Add a Builder class which encapsulates a full agent.

This class allows for the consituent components to be broken apart so that it
can be used both for distributed and non-distributed variants. For the
time-being this is only incorporated into the TF D4PG agent to allow for
minimal disruption and experimentation, but should be rolled out for all agents
soon.

PiperOrigin-RevId: 356975846
Change-Id: I00ead33da40f4f98052ae3beb218c23788ada206

acme/agents/builders.py

@@ -0,0 +1,103 @@
+# python3
+# Copyright 2018 DeepMind Technologies Limited. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""RL agent Builder interface."""
+
+import abc
+from typing import Iterator, List, Optional
+
+from acme import adders
+from acme import core
+from acme import specs
+from acme.utils import counting
+from acme.utils import loggers
+import reverb
+
+
+class ActorLearnerBuilder(abc.ABC):
+  """Defines an interface for defining the components of an RL agent.
+
+  Implementations of this interface contain a complete specification of a
+  concrete RL agent. An instance of this class can be used to build an
+  RL agent which interacts with the environment either locally or in a
+  distributed setup.
+  """
+
+  @abc.abstractmethod
+  def make_replay_tables(
+      self,
+      environment_spec: specs.EnvironmentSpec,
+  ) -> List[reverb.Table]:
+    """Create tables to insert data into."""
+
+  @abc.abstractmethod
+  def make_dataset_iterator(
+      self,
+      replay_client: reverb.Client,
+  ) -> Iterator[reverb.ReplaySample]:
+    """Create a dataset iterator to use for learning/updating the agent."""
+
+  @abc.abstractmethod
+  def make_adder(
+      self,
+      replay_client: reverb.Client,
+  ) -> Optional[adders.Adder]:
+    """Create an adder which records data generated by the actor/environment.
+
+    Args:
+      replay_client: Reverb Client which points to the replay server.
+    """
+
+  @abc.abstractmethod
+  def make_actor(
+      self,
+      policy_network,
+      adder: Optional[adders.Adder] = None,
+      variable_source: Optional[core.VariableSource] = None,
+  ) -> core.Actor:
+    """Create an actor instance.
+
+    Args:
+      policy_network: Instance of a policy network; this should be a callable
+        which takes as input observations and returns actions.
+      adder: How data is recorded (e.g. added to replay).
+      variable_source: A source providing the necessary actor parameters.
+    """
+
+  @abc.abstractmethod
+  def make_learner(
+      self,
+      networks,
+      dataset: Iterator[reverb.ReplaySample],
+      replay_client: Optional[reverb.Client] = None,
+      counter: Optional[counting.Counter] = None,
+      # TODO(mwhoffman): consider eliminating logger and log return values.
+      # TODO(mwhoffman): eliminate checkpoint and move it outside.
+      logger: Optional[loggers.Logger] = None,
+      checkpoint: bool = False,
+  ) -> core.Learner:
+    """Creates an instance of the learner.
+
+    Args:
+      networks: struct describing the networks needed by the learner; this can
+        be specific to the learner in question.
+      dataset: iterator over samples from replay.
+      replay_client: client which allows communication with replay, e.g. in
+        order to update priorities.
+      counter: a Counter which allows for recording of counts (learner steps,
+        actor steps, etc.) distributed throughout the agent.
+      logger: Logger object for logging metadata.
+      checkpoint: bool controlling whether the learner checkpoints itself.
+    """