Allow custom spaces in VectorEnv

gym/error.py

@@ -165,3 +165,10 @@ class ClosedEnvironmentError(Exception):
     Trying to call `reset`, or `step`, while the environment is closed.
     """
     pass
+
+class CustomSpaceError(Exception):
+    """
+    The space is a custom gym.Space instance, and is not supported by
+    `AsyncVectorEnv` with `shared_memory=True`.
+    """
+    pass

gym/spaces/space.py

@@ -5,6 +5,15 @@ class Space(object):
     """Defines the observation and action spaces, so you can write generic
     code that applies to any Env. For example, you can choose a random
     action.
+
+    WARNING - Custom observation & action spaces can inherit from the `Space`
+    class. However, most use-cases should be covered by the existing space
+    classes (e.g. `Box`, `Discrete`, etc...), and container classes (`Tuple` &
+    `Dict`). Note that parametrized probability distributions (through the
+    `sample()` method), and batching functions (in `gym.vector.VectorEnv`), are
+    only well-defined for instances of spaces provided in gym by default.
+    Moreover, some implementations of Reinforcement Learning algorithms might
+    not handle custom spaces properly. Use custom spaces with care.
     """
     def __init__(self, shape=None, dtype=None):
         import numpy as np  # takes about 300-400ms to import, so we load lazily

gym/vector/async_vector_env.py

@@ -8,7 +8,7 @@
 from gym import logger
 from gym.vector.vector_env import VectorEnv
 from gym.error import (AlreadyPendingCallError, NoAsyncCallError,
-                       ClosedEnvironmentError)
+                       ClosedEnvironmentError, CustomSpaceError)
 from gym.vector.utils import (create_shared_memory, create_empty_array,
                               write_to_shared_memory, read_from_shared_memory,
                               concatenate, CloudpickleWrapper, clear_mpi_env_vars)
@@ -88,10 +88,18 @@ def __init__(self, env_fns, observation_space=None, action_space=None,
             observation_space=observation_space, action_space=action_space)
 
         if self.shared_memory:
-            _obs_buffer = create_shared_memory(self.single_observation_space,
-                n=self.num_envs, ctx=ctx)
-            self.observations = read_from_shared_memory(_obs_buffer,
-                self.single_observation_space, n=self.num_envs)
+            try:
+                _obs_buffer = create_shared_memory(self.single_observation_space,
+                    n=self.num_envs, ctx=ctx)
+                self.observations = read_from_shared_memory(_obs_buffer,
+                    self.single_observation_space, n=self.num_envs)
+            except CustomSpaceError:
+                raise ValueError('Using `shared_memory=True` in `AsyncVectorEnv` '
+                    'is incompatible with non-standard Gym observation spaces '
+                    '(i.e. custom spaces inheriting from `gym.Space`), and is '
+                    'only compatible with default Gym spaces (e.g. `Box`, '
+                    '`Tuple`, `Dict`) for batching. Set `shared_memory=False` '
+                    'if you use custom observation spaces.')
         else:
             _obs_buffer = None
             self.observations = create_empty_array(
@@ -176,7 +184,8 @@ def reset_wait(self, timeout=None):
         self._state = AsyncState.DEFAULT
 
         if not self.shared_memory:
-            concatenate(results, self.observations, self.single_observation_space)
+            self.observations = concatenate(results, self.observations,
+                self.single_observation_space)
 
         return deepcopy(self.observations) if self.copy else self.observations
 
@@ -235,7 +244,7 @@ def step_wait(self, timeout=None):
         observations_list, rewards, dones, infos = zip(*results)
 
         if not self.shared_memory:
-            concatenate(observations_list, self.observations,
+            self.observations = concatenate(observations_list, self.observations,
                 self.single_observation_space)
 
         return (deepcopy(self.observations) if self.copy else self.observations,

gym/vector/sync_vector_env.py

@@ -63,9 +63,10 @@ def reset_wait(self):
         for env in self.envs:
             observation = env.reset()
             observations.append(observation)
-        concatenate(observations, self.observations, self.single_observation_space)
+        self.observations = concatenate(observations, self.observations,
+            self.single_observation_space)
 
