[RLlib] Clean up offline prelearner and its unit testing

doc/source/rllib/package_ref/offline.rst

@@ -86,5 +86,3 @@ Transforming Data with an OfflinePreLearner
     OfflinePreLearner._map_to_episodes
     OfflinePreLearner._map_sample_batch_to_episode
     OfflinePreLearner._should_module_be_updated
-    OfflinePreLearner.default_prelearner_buffer_class
-    OfflinePreLearner.default_prelearner_buffer_kwargs

rllib/offline/offline_policy_evaluation_runner.py

@@ -29,7 +29,7 @@
 from ray.rllib.core.columns import Columns
 from ray.rllib.core.rl_module.multi_rl_module import MultiRLModuleSpec
 from ray.rllib.env.single_agent_episode import SingleAgentEpisode
-from ray.rllib.offline.offline_prelearner import SCHEMA, OfflinePreLearner
+from ray.rllib.offline.offline_prelearner import OfflinePreLearner
 from ray.rllib.policy.sample_batch import MultiAgentBatch
 from ray.rllib.utils.annotations import override
 from ray.rllib.utils.checkpoints import Checkpointable
@@ -188,7 +188,6 @@ def __call__(self, batch: Dict[str, numpy.ndarray]) -> Dict[str, numpy.ndarray]:
                 self._is_multi_agent,
                 batch,
                 to_numpy=True,
-                schema=SCHEMA | self.config.input_read_schema,
                 input_compress_columns=self.config.input_compress_columns,
             )[
                 "episodes"
@@ -214,13 +213,7 @@ def __call__(self, batch: Dict[str, numpy.ndarray]) -> Dict[str, numpy.ndarray]:
         # Otherwise we map the batch to episodes.
         else:
             episodes: List[SingleAgentEpisode] = self._map_to_episodes(
-                self._is_multi_agent,
-                batch,
-                schema=SCHEMA | self.config.input_read_schema,
-                to_numpy=False,
-                input_compress_columns=self.config.input_compress_columns,
-                observation_space=self.observation_space,
-                action_space=self.action_space,
+                batch, to_numpy=False
             )["episodes"]
 
         episode_dicts = []