Add (A)syncVectorEnv support for sub-envs with different obs spaces (#…

…1126)

gymnasium/vector/async_vector_env.py

@@ -14,7 +14,7 @@
 
 import numpy as np
 
-from gymnasium import logger
+from gymnasium import Space, logger
 from gymnasium.core import ActType, Env, ObsType, RenderFrame
 from gymnasium.error import (
     AlreadyPendingCallError,
@@ -33,6 +33,10 @@
     read_from_shared_memory,
     write_to_shared_memory,
 )
+from gymnasium.vector.utils.batched_spaces import (
+    all_spaces_have_same_shape,
+    batch_differing_spaces,
+)
 from gymnasium.vector.vector_env import ArrayType, VectorEnv
 
 
@@ -98,6 +102,7 @@ def __init__(
             ]
             | None
         ) = None,
+        observation_mode: str | Space = "same",
     ):
         """Vectorized environment that runs multiple environments in parallel.
 
@@ -113,6 +118,9 @@ def __init__(
                 so for some environments you may want to have it set to ``False``.
             worker: If set, then use that worker in a subprocess instead of a default one.
                 Can be useful to override some inner vector env logic, for instance, how resets on termination or truncation are handled.
+            observation_mode: Defines how environment observation spaces should be batched. 'same' defines that there should be ``n`` copies of identical spaces.
+                'different' defines that there can be multiple observation spaces with the same length but different high/low values batched together. Passing a ``Space`` object
+                allows the user to set some custom observation space mode not covered by 'same' or 'different.'
 
         Warnings:
             worker is an advanced mode option. It provides a high degree of flexibility and a high chance
@@ -139,12 +147,29 @@ def __init__(
         self.metadata = dummy_env.metadata
         self.render_mode = dummy_env.render_mode
 
-        self.single_observation_space = dummy_env.observation_space
         self.single_action_space = dummy_env.action_space
 
-        self.observation_space = batch_space(
-            self.single_observation_space, self.num_envs
-        )
+        if isinstance(observation_mode, Space):
+            self.observation_space = observation_mode
+        else:
+            if observation_mode == "same":
+                self.single_observation_space = dummy_env.observation_space
+                self.observation_space = batch_space(
+                    self.single_observation_space, self.num_envs
+                )
+            elif observation_mode == "different":
+                current_spaces = [env().observation_space for env in self.env_fns]
+
+                assert all_spaces_have_same_shape(
+                    current_spaces
+                ), "Low & High values for observation spaces can be different but shapes need to be the same"
+
+                self.single_observation_space = batch_differing_spaces(current_spaces)
+
+                self.observation_space = self.single_observation_space
+
+            else:
+                raise ValueError("Need to pass in mode for batching observations")
         self.action_space = batch_space(self.single_action_space, self.num_envs)
 
         dummy_env.close()
@@ -716,7 +741,22 @@ def _async_worker(
             elif command == "_check_spaces":
                 pipe.send(
                     (
-                        (data[0] == observation_space, data[1] == action_space),
+                        (
+                            (data[0] == observation_space)
+                            or (
+                                hasattr(observation_space, "low")
+                                and hasattr(observation_space, "high")
+                                and np.any(
+                                    np.all(observation_space.low == data[0].low, axis=1)
+                                )
+                                and np.any(
+                                    np.all(
+                                        observation_space.high == data[0].high, axis=1
+                                    )
+                                )
+                            ),
+                            data[1] == action_space,
+                        ),
                         True,
                     )
                 )

gymnasium/vector/sync_vector_env.py

@@ -7,9 +7,14 @@
 
 import numpy as np
 
-from gymnasium import Env
+from gymnasium import Env, Space
 from gymnasium.core import ActType, ObsType, RenderFrame
 from gymnasium.vector.utils import batch_space, concatenate, create_empty_array, iterate
+from gymnasium.vector.utils.batched_spaces import (
+    all_spaces_have_same_shape,
+    all_spaces_have_same_type,
+    batch_differing_spaces,
+)
 from gymnasium.vector.vector_env import ArrayType, VectorEnv
 
 
@@ -57,13 +62,16 @@ def __init__(
         self,
         env_fns: Iterator[Callable[[], Env]] | Sequence[Callable[[], Env]],
         copy: bool = True,
+        observation_mode: str | Space = "same",
     ):
         """Vectorized environment that serially runs multiple environments.
 
