.sample()

tests/graphics/test_render.py

@@ -10,7 +10,7 @@
 @pytest.mark.parametrize("env_spec", ["highway-v0", "merge-v0"])
 def test_render(env_spec):
     env = gym.make(env_spec, render_mode="rgb_array").unwrapped
-    env.configure({"offscreen_rendering": True})
+    env.config.update({"offscreen_rendering": True})
     env.reset()
     img = env.render()
     env.close()
@@ -25,7 +25,7 @@ def test_render(env_spec):
 @pytest.mark.parametrize("env_spec", ["highway-v0", "merge-v0"])
 def test_obs_grayscale(env_spec, stack_size=4):
     env = gym.make(env_spec).unwrapped
-    env.configure(
+    env.config.update(
         {
             "offscreen_rendering": True,
             "observation": {