Add experimental Wrapper (openai#276)

gym/__init__.py

@@ -31,8 +31,8 @@ def sanity_check_dependencies():
 
 sanity_check_dependencies()
 
-from gym.core import Env, Space
+from gym.core import Env, Space, Wrapper
 from gym.envs import make, spec
 from gym.scoreboard.api import upload
 
-__all__ = ["Env", "Space", "make", "spec", "upload"]
+__all__ = ["Env", "Space", "Wrapper", "make", "spec", "upload"]

gym/core.py

@@ -2,6 +2,7 @@
 logger = logging.getLogger(__name__)
 
 import numpy as np
+import weakref
 
 from gym import error, monitoring
 from gym.utils import closer, reraise
@@ -52,6 +53,7 @@ def __new__(cls, *args, **kwargs):
         env._env_closer_id = env_closer.register(env)
         env._closed = False
         env._configured = False
+        env._unwrapped = None
 
         # Will be automatically set when creating an environment via 'make'
         env.spec = None
@@ -238,6 +240,42 @@ def configure(self, *args, **kwargs):
             else:
                 raise
 
+    def build(self):
+        """[EXPERIMENTAL: may be removed in a later version of Gym] Builds an
+        environment by applying any provided wrappers, with the
+        outmost wrapper supplied first. This method is automatically
+        invoked by 'gym.make', and should be manually invoked if
+        instantiating an environment by hand.
+
+        Notes:
+            The default implementation will wrap the environment in the
+            list of wrappers provided in self.metadata['wrappers'], in reverse
+            order. So for example, given:
+
+            class FooEnv(gym.Env):
+                metadata = {
+                    'wrappers': [Wrapper1, Wrapper2]
+                }
+
+            Calling 'env.build' will return 'Wrapper1(Wrapper2(env))'.
+
+        Returns:
+            gym.Env: A potentially wrapped environment instance.
+
+        """
+        wrapped = self
+        for wrapper in reversed(self.metadata.get('wrappers', [])):
+            wrapped = wrapper(wrapped)
+        return wrapped
+
+    @property
+    def unwrapped(self):
+        """Avoid refcycles by making this into a property."""
+        if self._unwrapped is not None:
+            return self._unwrapped
+        else:
+            return self
+
     def __del__(self):
         self.close()
 
@@ -247,10 +285,9 @@ def __str__(self):
 # Space-related abstractions
 
 class Space(object):
-    """
-    Provides a classification state spaces and action spaces,
-    so you can write generic code that applies to any Environment.
-    E.g. to choose a random action.
+    """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.
     """
 
     def sample(self, seed=0):
@@ -275,3 +312,34 @@ def from_jsonable(self, sample_n):
         """Convert a JSONable data type to a batch of samples from this space."""
         # By default, assume identity is JSONable
         return sample_n
+
+class Wrapper(Env):
+    def __init__(self, env):
+        self.env = env
+        self.metadata = env.metadata
+        self.action_space = env.action_space
+        self.observation_space = env.observation_space
+        self.reward_range = env.reward_range
+        self.spec = env.spec
+        self._unwrapped = env.unwrapped
+
+    def _step(self, action):
+        return self.env.step(action)
+
+    def _reset(self):
+        return self.env.reset()
+
+    def _render(self, mode='human', close=False):
+        return self.env.render(mode, close)
+
+    def _close(self):
+        return self.env.close()
+
+    def _configure(self, *args, **kwargs):
+        return self.env.configure(*args, **kwargs)
+
+    def _seed(self, seed=None):
+        return self.env.seed(seed)
+
+    def __str__(self):
+        return '<{}{} instance>'.format(type(self).__name__, self.env)

gym/envs/registration.py

@@ -63,6 +63,8 @@ def make(self):
 
         # Make the enviroment aware of which spec it came from.
         env.spec = self
+        env = env.build()
+
         return env
 
     def __repr__(self):