[RLlib] New ConnectorV2 API #04: Changes to Learner/LearnerGroup API to allow updating from Episodes.

doc/source/rllib/package_ref/learner.rst

@@ -72,7 +72,8 @@ Performing Updates
     :nosignatures:
     :toctree: doc/
 
-    Learner.update
+    Learner.update_from_batch
+    Learner.update_from_episodes
     Learner._update
     Learner.additional_update
     Learner.additional_update_for_module

doc/source/rllib/rllib-learner.rst

@@ -229,19 +229,23 @@ Updates
 
         .. testcode::
 
-            # This is a blocking update
-            results = learner_group.update(DUMMY_BATCH)
+            # This is a blocking update.
+            results = learner_group.update_from_batch(batch=DUMMY_BATCH)
 
             # This is a non-blocking update. The results are returned in a future
-            # call to `async_update`
-            _ = learner_group.async_update(DUMMY_BATCH)
+            # call to `update_from_batch(..., async_update=True)`
+            _ = learner_group.update_from_batch(batch=DUMMY_BATCH, async_update=True)
 
             # Artificially wait for async request to be done to get the results
-            # in the next call to `LearnerGroup.async_update()`.
+            # in the next call to
+            # `LearnerGroup.update_from_batch(..., async_update=True)`.
             time.sleep(5)
-            results = learner_group.async_update(DUMMY_BATCH)
+            results = learner_group.update_from_batch(
+                batch=DUMMY_BATCH, async_update=True
+            )
             # `results` is a list of results dict. The items in the list represent the different
-            # remote results from the different calls to `async_update()`.
+            # remote results from the different calls to
+            # `update_from_batch(..., async_update=True)`.
             assert len(results) > 0
             # Each item is a results dict, already reduced over the n Learner workers.
             assert isinstance(results[0], dict), results[0]
@@ -256,8 +260,8 @@ Updates
 
         .. testcode::
 
-            # This is a blocking update.
-            result = learner.update(DUMMY_BATCH)
+            # This is a blocking update (given a training batch).
+            result = learner.update_from_batch(batch=DUMMY_BATCH)
 
             # This is an additional non-gradient based update.
             learner_group.additional_update(**ADDITIONAL_UPDATE_KWARGS)

rllib/algorithms/algorithm.py

@@ -5,12 +5,12 @@
 import functools
 import gymnasium as gym
 import importlib
+import importlib.metadata
 import json
 import logging
 import numpy as np
 import os
 from packaging import version
-import importlib.metadata
 import re
 import tempfile
 import time
@@ -1607,7 +1607,7 @@ def training_step(self) -> ResultDict:
             # TODO: (sven) rename MultiGPUOptimizer into something more
             #  meaningful.
             if self.config._enable_new_api_stack:
-                train_results = self.learner_group.update(train_batch)
+                train_results = self.learner_group.update_from_batch(batch=train_batch)
             elif self.config.get("simple_optimizer") is True:
                 train_results = train_one_step(self, train_batch)
             else:

rllib/algorithms/appo/tests/test_appo_learner.py

@@ -97,7 +97,7 @@ def test_appo_loss(self):
                 env=algo.workers.local_worker().env
             )
             learner_group.set_weights(algo.get_weights())
-            learner_group.update(train_batch.as_multi_agent())
+            learner_group.update_from_batch(batch=train_batch.as_multi_agent())
 
             algo.stop()
 

rllib/algorithms/bc/bc.py

@@ -171,7 +171,7 @@ def training_step(self) -> ResultDict:
                 self._counters[NUM_ENV_STEPS_SAMPLED] += train_batch.env_steps()
 
             # Updating the policy.
-            train_results = self.learner_group.update(train_batch)
+            train_results = self.learner_group.update_from_batch(batch=train_batch)
 
             # Synchronize weights.
             # As the results contain for each policy the loss and in addition the

rllib/algorithms/dreamerv3/dreamerv3.py

@@ -606,8 +606,8 @@ def training_step(self) -> ResultDict:
                     )
 
                 # Perform the actual update via our learner group.
-                train_results = self.learner_group.update(
-                    SampleBatch(sample).as_multi_agent(),
+                train_results = self.learner_group.update_from_batch(
+                    batch=SampleBatch(sample).as_multi_agent(),
                     reduce_fn=self._reduce_results,
                 )
                 self._counters[NUM_AGENT_STEPS_TRAINED] += replayed_steps

rllib/algorithms/dreamerv3/utils/summaries.py

@@ -133,7 +133,8 @@ def report_predicted_vs_sampled_obs(
     Continues: Compute MSE (sampled vs predicted).
 
     Args:
-        results: The results dict that was returned by `LearnerGroup.update()`.
+        results: The results dict that was returned by
+            `LearnerGroup.update_from_batch()`.
         sample: The sampled data (dict) from the replay buffer. Already tf-tensor
             converted.
         batch_size_B: The batch size (B). This is the number of trajectories sampled

rllib/algorithms/impala/impala.py

@@ -946,24 +946,18 @@ def learn_on_processed_samples(self) -> ResultDict:
             self.batches_to_place_on_learner.clear()
             # If there are no learner workers and learning is directly on the driver
             # Then we can't do async updates, so we need to block.
-            blocking = self.config.num_learner_workers == 0
+            async_update = self.config.num_learner_workers > 0
             results = []
             for batch in batches:
-                if blocking:
-                    result = self.learner_group.update(
-                        batch,
-                        reduce_fn=_reduce_impala_results,
-                        num_iters=self.config.num_sgd_iter,
-                        minibatch_size=self.config.minibatch_size,
-                    )
+                result = self.learner_group.update_from_batch(
+                    batch=batch,
+                    async_update=async_update,
+                    reduce_fn=_reduce_impala_results,
+                    num_iters=self.config.num_sgd_iter,
+                    minibatch_size=self.config.minibatch_size,
+                )
+                if not async_update:
                     results = [result]
-                else:
-                    results = self.learner_group.async_update(
-                        batch,
-                        reduce_fn=_reduce_impala_results,
-                        num_iters=self.config.num_sgd_iter,
-                        minibatch_size=self.config.minibatch_size,
-                    )
 
                 for r in results:
                     self._counters[NUM_ENV_STEPS_TRAINED] += r[ALL_MODULES].pop(
@@ -973,14 +967,14 @@ def learn_on_processed_samples(self) -> ResultDict:
                         NUM_AGENT_STEPS_TRAINED
                     )
 
-            self._counters.update(self.learner_group.get_in_queue_stats())
+            self._counters.update(self.learner_group.get_stats())
             # If there are results, reduce-mean over each individual value and return.
             if results:
                 return tree.map_structure(lambda *x: np.mean(x), *results)
 
         # Nothing on the queue -> Don't send requests to learner group
-        # or no results ready (from previous `self.learner_group.update()` calls) for
-        # reducing.
+        # or no results ready (from previous `self.learner_group.update_from_batch()`
+        # calls) for reducing.
         return {}
 
     def place_processed_samples_on_learner_thread_queue(self) -> None:

rllib/algorithms/impala/tests/test_impala_learner.py

@@ -94,7 +94,7 @@ def test_impala_loss(self):
                 env=algo.workers.local_worker().env
             )
             learner_group.set_weights(algo.get_weights())
-            learner_group.update(train_batch.as_multi_agent())
+            learner_group.update_from_batch(batch=train_batch.as_multi_agent())
 
             algo.stop()
 

rllib/algorithms/ppo/ppo.py

@@ -424,8 +424,8 @@ def training_step(self) -> ResultDict:
         if self.config._enable_new_api_stack:
             # TODO (Kourosh) Clearly define what train_batch_size
             #  vs. sgd_minibatch_size and num_sgd_iter is in the config.
-            train_results = self.learner_group.update(
-                train_batch,
+            train_results = self.learner_group.update_from_batch(
+                batch=train_batch,
                 minibatch_size=self.config.sgd_minibatch_size,
                 num_iters=self.config.num_sgd_iter,
             )

rllib/algorithms/ppo/tests/test_ppo_learner.py

@@ -101,7 +101,7 @@ def test_loss(self):
 
             # Load the algo weights onto the learner_group.
             learner_group.set_weights(algo.get_weights())
-            learner_group.update(train_batch.as_multi_agent())
+            learner_group.update_from_batch(batch=train_batch.as_multi_agent())
 
             algo.stop()
 

rllib/algorithms/ppo/tests/test_ppo_with_rl_module.py

@@ -225,7 +225,7 @@ def get_value():
             assert init_std == 0.0, init_std
             batch = compute_gae_for_sample_batch(policy, PENDULUM_FAKE_BATCH.copy())
             batch = policy._lazy_tensor_dict(batch)
-            algo.learner_group.update(batch.as_multi_agent())
+            algo.learner_group.update_from_batch(batch=batch.as_multi_agent())
 
             # Check the variable is updated.
             post_std = get_value()