[Feature] Allow multiple observation keys in specs

benchmarl/algorithms/common.py

@@ -67,12 +67,6 @@ def _check_specs(self):
                     "you can apply a transform to your environment to satisfy this criteria."
                 )
         for group in self.group_map.keys():
-            if len(self.observation_spec[group].keys(True, True)) != 1:
-                raise ValueError(
-                    "Observation spec must contain one entry per group"
-                    " to follow the library conventions, "
-                    "you can apply a transform to your environment to satisfy this criteria."
-                )
             if (
                 len(self.action_spec[group].keys(True, True)) != 1
                 or list(self.action_spec[group].keys())[0] != "action"

benchmarl/environments/meltingpot/common.py

@@ -10,7 +10,13 @@
 from tensordict import TensorDictBase
 
 from torchrl.data import CompositeSpec
-from torchrl.envs import DoubleToFloat, DTypeCastTransform, EnvBase, Transform
+from torchrl.envs import (
+    DoubleToFloat,
+    DTypeCastTransform,
+    EnvBase,
+    FlattenObservation,
+    Transform,
+)
 
 from benchmarl.environments.common import Task
 from benchmarl.utils import DEVICE_TYPING
@@ -81,6 +87,7 @@ def get_env_fun(
         return lambda: MeltingpotEnv(
             substrate=self.name.lower(),
             categorical_actions=True,
+            device=device,
             **self.config,
         )
 
@@ -100,7 +107,23 @@ def group_map(self, env: EnvBase) -> Dict[str, List[str]]:
         return env.group_map
 
     def get_env_transforms(self, env: EnvBase) -> List[Transform]:
-        return [DoubleToFloat()]
+        interaction_inventories_keys = [
+            (group, "observation", "INTERACTION_INVENTORIES")
+            for group in self.group_map(env).keys()
+            if (group, "observation", "INTERACTION_INVENTORIES")
+            in env.observation_spec.keys(True, True)
+        ]
+        return [DoubleToFloat()] + (
+            [
+                FlattenObservation(
+                    in_keys=interaction_inventories_keys,
+                    first_dim=-2,
+                    last_dim=-1,
+                )
+            ]
+            if len(interaction_inventories_keys)
+            else []
+        )
 
     def get_replay_buffer_transforms(self, env: EnvBase) -> List[Transform]:
         return [
@@ -141,11 +164,6 @@ def observation_spec(self, env: EnvBase) -> CompositeSpec:
         for group_key in list(observation_spec.keys()):
             if group_key not in self.group_map(env).keys():
                 del observation_spec[group_key]
-            else:
-                group_obs_spec = observation_spec[group_key]["observation"]
-                for key in list(group_obs_spec.keys()):
-                    if key != "RGB":
-                        del group_obs_spec[key]
         return observation_spec
 
     def info_spec(self, env: EnvBase) -> Optional[CompositeSpec]:

benchmarl/experiment/experiment.py

@@ -387,14 +387,6 @@ def _setup_task(self):
                 device=self.config.sampling_device,
             )
         )
-        self.observation_spec = self.task.observation_spec(test_env)
-        self.info_spec = self.task.info_spec(test_env)
-        self.state_spec = self.task.state_spec(test_env)
-        self.action_mask_spec = self.task.action_mask_spec(test_env)
-        self.action_spec = self.task.action_spec(test_env)
-        self.group_map = self.task.group_map(test_env)
-        self.train_group_map = copy.deepcopy(self.group_map)
-        self.max_steps = self.task.max_steps(test_env)
 
         transforms_env = self.task.get_env_transforms(test_env)
         transforms_training = transforms_env + [
@@ -418,6 +410,15 @@ def _setup_task(self):
             self.config.sampling_device
         )
 
+        self.observation_spec = self.task.observation_spec(self.test_env)
+        self.info_spec = self.task.info_spec(self.test_env)
+        self.state_spec = self.task.state_spec(self.test_env)
+        self.action_mask_spec = self.task.action_mask_spec(self.test_env)
+        self.action_spec = self.task.action_spec(self.test_env)
+        self.group_map = self.task.group_map(self.test_env)
+        self.train_group_map = copy.deepcopy(self.group_map)
+        self.max_steps = self.task.max_steps(self.test_env)
+
     def _setup_algorithm(self):
         self.algorithm = self.algorithm_config.get_algorithm(experiment=self)
         self.replay_buffers = {