[RLlib] Properly serialize and restore StateBufferConnector states for policy stashing

rllib/connectors/agent/state_buffer.py

@@ -1,28 +1,56 @@
 from collections import defaultdict
+import logging
+import pickle
 from typing import Any
 
 import numpy as np
+from ray.rllib.utils.annotations import override
 import tree  # dm_tree
 
 from ray.rllib.connectors.connector import (
     AgentConnector,
+    Connector,
     ConnectorContext,
 )
+from ray import cloudpickle
 from ray.rllib.connectors.registry import register_connector
 from ray.rllib.policy.sample_batch import SampleBatch
 from ray.rllib.utils.spaces.space_utils import get_base_struct_from_space
 from ray.rllib.utils.typing import ActionConnectorDataType, AgentConnectorDataType
 from ray.util.annotations import PublicAPI
 
 
+logger = logging.getLogger(__name__)
+
+
 @PublicAPI(stability="alpha")
 class StateBufferConnector(AgentConnector):
-    def __init__(self, ctx: ConnectorContext):
+    def __init__(self, ctx: ConnectorContext, states: Any = None):
         super().__init__(ctx)
 
         self._initial_states = ctx.initial_states
         self._action_space_struct = get_base_struct_from_space(ctx.action_space)
+
         self._states = defaultdict(lambda: defaultdict(lambda: (None, None, None)))
+        # TODO(jungong) : we would not need this if policies are never stashed
+        # during the rollout of a single episode.
+        if states:
+            try:
+                self._states = cloudpickle.loads(states)
+            except pickle.UnpicklingError:
+                # StateBufferConnector states are only needed for rare cases
+                # like stashing then restoring a policy during the rollout of
+                # a single episode.
+                # It is ok to ignore the error for most of the cases here.
+                logger.info(
+                    "Can not restore StateBufferConnector states. This warning can "
+                    "usually be ignore, unless it is from restoring a stashed policy."
+                )
+
+    @override(Connector)
+    def in_eval(self):
+        self._states.clear()
+        super().in_eval()
 
     def reset(self, env_id: str):
         # States should not be carried over between episodes.
@@ -70,11 +98,18 @@ def transform(self, ac_data: AgentConnectorDataType) -> AgentConnectorDataType:
         return ac_data
 
     def to_state(self):
-        return StateBufferConnector.__name__, None
+        # Note(jungong) : it is ok to use cloudpickle here for stats because:
+        # 1. self._states may contain arbitary data objects, and will be hard
+        #     to serialize otherwise.
+        # 2. seriazlized states are only useful if a policy is stashed and
+        #     restored during the rollout of a single episode. So it is ok to
+        #     use cloudpickle for such non-persistent data bits.
+        states = cloudpickle.dumps(self._states)
+        return StateBufferConnector.__name__, states
 
     @staticmethod
     def from_state(ctx: ConnectorContext, params: Any):
-        return StateBufferConnector(ctx)
+        return StateBufferConnector(ctx, params)
 
 
 register_connector(StateBufferConnector.__name__, StateBufferConnector)

rllib/connectors/agent/view_requirement.py

@@ -79,8 +79,9 @@ def transform(self, ac_data: AgentConnectorDataType) -> AgentConnectorDataType:
             "and agent_id({agent_id})"
         )
 
-        vr = self._view_requirements
-        assert vr, "ViewRequirements required by ViewRequirementAgentConnector"
+        assert (
+            self._view_requirements
+        ), "ViewRequirements required by ViewRequirementAgentConnector"
 
         # Note(jungong) : we need to keep the entire input dict here.
         # A column may be used by postprocessing (GAE) even if its

rllib/evaluation/collectors/agent_collector.py

@@ -420,7 +420,7 @@ def build_for_inference(self) -> SampleBatch:
         return batch
 
