[RLlib - Offline RL] Fix bug in return_iterator in multi-learner settings.

rllib/algorithms/algorithm_config.py

@@ -2989,7 +2989,7 @@ def evaluation(
         if offline_evaluation_type is not NotProvided:
             self.offline_evaluation_type = offline_evaluation_type
         if offline_eval_runner_class is not NotProvided:
-            self.offline_eval_runner_cls = offline_eval_runner_class
+            self.offline_eval_runner_class = offline_eval_runner_class
         if offline_loss_for_module_fn is not NotProvided:
             self.offline_loss_for_module_fn = offline_loss_for_module_fn
         if offline_eval_batch_size_per_runner is not NotProvided:
@@ -5328,8 +5328,8 @@ def _validate_offline_settings(self):
 
         from ray.rllib.offline.offline_evaluation_runner import OfflineEvaluationRunner
 
-        if self.prelearner_class and not issubclass(
-            self.prelearner_class, OfflineEvaluationRunner
+        if self.offline_eval_runner_class and not issubclass(
+            self.offline_eval_runner_class, OfflineEvaluationRunner
         ):
             self._value_error(
                 "Unknown `offline_eval_runner_class`. OfflineEvaluationRunner class needs to inherit "

rllib/algorithms/cql/cql.py

@@ -302,15 +302,20 @@ def training_step(self) -> None:
 
         # Sampling from offline data.
         with self.metrics.log_time((TIMERS, OFFLINE_SAMPLING_TIMER)):
+            # If we should use an iterator in the learner(s). Note, in case of
+            # multiple learners we must always return a list of iterators.
+            return_iterator = return_iterator = (
+                self.config.num_learners > 0
+                or self.config.dataset_num_iters_per_learner != 1
+            )
+
             # Return an iterator in case we are using remote learners.
             batch_or_iterator = self.offline_data.sample(
                 num_samples=self.config.train_batch_size_per_learner,
                 num_shards=self.config.num_learners,
                 # Return an iterator, if a `Learner` should update
                 # multiple times per RLlib iteration.
-                return_iterator=self.config.dataset_num_iters_per_learner > 1
-                if self.config.dataset_num_iters_per_learner
-                else True,
+                return_iterator=return_iterator,
             )
 
         # Updating the policy.

rllib/algorithms/marwil/marwil.py

@@ -457,11 +457,13 @@ class (multi-/single-learner setup) and evaluation on
         #  the user that sth. is not right, although it is as
         #  we do not step the env.
         with self.metrics.log_time((TIMERS, OFFLINE_SAMPLING_TIMER)):
+            # If we should use an iterator in the learner(s). Note, in case of
+            # multiple learners we must always return a list of iterators.
             return_iterator = (
-                self.config.dataset_num_iters_per_learner > 1
-                if self.config.dataset_num_iters_per_learner
-                else True
+                self.config.num_learners > 0
+                or self.config.dataset_num_iters_per_learner != 1
             )
+
             # Sampling from offline data.
             batch_or_iterator = self.offline_data.sample(
                 num_samples=self.config.train_batch_size_per_learner,