         Args:
             env_fns: iterable of callable functions that create the environments.
             copy: If ``True``, then the :meth:`reset` and :meth:`step` methods return a copy of the observations.
-
+            observation_mode: Defines how environment observation spaces should be batched. 'same' defines that there should be ``n`` copies of identical spaces.
+                'different' defines that there can be multiple observation spaces with the same length but different high/low values batched together. Passing a ``Space`` object
+                allows the user to set some custom observation space mode not covered by 'same' or 'different.'
         Raises:
             RuntimeError: If the observation space of some sub-environment does not match observation_space
                 (or, by default, the observation space of the first sub-environment).
@@ -80,15 +88,39 @@ def __init__(
         self.metadata = self.envs[0].metadata
         self.render_mode = self.envs[0].render_mode
 
-        # Initialises the single spaces from the sub-environments
-        self.single_observation_space = self.envs[0].observation_space
         self.single_action_space = self.envs[0].action_space
+
+        # Initialise the obs and action space based on the desired mode
+
+        if isinstance(observation_mode, Space):
+            self.observation_space = observation_mode
+        else:
+            if observation_mode == "same":
+                self.single_observation_space = self.envs[0].observation_space
+                self.single_action_space = self.envs[0].action_space
+
+                self.observation_space = batch_space(
+                    self.single_observation_space, self.num_envs
+                )
+            elif observation_mode == "different":
+                current_spaces = [env.observation_space for env in self.envs]
+
+                assert all_spaces_have_same_shape(
+                    current_spaces
+                ), "Low & High values for observation spaces can be different but shapes need to be the same"
+                assert all_spaces_have_same_type(
+                    current_spaces
+                ), "Observation spaces must have same Space type"
+
+                self.observation_space = batch_differing_spaces(current_spaces)
+
+                self.single_observation_space = self.observation_space
+
+            else:
+                raise ValueError("Need to pass in mode for batching observations")
+
         self._check_spaces()
 
-        # Initialise the obs and action space based on the single versions and num of sub-environments
-        self.observation_space = batch_space(
-            self.single_observation_space, self.num_envs
-        )
         self.action_space = batch_space(self.single_action_space, self.num_envs)
 
         # Initialise attributes used in `step` and `reset`
@@ -270,10 +302,28 @@ def _check_spaces(self) -> bool:
         """Check that each of the environments obs and action spaces are equivalent to the single obs and action space."""
         for env in self.envs:
             if not (env.observation_space == self.single_observation_space):
-                raise RuntimeError(
-                    f"Some environments have an observation space different from `{self.single_observation_space}`. "
-                    "In order to batch observations, the observation spaces from all environments must be equal."
-                )
+                if not (
+                    hasattr(env.observation_space, "low")
+                    and hasattr(env.observation_space, "high")
+                    and np.any(
+                        np.all(
+                            env.observation_space.low
+                            == self.single_observation_space.low,
+                            axis=1,
+                        )
+                    )
+                    and np.any(
+                        np.all(
+                            env.observation_space.high
+                            == self.single_observation_space.high,
+                            axis=1,
+                        )
+                    )
+                ):
+                    raise RuntimeError(
+                        f"Some environments have an observation space different from `{self.single_observation_space}`. "
+                        "In order to batch observations, the observation spaces from all environments must be equal."
+                    )
 
             if not (env.action_space == self.single_action_space):
                 raise RuntimeError(

