[RLlib] New ConnectorV3 API #05: PPO runs in single-agent mode in this API stack by sven1977 · Pull Request #42272 · ray-project/ray

sven1977 · 2024-01-09T20:07:57Z

EnvRunners support new ConnectorV3 API; PPO runs in single-agent mode in this API stack
This PR:

Adds a new config key: train_batch_size_per_learner to better distinguish between total effective batch size and batch size per (GPU) learner worker.
Makes large changes to the PPO algorithm when run with the new API stack + EnvRunners:
- Forwards episode data directly from EnvRunner(s) to Learner worker(s) w/o having to form a MultiAgentBatch first.
- Removes need for PPO's forward_exploration to perform a value-function pass. This is an essential improvement in code quality as we now have full separation between the sampling- and the learning worlds. The EnvRunner (sampling world) is no longer concerned with having to think about what the PPOLearner (learning world) might need and only needs to compute actions for the next env step.
- All vf-computations, GAE, and advantages computations have been moved to the Learner side and these operations are now performed in a batched fashion (on all provided episodes at once). Having the episodes still intact on the Learner side helps reducing the complexity of these computations.

Benchmark results:
Learns Pong in ~5min via examples/connectors/connector_v2_frame_stacking.py example script:

Args: --num-gpus=8 --num-env-runners=95 --framework=torch

on commit: 790a537

Trial status: 1 RUNNING
Current time: 2024-01-12 12:41:55. Total running time: 7min 0s
Logical resource usage: 96.0/96 CPUs, 8.0/8 GPUs (0.0/1.0 accelerator_type:V100)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name            status       iter     total time (s)       ts     reward     episode_reward_max     episode_reward_min     episode_len_mean     episodes_this_iter │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ PPO_env_0b2b7_00000   RUNNING       226             362.71   904000      19.62                     21                      9              1728.96                      0 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Why are these changes needed?

Related issue number

Checks

I've signed off every commit(by using the -s flag, i.e., git commit -s) in this PR.
I've run scripts/format.sh to lint the changes in this PR.
I've included any doc changes needed for https://docs.ray.io/en/master/.
- I've added any new APIs to the API Reference. For example, if I added a
  method in Tune, I've added it in doc/source/tune/api/ under the
  corresponding .rst file.
I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
Testing Strategy
- Unit tests
- Release tests
- This PR is not tested :(

Signed-off-by: sven1977 <svenmika1977@gmail.com>

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sven1977 · 2024-01-16T11:54:17Z

rllib/algorithms/ppo/ppo_learner.py

        )

+    @override(Learner)
+    def _preprocess_train_data(


Note: Only called on the new API stack + EnvRunners.

sven1977 · 2024-01-16T11:57:09Z

rllib/algorithms/ppo/ppo_learner.py

+        if not episodes:
+            return batch, episodes
+
+        # Make all episodes one ts longer in order to just have a single batch


New way to do GAE:

elongate all episodes by one artificial ts.

perform vf-predictions AND bootstrap value predictions in one single batch (b/c we have the extra timestep!)

use the learner connector to make sure this forward pass is done using the correct (custom?) batch format.

remove extra timesteps from episodes (and computed advantages)

sven1977 · 2024-01-16T11:57:59Z

rllib/algorithms/ppo/ppo_rl_module.py

-            SampleBatch.VF_PREDS,
-            SampleBatch.ACTION_DIST_INPUTS,
-        ]
+        return self.output_specs_inference()


sven1977 · 2024-01-16T11:59:04Z

rllib/algorithms/ppo/tf/ppo_tf_rl_module.py

        """
+        # TODO (sven): Make this the only bahevior once PPO has been migrated
+        #  to new API stack (including EnvRunners!).
+        if self.config.model_config_dict.get("uses_new_env_runners"):


temporary hack to make sure RLModule knows, when it still has to compute vf-preds via forward_exploration (old and hybrid API stacks).

sven1977 · 2024-01-16T11:59:38Z

rllib/core/learner/learner.py

        # the final results dict in the `self.compile_update_results()` method.
        self._metrics = defaultdict(dict)

+    @OverrideToImplementCustomLogic_CallToSuperRecommended


Moved here for better ordering of methods (used to be all the way at the bottom of class).

sven1977 · 2024-01-16T12:00:15Z

rllib/core/learner/learner.py

+
+        # Build learner connector pipeline used on this Learner worker.
+        # TODO (sven): Support multi-agent cases.
+        if self.config.uses_new_env_runners and not self.config.is_multi_agent():


For now: Only on new API stack + EnvRunner + single-agent: use Learner connector (w/o this PPO on new stack would not learn).

sven1977 · 2024-01-16T12:01:08Z

rllib/utils/minibatch_utils.py

                    def get_len(b):
                        return len(b[SampleBatch.SEQ_LENS])

+                    n_steps = int(


Bug fix. When slicing on a BxT batch, we should slice properly along B-axis (with the correct slice size!).

sven1977 · 2024-01-16T12:01:18Z

rllib/policy/sample_batch.py

-                    return value
-
-            data = tree.map_structure(map_, self)
+            infos = self.pop(SampleBatch.INFOS, None)


Simplifications.

sven1977 · 2024-01-16T12:01:24Z

rllib/policy/sample_batch.py

-                            # we return the values here and slice them separately
-                            # TODO(Artur): Clean this hack up.
-                            return value
+                    return value[start_padded:stop_padded]


Simplifications.

kouroshHakha

Stmp

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Mark2000 · 2024-05-02T07:22:27Z

@sven1977 Could you speak more to why GAE support was dropped for APPO in this release?

        # IMPALA and APPO need vtrace (A3C Policies no longer exist).
         if not self.vtrace:
             raise ValueError(
                 "IMPALA and APPO do NOT support vtrace=False anymore! Set "
                 "`config.training(vtrace=True)`."
             )

… API stack. (ray-project#42272)

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sven1977 requested review from ArturNiederfahrenhorst, avnishn, kouroshHakha, maxpumperla and smorad as code owners January 9, 2024 20:07

sven1977 added do-not-merge Do not merge this PR! rllib-newstack labels Jan 9, 2024

sven1977 commented Jan 16, 2024

View reviewed changes

kouroshHakha approved these changes Jan 17, 2024

View reviewed changes

sven1977 added 17 commits January 18, 2024 09:25

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sven1977 merged commit e03dd6e into ray-project:master Jan 19, 2024

simonsays1980 pushed a commit to simonsays1980/ray that referenced this pull request Dec 17, 2025

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[RLlib] New ConnectorV3 API #05: PPO runs in single-agent mode in this API stack#42272

[RLlib] New ConnectorV3 API #05: PPO runs in single-agent mode in this API stack#42272
sven1977 merged 57 commits intoray-project:masterfrom
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