[Feature] Allow multiple observation keys 2 (#83)

Author: matteobettini

benchmarl/algorithms/qmix.py

@@ -177,13 +177,19 @@ def process_batch(self, group: str, batch: TensorDictBase) -> TensorDictBase:
 
     def get_mixer(self, group: str) -> TensorDictModule:
         n_agents = len(self.group_map[group])
-        group_observation_key = list(self.observation_spec[group].keys())[0]
 
         if self.state_spec is not None:
-            global_state_key = list(self.state_spec.keys())[0]
+            global_state_key = list(self.state_spec.keys(True, True))[0]
             state_shape = self.state_spec[global_state_key].shape
             in_keys = [(group, "chosen_action_value"), global_state_key]
         else:
+            group_observation_keys = list(self.observation_spec[group].keys(True, True))
+            if len(group_observation_keys) > 1:
+                raise ValueError(
+                    "QMIX called without a global state and multiple observation keys, currently the mixer"
+                    "takes only one observation key, please raise an issue if you need this fauture."
+                )
+            group_observation_key = group_observation_keys[0]
             state_shape = self.observation_spec[group, group_observation_key].shape
             in_keys = [(group, "chosen_action_value"), (group, group_observation_key)]
 

benchmarl/environments/common.py

@@ -156,11 +156,32 @@ def group_map(self, env: EnvBase) -> Dict[str, List[str]]:
     def observation_spec(self, env: EnvBase) -> CompositeSpec:
         """
         A spec for the observation.
-        Must be a CompositeSpec with one (group_name, observation_key) entry per group.
+        Must be a CompositeSpec with as many entries as needed nested under the ``group_name`` key.
 
         Args:
             env (EnvBase): An environment created via self.get_env_fun
 
+        Examples:
+            >>> print(task.observation_spec(env))
+            CompositeSpec(
+                agents: CompositeSpec(
+                    observation: CompositeSpec(
+                        image: UnboundedDiscreteTensorSpec(
+                            shape=torch.Size([8, 88, 88, 3]),
+                            space=ContinuousBox(
+                                low=Tensor(shape=torch.Size([8, 88, 88, 3]), device=cpu, dtype=torch.int64, contiguous=True),
+                                high=Tensor(shape=torch.Size([8, 88, 88, 3]), device=cpu, dtype=torch.int64, contiguous=True)),
+                            device=cpu,
+                            dtype=torch.uint8,
+                            domain=discrete),
+                        array: UnboundedContinuousTensorSpec(
+                            shape=torch.Size([8, 3]),
+                            space=None,
+                            device=cpu,
+                            dtype=torch.float32,
+                            domain=continuous), device=cpu, shape=torch.Size([8])), device=cpu, shape=torch.Size([8])), device=cpu, shape=torch.Size([]))
+
+
         """
         raise NotImplementedError