[Feature] Warn when kwargs passed to a Scenario are not used (#117)

* amend

* replace get with pop

* add check

* amend

* amend

* amend

Author: matteobettini

tests/test_scenarios/test_discovery.py

@@ -25,9 +25,7 @@ def setup_env(
 
     @pytest.mark.parametrize("n_agents", [1, 4])
     def test_heuristic(self, n_agents, n_steps=50, n_envs=4):
-        self.setup_env(
-            n_agents=n_agents, random_package_pos_on_line=False, n_envs=n_envs
-        )
+        self.setup_env(n_agents=n_agents, n_envs=n_envs)
         policy = discovery.HeuristicPolicy(True)
 
         obs = self.env.reset()

tests/test_scenarios/test_flocking.py

@@ -25,9 +25,7 @@ def setup_env(
 
     @pytest.mark.parametrize("n_agents", [1, 5])
     def test_heuristic(self, n_agents, n_steps=50, n_envs=4):
-        self.setup_env(
-            n_agents=n_agents, random_package_pos_on_line=False, n_envs=n_envs
-        )
+        self.setup_env(n_agents=n_agents, n_envs=n_envs)
         policy = flocking.HeuristicPolicy(True)
 
         obs = self.env.reset()

tests/test_scenarios/test_transport.py

@@ -53,9 +53,7 @@ def test_not_passing_through_packages(self, n_agents=1, n_envs=4):
 
     @pytest.mark.parametrize("n_agents", [6])
     def test_heuristic(self, n_agents, n_envs=4):
-        self.setup_env(
-            n_agents=n_agents, random_package_pos_on_line=False, n_envs=n_envs
-        )
+        self.setup_env(n_agents=n_agents, n_envs=n_envs)
         policy = transport.HeuristicPolicy(self.continuous_actions)
 
         obs = self.env.reset()

vmas/examples/use_vmas_env.py

@@ -31,15 +31,15 @@ def use_vmas_env(
     random_action: bool = False,
     device: str = "cpu",
     scenario_name: str = "waterfall",
-    n_agents: int = 4,
     continuous_actions: bool = True,
     visualize_render: bool = True,
+    dict_spaces: bool = True,
+    **kwargs,
 ):
     """Example function to use a vmas environment
 
     Args:
         continuous_actions (bool): Whether the agents have continuous or discrete actions
-        n_agents (int): Number of agents
         scenario_name (str): Name of scenario
         device (str): Torch device to use
         render (bool): Whether to render the scenario
@@ -48,15 +48,15 @@ def use_vmas_env(
         n_steps (int): Number of steps before returning done
         random_action (bool): Use random actions or have all agents perform the down action
         visualize_render (bool, optional): Whether to visualize the render. Defaults to ``True``.
+        dict_spaces (bool, optional): Weather to return obs, rewards, and infos as dictionaries with agent names.
+            By default, they are lists of len # of agents
+        kwargs (dict, optional): Keyword arguments to pass to the scenario
 
     Returns:
 
     """
     assert not (save_render and not render), "To save the video you have to render it"
 
-    dict_spaces = True  # Weather to return obs, rewards, and infos as dictionaries with agent names
-    # (by default they are lists of len # of agents)
-
     env = make_env(
         scenario=scenario_name,
         num_envs=num_envs,
@@ -66,7 +66,7 @@ def use_vmas_env(
         wrapper=None,
         seed=None,
         # Environment specific variables
-        n_agents=n_agents,
+        **kwargs,
     )
 
     frame_list = []  # For creating a gif
@@ -121,4 +121,6 @@ def use_vmas_env(
         save_render=False,
         random_action=False,
         continuous_actions=False,
+        # Environment specific
+        n_agents=4,
     )

vmas/scenarios/balance.py

@@ -8,14 +8,15 @@
 from vmas.simulator.core import Agent, Box, Landmark, Line, Sphere, World
 from vmas.simulator.heuristic_policy import BaseHeuristicPolicy
 from vmas.simulator.scenario import BaseScenario
-from vmas.simulator.utils import Color, Y
+from vmas.simulator.utils import Color, ScenarioUtils, Y
 
