[Feature] env.step_mdp

torchrl/envs/common.py

@@ -3015,6 +3015,52 @@ def add_truncated_keys(self) -> EnvBase:
         self.__dict__["_done_keys"] = None
         return self
 
+    def step_mdp(self, next_tensordict: TensorDictBase) -> TensorDictBase:
+        """Advances the environment state by one step using the provided `next_tensordict`.
+
+        This method updates the environment's state by transitioning from the current
+        state to the next, as defined by the `next_tensordict`. The resulting tensordict
+        includes updated observations and any other relevant state information, with
+        keys managed according to the environment's specifications.
+
+        Internally, this method utilizes a precomputed :class:`~torchrl.envs.utils._StepMDP` instance to efficiently
+        handle the transition of state, observation, action, reward, and done keys. The
+        :class:`~torchrl.envs.utils._StepMDP` class optimizes the process by precomputing the keys to include and
+        exclude, reducing runtime overhead during repeated calls. The :class:`~torchrl.envs.utils._StepMDP` instance
+        is created with `exclude_action=False`, meaning that action keys are retained in
+        the root tensordict.
+
+        Args:
+            next_tensordict (TensorDictBase): A tensordict containing the state of the
+                environment at the next time step. This tensordict should include keys
+                for observations, actions, rewards, and done flags, as defined by the
+                environment's specifications.
+
+        Returns:
+            TensorDictBase: A new tensordict representing the environment state after
+            advancing by one step.
+
+        .. note:: The method ensures that the environment's key specifications are validated
+              against the provided `next_tensordict`, issuing warnings if discrepancies
+              are found.
+
+        .. note:: This method is designed to work efficiently with environments that have
+              consistent key specifications, leveraging the `_StepMDP` class to minimize
+              overhead.
+
+        Example:
+            >>> from torchrl.envs import GymEnv
+            >>> env = GymEnv("Pendulum-1")
+            >>> data = env.reset()
+            >>> for i in range(10):
+            ...     # compute action
+            ...     env.rand_action(data)
+            ...     # Perform action
+            ...     next_data = env.step(reset_data)
+            ...     data = env.step_mdp(next_data)
+        """
+        return self._step_mdp(next_tensordict)
+
     @property
     def _step_mdp(self):
         step_func = self.__dict__.get("_step_mdp_value")