[Feature] custom info_dict reader methods

torchrl/envs/__init__.py

@@ -8,3 +8,4 @@
 from .vec_env import *
 from .transforms import *
 from .env_creator import *
+from .gym_like import *

torchrl/envs/gym_like.py

@@ -10,9 +10,43 @@
 from torchrl.envs.common import _EnvWrapper
 from torchrl.envs.utils import step_tensordict
 
+__all__ = ["GymLikeEnv", "default_info_dict_reader"]
+
+
+class default_info_dict_reader:
+    """
+    Default info-key reader.
+
+    In cases where keys can be directly written to a tensordict (mostly if they abide to the
+    tensordict shape), one simply needs to indicate the keys to be registered during
+    instantiation.
+
+    Examples:
+        >>> from torchrl.envs import GymWrapper, default_info_dict_reader
+        >>> reader = default_info_dict_reader(["my_info_key"])
+        >>> # assuming "some_env-v0" returns a dict with a key "my_info_key"
+        >>> env = GymWrapper(gym.make("some_env-v0"))
+        >>> env.set_info_dict_reader(info_dict_reader=reader)
+        >>> tensordict = env.reset()
+        >>> tensordict = env.rand_step(tensordict)
+        >>> assert "my_info_key" in tensordict.keys()
+
+    """
+
+    def __init__(self, keys=None):
+        if keys is None:
+            keys = []
+        self.keys = keys
+
+    def __call__(self, info_dict: dict, tensordict: _TensorDict) -> _TensorDict:
+        for key in self.keys:
+            if key in info_dict:
+                tensordict[key] = info_dict[key]
+        return tensordict
+
 
 class GymLikeEnv(_EnvWrapper):
-    info_keys = []
+    _info_dict_reader: callable
 
     """
     A gym-like env is an environment whose behaviour is similar to gym environments in what
@@ -25,7 +59,7 @@ class GymLikeEnv(_EnvWrapper):
 
     where the outputs are the observation, reward and done state respectively.
     In this implementation, the info output is discarded (but specific keys can be read
-    by updating the `"info_keys"` class attribute).
+    by updating info_dict_reader, see `set_info_dict_reader` class method).
 
     By default, the first output is written at the "next_observation" key-value pair in the output tensordict, unless
     the first output is a dictionary. In that case, each observation output will be put at the corresponding
@@ -65,9 +99,7 @@ def _step(self, tensordict: _TensorDict) -> _TensorDict:
         )
         tensordict_out.set("reward", reward)
         tensordict_out.set("done", done)
-        for key in self.info_keys:
-            data = info[0][key]
-            tensordict_out.set(key, data)
+        self.info_dict_reader(info, tensordict_out)
 
         self.current_tensordict = step_tensordict(tensordict_out)
         return tensordict_out
@@ -100,6 +132,42 @@ def _output_transform(self, step_outputs_tuple: Tuple) -> Tuple:
             )
         return step_outputs_tuple
 
+    def set_info_dict_reader(self, info_dict_reader: callable) -> GymLikeEnv:
+        """
+        Sets an info_dict_reader function. This function should take as input an
+        info_dict dictionary and the tensordict returned by the step function, and
+        write values in an ad-hoc manner from one to the other.
+
+        Args:
+            info_dict_reader (callable): a callable taking a input dictionary and
+                output tensordict as arguments. This function should modify the
+                tensordict in-place.
+
+        Returns: the same environment with the dict_reader registered.
+
+        Examples:
+            >>> from torchrl.envs import GymWrapper, default_info_dict_reader
+            >>> reader = default_info_dict_reader(["my_info_key"])
+            >>> # assuming "some_env-v0" returns a dict with a key "my_info_key"
+            >>> env = GymWrapper(gym.make("some_env-v0")).set_info_dict_reader(info_dict_reader=reader)
+            >>> tensordict = env.reset()
+            >>> tensordict = env.rand_step(tensordict)
+            >>> assert "my_info_key" in tensordict.keys()
+
+        """
+        self.info_dict_reader = info_dict_reader
+        return self
+
+    @property
+    def info_dict_reader(self):
+        if "_info_dict_reader" not in self.__dir__():
+            self._info_dict_reader = default_info_dict_reader()
+        return self._info_dict_reader
+
+    @info_dict_reader.setter
+    def info_dict_reader(self, value: callable):
+        self._info_dict_reader = value
+
     def __repr__(self) -> str:
         return (
             f"{self.__class__.__name__}(env={self._env}, batch_size={self.batch_size})"