-        return np.copy(self.observations) if self.copy else self.observations
+        return deepcopy(self.observations) if self.copy else self.observations
 
     def step_async(self, actions):
         self._actions = actions
@@ -78,7 +79,8 @@ def step_wait(self):
                 observation = env.reset()
             observations.append(observation)
             infos.append(info)
-        concatenate(observations, self.observations, self.single_observation_space)
+        self.observations = concatenate(observations, self.observations,
+            self.single_observation_space)
 
         return (deepcopy(self.observations) if self.copy else self.observations,
             np.copy(self._rewards), np.copy(self._dones), infos)

gym/vector/tests/test_async_vector_env.py

@@ -2,10 +2,11 @@
 import numpy as np
 
 from multiprocessing import TimeoutError
-from gym.spaces import Box
+from gym.spaces import Box, Tuple
 from gym.error import (AlreadyPendingCallError, NoAsyncCallError,
                        ClosedEnvironmentError)
-from gym.vector.tests.utils import make_env, make_slow_env
+from gym.vector.tests.utils import (CustomSpace, make_env,
+    make_slow_env, make_custom_space_env)
 
 from gym.vector.async_vector_env import AsyncVectorEnv
 
@@ -194,3 +195,31 @@ def test_check_observations_async_vector_env(shared_memory):
     with pytest.raises(RuntimeError):
         env = AsyncVectorEnv(env_fns, shared_memory=shared_memory)
         env.close(terminate=True)
+
+
+def test_custom_space_async_vector_env():
+    env_fns = [make_custom_space_env(i) for i in range(4)]
+    try:
+        env = AsyncVectorEnv(env_fns, shared_memory=False)
+        reset_observations = env.reset()
+        actions = ('action-2', 'action-3', 'action-5', 'action-7')
+        step_observations, rewards, dones, _ = env.step(actions)
+    finally:
+        env.close()
+
+    assert isinstance(env.single_observation_space, CustomSpace)
+    assert isinstance(env.observation_space, Tuple)
+
+    assert isinstance(reset_observations, tuple)
+    assert reset_observations == ('reset', 'reset', 'reset', 'reset')
+
+    assert isinstance(step_observations, tuple)
+    assert step_observations == ('step(action-2)', 'step(action-3)',
+                                 'step(action-5)', 'step(action-7)')
+
+
+def test_custom_space_async_vector_env_shared_memory():
+    env_fns = [make_custom_space_env(i) for i in range(4)]
+    with pytest.raises(ValueError):
+        env = AsyncVectorEnv(env_fns, shared_memory=True)
+        env.close(terminate=True)

gym/vector/tests/test_shared_memory.py

@@ -7,9 +7,10 @@
 from multiprocessing import Array, Process
 from collections import OrderedDict
 
-from gym.spaces import Tuple, Dict
+from gym.spaces import Box, Tuple, Dict
+from gym.error import CustomSpaceError
 from gym.vector.utils.spaces import _BaseGymSpaces
-from gym.vector.tests.utils import spaces
+from gym.vector.tests.utils import spaces, custom_spaces
 
 from gym.vector.utils.shared_memory import (create_shared_memory,
     read_from_shared_memory, write_to_shared_memory)
@@ -64,6 +65,15 @@ def assert_nested_type(lhs, rhs, n):
     assert_nested_type(shared_memory, expected_type, n=n)
 
 
+@pytest.mark.parametrize('n', [1, 8])
+@pytest.mark.parametrize('ctx', [None, 'fork', 'spawn'], ids=['default', 'fork', 'spawn'])
+@pytest.mark.parametrize('space', custom_spaces)
+def test_create_shared_memory_custom_space(n, ctx, space):
+    ctx = mp if (ctx is None) else mp.get_context(ctx)
+    with pytest.raises(CustomSpaceError):
+        shared_memory = create_shared_memory(space, n=n, ctx=ctx)
+
+
 @pytest.mark.parametrize('space', spaces,
     ids=[space.__class__.__name__ for space in spaces])
 def test_write_to_shared_memory(space):

gym/vector/tests/test_spaces.py

@@ -2,7 +2,7 @@
 import numpy as np
 
 from gym.spaces import Box, MultiDiscrete, Tuple, Dict
-from gym.vector.tests.utils import spaces
+from gym.vector.tests.utils import spaces, custom_spaces, CustomSpace
 
 from gym.vector.utils.spaces import _BaseGymSpaces, batch_space
 
@@ -32,8 +32,23 @@
     })
 ]
 
+expected_custom_batch_spaces_4 = [
+    Tuple((CustomSpace(), CustomSpace(), CustomSpace(), CustomSpace())),
+    Tuple((
+        Tuple((CustomSpace(), CustomSpace(), CustomSpace(), CustomSpace())),
+        Box(low=0, high=255, shape=(4,), dtype=np.uint8)
+    ))
+]
+
 @pytest.mark.parametrize('space,expected_batch_space_4', list(zip(spaces,
     expected_batch_spaces_4)), ids=[space.__class__.__name__ for space in spaces])
 def test_batch_space(space, expected_batch_space_4):
     batch_space_4 = batch_space(space, n=4)
     assert batch_space_4 == expected_batch_space_4
+
+
+@pytest.mark.parametrize('space,expected_batch_space_4', list(zip(custom_spaces,
+    expected_custom_batch_spaces_4)), ids=[space.__class__.__name__ for space in custom_spaces])
+def test_batch_space_custom_space(space, expected_batch_space_4):
+    batch_space_4 = batch_space(space, n=4)
+    assert batch_space_4 == expected_batch_space_4

gym/vector/tests/test_sync_vector_env.py

@@ -1,8 +1,8 @@
 import pytest
 import numpy as np
 
-from gym.spaces import Box
-from gym.vector.tests.utils import make_env
+from gym.spaces import Box, Tuple
+from gym.vector.tests.utils import CustomSpace, make_env, make_custom_space_env
 
 from gym.vector.sync_vector_env import SyncVectorEnv
 
@@ -70,3 +70,24 @@ def test_check_observations_sync_vector_env():
     with pytest.raises(RuntimeError):
         env = SyncVectorEnv(env_fns)
         env.close()
+
+
+def test_custom_space_sync_vector_env():
+    env_fns = [make_custom_space_env(i) for i in range(4)]
+    try:
+        env = SyncVectorEnv(env_fns)
+        reset_observations = env.reset()
+        actions = ('action-2', 'action-3', 'action-5', 'action-7')
+        step_observations, rewards, dones, _ = env.step(actions)
+    finally:
+        env.close()
+
+    assert isinstance(env.single_observation_space, CustomSpace)
+    assert isinstance(env.observation_space, Tuple)
+
+    assert isinstance(reset_observations, tuple)
+    assert reset_observations == ('reset', 'reset', 'reset', 'reset')
+
+    assert isinstance(step_observations, tuple)
+    assert step_observations == ('step(action-2)', 'step(action-3)',
+                                 'step(action-5)', 'step(action-7)')

gym/vector/tests/test_vector_env.py

@@ -1,10 +1,12 @@
 import pytest
 import numpy as np
 
-from gym.vector.tests.utils import make_env
+from gym.spaces import Tuple
+from gym.vector.tests.utils import CustomSpace, make_env
 
 from gym.vector.async_vector_env import AsyncVectorEnv
 from gym.vector.sync_vector_env import SyncVectorEnv
+from gym.vector.vector_env import VectorEnv
 
