[Feature] More callbacks

benchmarl/environments/common.py

@@ -238,6 +238,13 @@ def get_reward_sum_transform(self, env: EnvBase) -> Transform:
         """
         return RewardSum(reset_keys=env.reset_keys)
 
+    @staticmethod
+    def render_callback(experiment, env: EnvBase, data: TensorDictBase):
+        try:
+            return env.render(mode="rgb_array")
+        except TypeError:
+            return env.render()
+
     def __repr__(self):
         cls_name = self.__class__.__name__
         return f"{cls_name}.{self.name}: (config={self.config})"

benchmarl/experiment/callback.py

@@ -34,12 +34,27 @@ def on_batch_collected(self, batch: TensorDictBase):
         """
         pass
 
-    def on_train_end(self, training_td: TensorDictBase):
+    def on_train_step(self, batch: TensorDictBase, group: str) -> TensorDictBase:
         """
-        A callback called at the end of every training step.
+        A callback called for every training step.
+
+        Args:
+           batch (TensorDictBase): tensordict with the training batch
+           group (str): group name
+
+        Returns:
+            TensorDictBase: a new tensordict containing the loss values
+
+        """
+        pass
+
+    def on_train_end(self, training_td: TensorDictBase, group: str):
+        """
+        A callback called at the end of training.
 
         Args:
             training_td (TensorDictBase): tensordict containing the loss values
+            group (str): group name
 
         """
         pass
@@ -65,9 +80,20 @@ def on_batch_collected(self, batch: TensorDictBase):
         for callback in self.callbacks:
             callback.on_batch_collected(batch)
 
-    def on_train_end(self, training_td: TensorDictBase):
+    def on_train_step(self, batch: TensorDictBase, group: str) -> TensorDictBase:
+        train_td = None
+        for callback in self.callbacks:
+            td = callback.on_train_step(batch, group)
+            if td is not None:
+                if train_td is None:
+                    train_td = td
+                else:
+                    train_td.update(td)
+        return train_td
+
+    def on_train_end(self, training_td: TensorDictBase, group: str):
         for callback in self.callbacks:
-            callback.on_train_end(training_td)
+            callback.on_train_end(training_td, group)
 
     def on_evaluation_end(self, rollouts: List[TensorDictBase]):
         for callback in self.callbacks:

benchmarl/experiment/experiment.py

@@ -7,6 +7,7 @@
 from __future__ import annotations
 
 import importlib
+
 import os
 import time
 from collections import OrderedDict
@@ -535,7 +536,6 @@ def _collection_loop(self):
                 step=self.n_iters_performed,
             )
             pbar.set_description(f"mean return = {self.mean_return}", refresh=False)
-            pbar.update()
 
             # Callback
             self.on_batch_collected(batch)
@@ -561,7 +561,7 @@ def _collection_loop(self):
                 )
 
                 # Callback
-                self.on_train_end(training_td)
+                self.on_train_end(training_td, group)
 
                 # Exploration update
                 if isinstance(self.group_policies[group], TensorDictSequential):
@@ -607,6 +607,7 @@ def _collection_loop(self):
                 and self.total_frames % self.config.checkpoint_interval == 0
             ):
                 self._save_experiment()
+            pbar.update()
             sampling_start = time.time()
 
         self.close()
@@ -638,6 +639,7 @@ def _optimizer_loop(self, group: str) -> TensorDictBase:
                 loss_value.backward()
 
                 grad_norm = self._grad_clip(optimizer)
+
                 training_td.set(
                     f"grad_norm_{loss_name}",
                     torch.tensor(grad_norm, device=self.config.train_device),
@@ -648,6 +650,11 @@ def _optimizer_loop(self, group: str) -> TensorDictBase:
         self.replay_buffers[group].update_tensordict_priority(subdata)
         if self.target_updaters[group] is not None:
             self.target_updaters[group].step()
+
+        callback_loss = self.on_train_step(subdata, group)
+        if callback_loss is not None:
+            training_td.update(callback_loss)
+
         return training_td
 
     def _grad_clip(self, optimizer: torch.optim.Optimizer) -> float:
@@ -672,10 +679,9 @@ def _evaluation_loop(self):
                 video_frames = []
 
                 def callback(env, td):
-                    try:
-                        video_frames.append(env.render(mode="rgb_array"))
-                    except TypeError:
-                        video_frames.append(env.render())
+                    video_frames.append(
+                        self.task.__class__.render_callback(self, env, td)
+                    )
 
             else:
                 video_frames = None

benchmarl/experiment/logger.py

@@ -88,7 +88,6 @@ def log_collection(
         total_frames: int,
         step: int,
     ) -> float:
-
         to_log = {}
         json_metrics = {}
         for group in self.group_map.keys():
@@ -218,7 +217,9 @@ def log_evaluation(
         self.log(to_log, step=step)
         if video_frames is not None:
             vid = torch.tensor(
-                np.transpose(video_frames[: rollouts[0].batch_size[0]], (0, 3, 1, 2)),
+                np.transpose(
+                    video_frames[: rollouts[0].batch_size[0] - 1], (0, 3, 1, 2)
+                ),
                 dtype=torch.uint8,
             ).unsqueeze(0)
             for logger in self.loggers:

examples/callback/custom_callback.py

@@ -11,14 +11,18 @@
 from benchmarl.experiment import Experiment, ExperimentConfig
 from benchmarl.experiment.callback import Callback
 from benchmarl.models.mlp import MlpConfig
-from tensordict import TensorDictBase
+from tensordict import TensorDict, TensorDictBase
 
 
 class MyCallbackA(Callback):
     def on_batch_collected(self, batch: TensorDictBase):
         print(f"Callback A is doing something with the sampling batch {batch}")
 
-    def on_train_end(self, training_td: TensorDictBase):
+    def on_train_step(self, batch: TensorDictBase, group: str) -> TensorDictBase:
+        print(f"Callback A is computing a loss with the training tensordict {batch}")
+        return TensorDict({}, [])
+
+    def on_train_end(self, training_td: TensorDictBase, group: str):
         print(
             f"Callback A is doing something with the training tensordict {training_td}"
         )
@@ -37,7 +41,6 @@ def on_evaluation_end(self, rollouts: List[TensorDictBase]):
 
 
 if __name__ == "__main__":
-
     experiment_config = ExperimentConfig.get_from_yaml()
     task = VmasTask.BALANCE.get_from_yaml()
     algorithm_config = MappoConfig.get_from_yaml()