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| 1 | +# python3 |
| 2 | +# Copyright 2018 DeepMind Technologies Limited. All rights reserved. |
| 3 | +# |
| 4 | +# Licensed under the Apache License, Version 2.0 (the "License"); |
| 5 | +# you may not use this file except in compliance with the License. |
| 6 | +# You may obtain a copy of the License at |
| 7 | +# |
| 8 | +# http://www.apache.org/licenses/LICENSE-2.0 |
| 9 | +# |
| 10 | +# Unless required by applicable law or agreed to in writing, software |
| 11 | +# distributed under the License is distributed on an "AS IS" BASIS, |
| 12 | +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 13 | +# See the License for the specific language governing permissions and |
| 14 | +# limitations under the License. |
| 15 | + |
| 16 | +"""RL agent Builder interface.""" |
| 17 | + |
| 18 | +import abc |
| 19 | +from typing import Iterator, List, Optional |
| 20 | + |
| 21 | +from acme import adders |
| 22 | +from acme import core |
| 23 | +from acme import specs |
| 24 | +from acme.utils import counting |
| 25 | +from acme.utils import loggers |
| 26 | +import reverb |
| 27 | + |
| 28 | + |
| 29 | +class ActorLearnerBuilder(abc.ABC): |
| 30 | + """Defines an interface for defining the components of an RL agent. |
| 31 | +
|
| 32 | + Implementations of this interface contain a complete specification of a |
| 33 | + concrete RL agent. An instance of this class can be used to build an |
| 34 | + RL agent which interacts with the environment either locally or in a |
| 35 | + distributed setup. |
| 36 | + """ |
| 37 | + |
| 38 | + @abc.abstractmethod |
| 39 | + def make_replay_tables( |
| 40 | + self, |
| 41 | + environment_spec: specs.EnvironmentSpec, |
| 42 | + ) -> List[reverb.Table]: |
| 43 | + """Create tables to insert data into.""" |
| 44 | + |
| 45 | + @abc.abstractmethod |
| 46 | + def make_dataset_iterator( |
| 47 | + self, |
| 48 | + replay_client: reverb.Client, |
| 49 | + ) -> Iterator[reverb.ReplaySample]: |
| 50 | + """Create a dataset iterator to use for learning/updating the agent.""" |
| 51 | + |
| 52 | + @abc.abstractmethod |
| 53 | + def make_adder( |
| 54 | + self, |
| 55 | + replay_client: reverb.Client, |
| 56 | + ) -> Optional[adders.Adder]: |
| 57 | + """Create an adder which records data generated by the actor/environment. |
| 58 | +
|
| 59 | + Args: |
| 60 | + replay_client: Reverb Client which points to the replay server. |
| 61 | + """ |
| 62 | + |
| 63 | + @abc.abstractmethod |
| 64 | + def make_actor( |
| 65 | + self, |
| 66 | + policy_network, |
| 67 | + adder: Optional[adders.Adder] = None, |
| 68 | + variable_source: Optional[core.VariableSource] = None, |
| 69 | + ) -> core.Actor: |
| 70 | + """Create an actor instance. |
| 71 | +
|
| 72 | + Args: |
| 73 | + policy_network: Instance of a policy network; this should be a callable |
| 74 | + which takes as input observations and returns actions. |
| 75 | + adder: How data is recorded (e.g. added to replay). |
| 76 | + variable_source: A source providing the necessary actor parameters. |
| 77 | + """ |
| 78 | + |
| 79 | + @abc.abstractmethod |
| 80 | + def make_learner( |
| 81 | + self, |
| 82 | + networks, |
| 83 | + dataset: Iterator[reverb.ReplaySample], |
| 84 | + replay_client: Optional[reverb.Client] = None, |
| 85 | + counter: Optional[counting.Counter] = None, |
| 86 | + # TODO(mwhoffman): consider eliminating logger and log return values. |
| 87 | + # TODO(mwhoffman): eliminate checkpoint and move it outside. |
| 88 | + logger: Optional[loggers.Logger] = None, |
| 89 | + checkpoint: bool = False, |
| 90 | + ) -> core.Learner: |
| 91 | + """Creates an instance of the learner. |
| 92 | +
|
| 93 | + Args: |
| 94 | + networks: struct describing the networks needed by the learner; this can |
| 95 | + be specific to the learner in question. |
| 96 | + dataset: iterator over samples from replay. |
| 97 | + replay_client: client which allows communication with replay, e.g. in |
| 98 | + order to update priorities. |
| 99 | + counter: a Counter which allows for recording of counts (learner steps, |
| 100 | + actor steps, etc.) distributed throughout the agent. |
| 101 | + logger: Logger object for logging metadata. |
| 102 | + checkpoint: bool controlling whether the learner checkpoints itself. |
| 103 | + """ |
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