 
 class Scenario(BaseScenario):
     def make_world(self, batch_dim: int, device: torch.device, **kwargs):
-        self.n_agents = kwargs.get("n_agents", 3)
-        self.package_mass = kwargs.get("package_mass", 5)
-        self.random_package_pos_on_line = kwargs.get("random_package_pos_on_line", True)
+        self.n_agents = kwargs.pop("n_agents", 3)
+        self.package_mass = kwargs.pop("package_mass", 5)
+        self.random_package_pos_on_line = kwargs.pop("random_package_pos_on_line", True)
+        ScenarioUtils.check_kwargs_consumed(kwargs)
 
         assert self.n_agents > 1
 

vmas/scenarios/ball_passage.py

@@ -10,14 +10,15 @@
 from vmas import render_interactively
 from vmas.simulator.core import Agent, Box, Landmark, Line, Sphere, World
 from vmas.simulator.scenario import BaseScenario
-from vmas.simulator.utils import Color, X, Y
+from vmas.simulator.utils import Color, ScenarioUtils, X, Y
 
 
 class Scenario(BaseScenario):
     def make_world(self, batch_dim: int, device: torch.device, **kwargs):
-        self.n_passages = kwargs.get("n_passages", 1)
-        self.fixed_passage = kwargs.get("fixed_passage", False)
-        self.random_start_angle = kwargs.get("random_start_angle", True)
+        self.n_passages = kwargs.pop("n_passages", 1)
+        self.fixed_passage = kwargs.pop("fixed_passage", False)
+        self.random_start_angle = kwargs.pop("random_start_angle", True)
+        ScenarioUtils.check_kwargs_consumed(kwargs)
 
         assert 1 <= self.n_passages <= 20
 

vmas/scenarios/ball_trajectory.py

@@ -10,15 +10,16 @@
 from vmas.simulator.core import Agent, Landmark, Sphere, World
 from vmas.simulator.joints import Joint
 from vmas.simulator.scenario import BaseScenario
-from vmas.simulator.utils import Color, JOINT_FORCE, X
+from vmas.simulator.utils import Color, JOINT_FORCE, ScenarioUtils, X
 
 
 class Scenario(BaseScenario):
     def make_world(self, batch_dim: int, device: torch.device, **kwargs):
-        self.pos_shaping_factor = kwargs.get("pos_shaping_factor", 0)
-        self.speed_shaping_factor = kwargs.get("speed_shaping_factor", 1)
-        self.dist_shaping_factor = kwargs.get("dist_shaping_factor", 0)
-        self.joints = kwargs.get("joints", True)
+        self.pos_shaping_factor = kwargs.pop("pos_shaping_factor", 0)
+        self.speed_shaping_factor = kwargs.pop("speed_shaping_factor", 1)
+        self.dist_shaping_factor = kwargs.pop("dist_shaping_factor", 0)
+        self.joints = kwargs.pop("joints", True)
+        ScenarioUtils.check_kwargs_consumed(kwargs)
 
         self.n_agents = 2
 

vmas/scenarios/buzz_wire.py

@@ -10,15 +10,16 @@
 from vmas.simulator.core import Agent, Landmark, Line, Sphere, World
 from vmas.simulator.joints import Joint
 from vmas.simulator.scenario import BaseScenario
-from vmas.simulator.utils import Color
+from vmas.simulator.utils import Color, ScenarioUtils
 
 
 class Scenario(BaseScenario):
     def make_world(self, batch_dim: int, device: torch.device, **kwargs):
-        self.random_start_angle = kwargs.get("random_start_angle", True)
-        self.pos_shaping_factor = kwargs.get("pos_shaping_factor", 1)
-        self.collision_reward = kwargs.get("collision_reward", -10)
-        self.max_speed_1 = kwargs.get("max_speed_1", None)  # 0.05
+        self.random_start_angle = kwargs.pop("random_start_angle", True)
+        self.pos_shaping_factor = kwargs.pop("pos_shaping_factor", 1)
+        self.collision_reward = kwargs.pop("collision_reward", -10)
+        self.max_speed_1 = kwargs.pop("max_speed_1", None)  # 0.05
+        ScenarioUtils.check_kwargs_consumed(kwargs)
 
         self.pos_shaping_factor = 1
 

vmas/scenarios/debug/asym_joint.py

@@ -12,7 +12,7 @@
 from vmas.simulator.core import Agent, Landmark, Sphere, World
 from vmas.simulator.joints import Joint
 from vmas.simulator.scenario import BaseScenario
-from vmas.simulator.utils import Color
+from vmas.simulator.utils import Color, ScenarioUtils
 
 if typing.TYPE_CHECKING:
     from vmas.simulator.rendering import Geom
@@ -47,19 +47,21 @@ def angle_to_vector(angle):
 
 class Scenario(BaseScenario):
     def make_world(self, batch_dim: int, device: torch.device, **kwargs):
-        self.joint_length = kwargs.get("joint_length", 0.5)
-        self.random_start_angle = kwargs.get("random_start_angle", False)
-        self.observe_joint_angle = kwargs.get("observe_joint_angle", False)
-        self.joint_angle_obs_noise = kwargs.get("joint_angle_obs_noise", 0.0)
-        self.asym_package = kwargs.get("asym_package", True)
-        self.mass_ratio = kwargs.get("mass_ratio", 5)
-        self.mass_position = kwargs.get("mass_position", 0.75)
-        self.max_speed_1 = kwargs.get("max_speed_1", None)  # 0.1
-        self.obs_noise = kwargs.get("obs_noise", 0.2)
+        self.joint_length = kwargs.pop("joint_length", 0.5)
+        self.random_start_angle = kwargs.pop("random_start_angle", False)
+        self.observe_joint_angle = kwargs.pop("observe_joint_angle", False)
+        self.joint_angle_obs_noise = kwargs.pop("joint_angle_obs_noise", 0.0)
+        self.asym_package = kwargs.pop("asym_package", True)
+        self.mass_ratio = kwargs.pop("mass_ratio", 5)
+        self.mass_position = kwargs.pop("mass_position", 0.75)
+        self.max_speed_1 = kwargs.pop("max_speed_1", None)  # 0.1
+        self.obs_noise = kwargs.pop("obs_noise", 0.2)
 
         # Reward
-        self.rot_shaping_factor = kwargs.get("rot_shaping_factor", 1)
-        self.energy_reward_coeff = kwargs.get("energy_reward_coeff", 0.08)
+        self.rot_shaping_factor = kwargs.pop("rot_shaping_factor", 1)
+        self.energy_reward_coeff = kwargs.pop("energy_reward_coeff", 0.08)
+
+        ScenarioUtils.check_kwargs_consumed(kwargs)
 
         # Make world
         world = World(

vmas/scenarios/debug/circle_trajectory.py

@@ -10,17 +10,18 @@
 from vmas.simulator.controllers.velocity_controller import VelocityController
 from vmas.simulator.core import Agent, Sphere, World
 from vmas.simulator.scenario import BaseScenario
-from vmas.simulator.utils import Color, TorchUtils, X, Y
+from vmas.simulator.utils import Color, ScenarioUtils, TorchUtils, X, Y
 
 
 class Scenario(BaseScenario):
     def make_world(self, batch_dim: int, device: torch.device, **kwargs):
-        self.u_range = kwargs.get("u_range", 1)
-        self.a_range = kwargs.get("a_range", 1)
-        self.obs_noise = kwargs.get("obs_noise", 0.0)
-        self.dt_delay = kwargs.get("dt_delay", 0)
-        self.min_input_norm = kwargs.get("min_input_norm", 0.08)
-        self.linear_friction = kwargs.get("linear_friction", 0.1)
+        self.u_range = kwargs.pop("u_range", 1)
+        self.a_range = kwargs.pop("a_range", 1)
+        self.obs_noise = kwargs.pop("obs_noise", 0.0)
+        self.dt_delay = kwargs.pop("dt_delay", 0)
+        self.min_input_norm = kwargs.pop("min_input_norm", 0.08)
+        self.linear_friction = kwargs.pop("linear_friction", 0.1)
+        ScenarioUtils.check_kwargs_consumed(kwargs)
 
         self.agent_radius = 0.16
         self.desired_radius = 1.5