Introduce LearningLogger

Summary: Introduce protocol LearningLogger for logging with `offline_learning`.

Reviewed By: PavlosApo

Differential Revision: D65361620

fbshipit-source-id: 9fe9c9a4752cb549185c163d37c282a90d55921a

pearl/utils/functional_utils/train_and_eval/learning_logger.py

@@ -0,0 +1,26 @@
+# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
+
+# pyre-strict
+
+from typing import Any, Dict, Protocol
+
+
+class LearningLogger(Protocol):
+    """Protocol for a learning logger.
+    A learning logger is a callable that takes in a dictionary of results and a step number.
+    It can be used to log the results of a learning process to a database or a file.
+    Args:
+        results: A dictionary of results.
+        step: The current step of the learning process.
+        prefix: A prefix to add to the logged results.
+    """
+
+    def __call__(self, results: Dict[str, Any], step: int, prefix: str = "") -> None:
+        pass
+
+
+def null_learning_logger(results: Dict[str, str], step: int, prefix: str = "") -> None:
+    """
+    A null learning logger that does nothing.
+    """
+    pass

pearl/utils/functional_utils/train_and_eval/offline_learning_and_evaluation.py

@@ -21,6 +21,10 @@
 from pearl.replay_buffers.transition import TransitionBatch
 from pearl.utils.functional_utils.experimentation.set_seed import set_seed
 from pearl.utils.functional_utils.requests_get import requests_get
+from pearl.utils.functional_utils.train_and_eval.learning_logger import (
+    LearningLogger,
+    null_learning_logger,
+)
 from pearl.utils.functional_utils.train_and_eval.online_learning import run_episode
 from pearl.utils.instantiations.spaces.discrete_action import DiscreteActionSpace
 
@@ -131,6 +135,7 @@ def offline_learning(
     offline_agent: PearlAgent,
     data_buffer: ReplayBuffer,
     training_epochs: int = 1000,
+    logger: LearningLogger = null_learning_logger,
     seed: int = 100,
 ) -> None:
     """
@@ -153,8 +158,7 @@ def offline_learning(
         batch = data_buffer.sample(offline_agent.policy_learner.batch_size)
         assert isinstance(batch, TransitionBatch)
         loss = offline_agent.learn_batch(batch=batch)
-        if i % 500 == 0:
-            print("training epoch", i, "training loss", loss)
+        logger(loss, i, "training")
 
 
 def offline_evaluation(

pearl/utils/functional_utils/train_and_eval/online_learning.py

@@ -80,6 +80,7 @@ def online_learning(
     print_every_x_steps: Optional[int] = None,
     seed: Optional[int] = None,
     record_period: int = 1,
+    # TODO: use LearningLogger similarly to offline_learning
 ) -> Dict[str, Any]:
     """
     Performs online learning for a number of episodes.