 @pytest.mark.parametrize('shared_memory', [True, False])
 def test_vector_env_equal(shared_memory):
@@ -41,3 +43,13 @@ def test_vector_env_equal(shared_memory):
     finally:
         async_env.close()
         sync_env.close()
+
+
+def test_custom_space_vector_env():
+    env = VectorEnv(4, CustomSpace(), CustomSpace())
+
+    assert isinstance(env.single_observation_space, CustomSpace)
+    assert isinstance(env.observation_space, Tuple)
+
+    assert isinstance(env.single_action_space, CustomSpace)
+    assert isinstance(env.action_space, Tuple)

gym/vector/tests/utils.py

@@ -47,6 +47,30 @@ def step(self, action):
         reward, done = 0., False
         return observation, reward, done, {}
 
+class CustomSpace(gym.Space):
+    """Minimal custom observation space."""
+    def __eq__(self, other):
+        return isinstance(other, CustomSpace)
+
+custom_spaces = [
+    CustomSpace(),
+    Tuple((CustomSpace(), Box(low=0, high=255, shape=(), dtype=np.uint8)))
+]
+
+class CustomSpaceEnv(gym.Env):
+    def __init__(self):
+        super(CustomSpaceEnv, self).__init__()
+        self.observation_space = CustomSpace()
+        self.action_space = CustomSpace()
+
+    def reset(self):
+        return 'reset'
+
+    def step(self, action):
+        observation = 'step({0:s})'.format(action)
+        reward, done = 0., False
+        return observation, reward, done, {}
+
 def make_env(env_name, seed):
     def _make():
         env = gym.make(env_name)
@@ -60,3 +84,10 @@ def _make():
         env.seed(seed)
         return env
     return _make
+
+def make_custom_space_env(seed):
+    def _make():
+        env = CustomSpaceEnv()
+        env.seed(seed)
+        return env
+    return _make

gym/vector/utils/numpy_utils.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from gym.spaces import Tuple, Dict
+from gym.spaces import Space, Tuple, Dict
 from gym.vector.utils.spaces import _BaseGymSpaces
 from collections import OrderedDict
 
@@ -42,8 +42,11 @@ def concatenate(items, out, space):
         return concatenate_tuple(items, out, space)
     elif isinstance(space, Dict):
         return concatenate_dict(items, out, space)
+    elif isinstance(space, Space):
+        return concatenate_custom(items, out, space)
     else:
-        raise NotImplementedError()
+        raise ValueError('Space of type `{0}` is not a valid `gym.Space` '
+                         'instance.'.format(type(space)))
 
 def concatenate_base(items, out, space):
     return np.stack(items, axis=0, out=out)
@@ -56,6 +59,9 @@ def concatenate_dict(items, out, space):
     return OrderedDict([(key, concatenate([item[key] for item in items],
         out[key], subspace)) for (key, subspace) in space.spaces.items()])
 
+def concatenate_custom(items, out, space):
+    return tuple(items)
+
 
 def create_empty_array(space, n=1, fn=np.zeros):
     """Create an empty (possibly nested) numpy array.
@@ -96,8 +102,11 @@ def create_empty_array(space, n=1, fn=np.zeros):
         return create_empty_array_tuple(space, n=n, fn=fn)
     elif isinstance(space, Dict):
         return create_empty_array_dict(space, n=n, fn=fn)
+    elif isinstance(space, Space):
+        return create_empty_array_custom(space, n=n, fn=fn)
     else:
-        raise NotImplementedError()
+        raise ValueError('Space of type `{0}` is not a valid `gym.Space` '
+                         'instance.'.format(type(space)))
 
 def create_empty_array_base(space, n=1, fn=np.zeros):
     shape = space.shape if (n is None) else (n,) + space.shape
@@ -110,3 +119,6 @@ def create_empty_array_tuple(space, n=1, fn=np.zeros):
 def create_empty_array_dict(space, n=1, fn=np.zeros):
     return OrderedDict([(key, create_empty_array(subspace, n=n, fn=fn))
         for (key, subspace) in space.spaces.items()])
+
+def create_empty_array_custom(space, n=1, fn=np.zeros):
+    return None