gymnasium/vector/utils/batched_spaces.py

@@ -0,0 +1,141 @@
+"""Batching support for Spaces of same type but possibly varying low/high values."""
+
+from __future__ import annotations
+
+from copy import deepcopy
+from functools import singledispatch
+
+import numpy as np
+
+from gymnasium import Space
+from gymnasium.spaces import (
+    Box,
+    Dict,
+    Discrete,
+    Graph,
+    MultiBinary,
+    MultiDiscrete,
+    OneOf,
+    Sequence,
+    Text,
+    Tuple,
+)
+
+
+@singledispatch
+def batch_differing_spaces(spaces: list[Space]):
+    """Batch a Sequence of spaces that allows the subspaces to contain minor differences."""
+    assert len(spaces) > 0
+    assert all(isinstance(space, type(spaces[0])) for space in spaces)
+    assert type(spaces[0]) in batch_differing_spaces.registry
+
+    return batch_differing_spaces.dispatch(type(spaces[0]))(spaces)
+
+
+@batch_differing_spaces.register(Box)
+def _batch_differing_spaces_box(spaces: list[Box]):
+    assert all(spaces[0].dtype == space for space in spaces)
+
+    return Box(
+        low=np.array([space.low for space in spaces]),
+        high=np.array([space.high for space in spaces]),
+        dtype=spaces[0].dtype,
+        seed=deepcopy(spaces[0].np_random),
+    )
+
+
+@batch_differing_spaces.register(Discrete)
+def _batch_differing_spaces_discrete(spaces: list[Discrete]):
+    return MultiDiscrete(
+        nvec=np.array([space.n for space in spaces]),
+        start=np.array([space.start for space in spaces]),
+        seed=deepcopy(spaces[0].np_random),
+    )
+
+
+@batch_differing_spaces.register(MultiDiscrete)
+def _batch_differing_spaces_multi_discrete(spaces: list[MultiDiscrete]):
+    return Box(
+        low=np.array([space.start for space in spaces]),
+        high=np.array([space.start + space.nvec for space in spaces]) - 1,
+        dtype=spaces[0].dtype,
+        seed=deepcopy(spaces[0].np_random),
+    )
+
+
+@batch_differing_spaces.register(MultiBinary)
+def _batch_differing_spaces_multi_binary(spaces: list[MultiBinary]):
+    assert all(spaces[0].shape == space.shape for space in spaces)
+
+    return Box(
+        low=0,
+        high=1,
+        shape=(len(spaces),) + spaces[0].shape,
+        dtype=spaces[0].dtype,
+        seed=deepcopy(spaces[0].np_random),
+    )
+
+
+@batch_differing_spaces.register(Tuple)
+def _batch_differing_spaces_tuple(spaces: list[Tuple]):
+    return Tuple(
+        tuple(
+            batch_differing_spaces(subspaces)
+            for subspaces in zip(*[space.spaces for space in spaces])
+        ),
+        seed=deepcopy(spaces[0].np_random),
+    )
+
+
+@batch_differing_spaces.register(Dict)
+def _batch_differing_spaces_dict(spaces: list[Dict]):
+    assert all(spaces[0].keys() == space.keys() for space in spaces)
+
+    return Dict(
+        {
+            key: batch_differing_spaces([space[key] for space in spaces])
+            for key in spaces[0].keys()
+        },
+        seed=deepcopy(spaces[0].np_random),
+    )
+
+
+@batch_differing_spaces.register(Graph)
+@batch_differing_spaces.register(Text)
+@batch_differing_spaces.register(Sequence)
+@batch_differing_spaces.register(OneOf)
+def _batch_spaces_undefined(spaces: list[Graph | Text | Sequence | OneOf]):
+    return Tuple(spaces, seed=deepcopy(spaces[0].np_random))
+
+
+def all_spaces_have_same_shape(spaces):
+    """Check if all spaces have the same size."""
+    if not spaces:
+        return True  # An empty list is considered to have the same shape
+
+    def get_space_shape(space):
+        if isinstance(space, Box):
+            return space.shape
+        elif isinstance(space, Discrete):
+            return ()  # Discrete spaces are considered scalar
+        elif isinstance(space, Dict):
+            return tuple(get_space_shape(s) for s in space.spaces.values())
+        elif isinstance(space, Tuple):
+            return tuple(get_space_shape(s) for s in space.spaces)
+        else:
+            raise ValueError(f"Unsupported space type: {type(space)}")
+
+    first_shape = get_space_shape(spaces[0])
+    return all(get_space_shape(space) == first_shape for space in spaces[1:])
+
+
+def all_spaces_have_same_type(spaces):
+    """Check if all spaces have the same space type (Box, Discrete, etc)."""
+    if not spaces:
+        return True  # An empty list is considered to have the same type
+
+    # Get the type of the first space
+    first_type = type(spaces[0])
+
+    # Check if all spaces have the same type as the first one
+    return all(isinstance(space, first_type) for space in spaces)