     # TODO: @kouorsh we don't really need view_requirements anymore since it's already
-    # and attribute of the class
+    # an attribute of the class
     def build_for_training(
         self, view_requirements: ViewRequirementsDict
     ) -> SampleBatch:

rllib/evaluation/collectors/simple_list_collector.py

@@ -102,9 +102,7 @@ def build(self):
 
 class _PolicyCollectorGroup:
     def __init__(self, policy_map):
-        self.policy_collectors = {
-            pid: _PolicyCollector(policy) for pid, policy in policy_map.items()
-        }
+        self.policy_collectors = {}
         # Total env-steps (1 env-step=up to N agents stepped).
         self.env_steps = 0
         # Total agent steps (1 agent-step=1 individual agent (out of N)

rllib/evaluation/env_runner_v2.py

@@ -1124,7 +1124,7 @@ def _process_policy_eval_results(
 
                 # Notify agent connectors with this new policy output.
                 # Necessary for state buffering agent connectors, for example.
-                ac_data: AgentConnectorDataType = ActionConnectorDataType(
+                ac_data: ActionConnectorDataType = ActionConnectorDataType(
                     env_id,
                     agent_id,
                     input_dict,

rllib/policy/policy.py

@@ -970,14 +970,14 @@ def restore_connectors(self, state: PolicyState):
             self.agent_connectors = restore_connectors_for_policy(
                 self, connector_configs["agent"]
             )
-            logger.info("restoring agent connectors:")
-            logger.info(self.agent_connectors.__str__(indentation=4))
+            logger.debug("restoring agent connectors:")
+            logger.debug(self.agent_connectors.__str__(indentation=4))
         if "action" in connector_configs:
             self.action_connectors = restore_connectors_for_policy(
                 self, connector_configs["action"]
             )
-            logger.info("restoring action connectors:")
-            logger.info(self.action_connectors.__str__(indentation=4))
+            logger.debug("restoring action connectors:")
+            logger.debug(self.action_connectors.__str__(indentation=4))
 
     @DeveloperAPI
     @OverrideToImplementCustomLogic_CallToSuperRecommended

rllib/policy/policy_map.py

@@ -1,6 +1,7 @@
 from collections import deque
 import threading
 from typing import Dict, Set
+import logging
 
 import ray
 from ray.rllib.policy.policy import Policy
@@ -12,6 +13,7 @@
 from ray.util.annotations import PublicAPI
 
 tf1, tf, tfv = try_import_tf()
+logger = logging.getLogger(__name__)
 
 
 @PublicAPI(stability="beta")
@@ -129,9 +131,10 @@ def __getitem__(self, item: PolicyID):
         # -> Load new policy's state into the one that just got removed from the cache.
         # This way, we save the costly re-creation step.
         if policy is not None and self.policy_states_are_swappable:
+            logger.debug(f"restoring policy: {item}")
             policy.set_state(policy_state)
-        #
         else:
+            logger.debug(f"creating new policy: {item}")
             policy = Policy.from_state(policy_state)
 
         self.cache[item] = policy

rllib/tuned_examples/appo/multi-agent-cartpole-w-100-policies-appo.py

@@ -8,11 +8,11 @@
 register_env("multi_cartpole", lambda _: MultiAgentCartPole({"num_agents": 2}))
 
 # Number of policies overall in the PolicyMap.
-num_policies = 100
+num_policies = 20
 # Number of those policies that should be trained. These are a subset of `num_policies`.
-num_trainable = 20
+num_trainable = 10
 
-num_envs_per_worker = 5
+num_envs_per_worker = 2
 
 # Define the config as an APPOConfig object.
 config = (

rllib/utils/test_utils.py

@@ -219,7 +219,9 @@ def check(x, y, decimals=5, atol=None, rtol=None, false=False):
         else:
             assert x == y, f"ERROR: x ({x}) is not the same as y ({y})!"
     # String/byte comparisons.
-    elif hasattr(x, "dtype") and (x.dtype == object or str(x.dtype).startswith("<U")):
+    elif (
+        hasattr(x, "dtype") and (x.dtype == object or str(x.dtype).startswith("<U"))
+    ) or isinstance(x, bytes):
         try:
             np.testing.assert_array_equal(x, y)
             if false is True: