[RLlib] DatasetReader action normalization

rllib/evaluation/tests/test_rollout_worker.py

@@ -1,9 +1,11 @@
 from collections import Counter
 import gym
 from gym.spaces import Box, Discrete
+import json
 import numpy as np
 import os
 import random
+import tempfile
 import time
 import unittest
 
@@ -22,6 +24,8 @@
 )
 from ray.rllib.examples.env.multi_agent import BasicMultiAgent, MultiAgentCartPole
 from ray.rllib.examples.policy.random_policy import RandomPolicy
+from ray.rllib.offline.dataset_reader import DatasetReader, get_dataset_and_shards
+from ray.rllib.offline.json_reader import JsonReader
 from ray.rllib.policy.policy import Policy, PolicySpec
 from ray.rllib.policy.sample_batch import (
     DEFAULT_POLICY_ID,
@@ -358,6 +362,90 @@ def test_action_normalization(self):
         self.assertLess(np.min(sample["actions"]), action_space.low[0])
         ev.stop()
 
+    def test_action_normalization_offline_dataset(self):
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            # create environment
+            env = gym.make("Pendulum-v1")
+
+            # create temp data with actions at min and max
+            data = {
+                "type": "SampleBatch",
+                "actions": [[2.0], [-2.0]],
+                "dones": [0.0, 0.0],
+                "rewards": [0.0, 0.0],
+                "obs": [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
+                "new_obs": [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
+            }
+
+            data_file = os.path.join(tmp_dir, "data.json")
+
+            with open(data_file, "w") as f:
+                json.dump(data, f)
+
+            # create input reader functions
+            def dataset_reader_creator(ioctx):
+                config = {
+                    "input": "dataset",
+                    "input_config": {"format": "json", "paths": data_file},
+                }
+                _, shards = get_dataset_and_shards(config, num_workers=0)
+                return DatasetReader(shards[0], ioctx)
+
+            def json_reader_creator(ioctx):
+                return JsonReader(data_file, ioctx)
+
+            input_creators = [dataset_reader_creator, json_reader_creator]
+
+            # check that if actions_in_input_normalized is False
+            # it will normalize input
+            for input_creator in input_creators:
+                ev = RolloutWorker(
+                    env_creator=lambda _: env,
+                    policy_spec=MockPolicy,
+                    policy_config=dict(
+                        actions_in_input_normalized=False,
+                        normalize_actions=True,
+                        clip_actions=False,
+                        offline_sampling=True,
+                        train_batch_size=1,
+                    ),
+                    rollout_fragment_length=1,
+                    input_creator=input_creator,
+                )
+
+                sample = ev.sample()
+
+                # check if the samples from dataset are normalized properly
+                self.assertLessEqual(np.max(sample["actions"]), 1.0)
+                self.assertGreaterEqual(np.min(sample["actions"]), -1.0)
+
+                ev.stop()
+
+            # check that if actions_in_input_normalized is True
+            # it will not normalize input
+            for input_creator in input_creators:
+                ev = RolloutWorker(
+                    env_creator=lambda _: env,
+                    policy_spec=MockPolicy,
+                    policy_config=dict(
+                        actions_in_input_normalized=True,
+                        normalize_actions=True,
+                        clip_actions=False,
+                        offline_sampling=True,
+                        train_batch_size=1,
+                    ),
+                    rollout_fragment_length=1,
+                    input_creator=input_creator,
+                )
+
+                sample = ev.sample()
+
+                # check if the samples from dataset are not normalized
+                self.assertGreater(np.max(sample["actions"]), 1.0)
+                self.assertLess(np.min(sample["actions"]), -1.0)
+
+                ev.stop()
+
     def test_action_immutability(self):
         from ray.rllib.examples.env.random_env import RandomEnv
 

rllib/offline/dataset_reader.py

@@ -8,7 +8,7 @@
 import ray.data
 from ray.rllib.offline.input_reader import InputReader
 from ray.rllib.offline.io_context import IOContext
-from ray.rllib.offline.json_reader import from_json_data
+from ray.rllib.offline.json_reader import from_json_data, postprocess_actions
 from ray.rllib.policy.sample_batch import concat_samples, SampleBatch, DEFAULT_POLICY_ID
 from ray.rllib.utils.annotations import override, PublicAPI
 from ray.rllib.utils.typing import SampleBatchType, AlgorithmConfigDict
@@ -251,6 +251,7 @@ def next(self) -> SampleBatchType:
             d = next(self._iter).as_pydict()
             # Columns like obs are compressed when written by DatasetWriter.
             d = from_json_data(d, self._ioctx.worker)
+            d = postprocess_actions(d, self._ioctx)
             count += d.count
             ret.append(self._postprocess_if_needed(d))
         ret = concat_samples(ret)

rllib/offline/json_reader.py

@@ -83,6 +83,78 @@ def _adjust_obs_actions_for_policy(json_data: dict, policy: Policy) -> dict:
     return json_data
 
 
+@DeveloperAPI
+def postprocess_actions(batch: SampleBatchType, ioctx: IOContext) -> SampleBatchType:
+    # Clip actions (from any values into env's bounds), if necessary.
+    cfg = ioctx.config
+    # TODO(jungong) : we should not clip_action in input reader.
+    # Use connector to handle this.
+    if cfg.get("clip_actions"):
+        if ioctx.worker is None:
+            raise ValueError(
+                "clip_actions is True but cannot clip actions since no workers exist"
+            )
+
+        if isinstance(batch, SampleBatch):
+            default_policy = ioctx.worker.policy_map.get(DEFAULT_POLICY_ID)
+            batch[SampleBatch.ACTIONS] = clip_action(
+                batch[SampleBatch.ACTIONS], default_policy.action_space_struct
+            )
+        else:
+            for pid, b in batch.policy_batches.items():
+                b[SampleBatch.ACTIONS] = clip_action(
+                    b[SampleBatch.ACTIONS],
+                    ioctx.worker.policy_map[pid].action_space_struct,
+                )
+    # Re-normalize actions (from env's bounds to zero-centered), if
+    # necessary.
+    if (
+        cfg.get("actions_in_input_normalized") is False
+        and cfg.get("normalize_actions") is True
+    ):
+        if ioctx.worker is None:
+            raise ValueError(
+                "actions_in_input_normalized is False but"
+                "cannot normalize actions since no workers exist"
+            )
+
+        # If we have a complex action space and actions were flattened
+        # and we have to normalize -> Error.
+        error_msg = (
+            "Normalization of offline actions that are flattened is not "
+            "supported! Make sure that you record actions into offline "
+            "file with the `_disable_action_flattening=True` flag OR "
+            "as already normalized (between -1.0 and 1.0) values. "
+            "Also, when reading already normalized action values from "
+            "offline files, make sure to set "
+            "`actions_in_input_normalized=True` so that RLlib will not "
+            "perform normalization on top."
+        )
+
+        if isinstance(batch, SampleBatch):
+            pol = ioctx.worker.policy_map.get(DEFAULT_POLICY_ID)
+            if isinstance(
+                pol.action_space_struct, (tuple, dict)
+            ) and not pol.config.get("_disable_action_flattening"):
+                raise ValueError(error_msg)
+            batch[SampleBatch.ACTIONS] = normalize_action(
+                batch[SampleBatch.ACTIONS], pol.action_space_struct
+            )
+        else:
+            for pid, b in batch.policy_batches.items():
+                pol = ioctx.worker.policy_map[pid]
+                if isinstance(
+                    pol.action_space_struct, (tuple, dict)
+                ) and not pol.config.get("_disable_action_flattening"):
+                    raise ValueError(error_msg)
+                b[SampleBatch.ACTIONS] = normalize_action(
+                    b[SampleBatch.ACTIONS],
+                    ioctx.worker.policy_map[pid].action_space_struct,
+                )
+
+    return batch
+
+
 @DeveloperAPI
 def from_json_data(json_data: Any, worker: Optional["RolloutWorker"]):
     # Try to infer the SampleBatchType (SampleBatch or MultiAgentBatch).
@@ -290,61 +362,8 @@ def _try_parse(self, line: str) -> Optional[SampleBatchType]:
             )
             return None
 
-        # Clip actions (from any values into env's bounds), if necessary.
-        cfg = self.ioctx.config
-        # TODO(jungong) : we should not clip_action in input reader.
-        # Use connector to handle this.
-        if cfg.get("clip_actions") and self.ioctx.worker is not None:
-            if isinstance(batch, SampleBatch):
-                batch[SampleBatch.ACTIONS] = clip_action(
-                    batch[SampleBatch.ACTIONS], self.default_policy.action_space_struct
-                )
-            else:
-                for pid, b in batch.policy_batches.items():
-                    b[SampleBatch.ACTIONS] = clip_action(
-                        b[SampleBatch.ACTIONS],
-                        self.ioctx.worker.policy_map[pid].action_space_struct,
-                    )
-        # Re-normalize actions (from env's bounds to zero-centered), if
-        # necessary.
-        if (
-            cfg.get("actions_in_input_normalized") is False
-            and self.ioctx.worker is not None
-        ):
+        batch = postprocess_actions(batch, self.ioctx)
 
-            # If we have a complex action space and actions were flattened
-            # and we have to normalize -> Error.
-            error_msg = (
-                "Normalization of offline actions that are flattened is not "
-                "supported! Make sure that you record actions into offline "
-                "file with the `_disable_action_flattening=True` flag OR "
-                "as already normalized (between -1.0 and 1.0) values. "
-                "Also, when reading already normalized action values from "
-                "offline files, make sure to set "
-                "`actions_in_input_normalized=True` so that RLlib will not "
-                "perform normalization on top."
-            )
-
-            if isinstance(batch, SampleBatch):
-                pol = self.default_policy
-                if isinstance(
-                    pol.action_space_struct, (tuple, dict)
-                ) and not pol.config.get("_disable_action_flattening"):
-                    raise ValueError(error_msg)
-                batch[SampleBatch.ACTIONS] = normalize_action(
-                    batch[SampleBatch.ACTIONS], pol.action_space_struct
-                )
-            else:
-                for pid, b in batch.policy_batches.items():
-                    pol = self.policy_map[pid]
-                    if isinstance(
-                        pol.action_space_struct, (tuple, dict)
-                    ) and not pol.config.get("_disable_action_flattening"):
-                        raise ValueError(error_msg)
-                    b[SampleBatch.ACTIONS] = normalize_action(
-                        b[SampleBatch.ACTIONS],
-                        self.ioctx.worker.policy_map[pid].action_space_struct,
-                    )
         return batch
 
     def _next_line(self) -> str:

rllib/tuned_examples/crr/pendulum-v1-crr.yaml

@@ -20,7 +20,7 @@ pendulum_crr:
         actor_hidden_activation: 'relu'
         actor_hiddens: [256, 256]
         actor_lr: 0.0003
-        actions_in_input_normalized: True
+        actions_in_input_normalized: False
         clip_actions: True
         # Q function update setting
         twin_q: True