ray/rllib/agents/dqn/dqn.py

"""
Deep Q-Networks (DQN, Rainbow, Parametric DQN)
==============================================

This file defines the distributed Trainer class for the Deep Q-Networks
algorithm. See `dqn_[tf|torch]_policy.py` for the definition of the policies.

Detailed documentation:
https://docs.ray.io/en/master/rllib-algorithms.html#deep-q-networks-dqn-rainbow-parametric-dqn
"""  # noqa: E501

import logging
from typing import List, Optional, Type

from ray.rllib.agents.dqn.dqn_tf_policy import DQNTFPolicy
from ray.rllib.agents.dqn.dqn_torch_policy import DQNTorchPolicy
from ray.rllib.agents.trainer import with_common_config
from ray.rllib.agents.trainer_template import build_trainer
from ray.rllib.evaluation.worker_set import WorkerSet
from ray.rllib.execution.concurrency_ops import Concurrently
from ray.rllib.execution.metric_ops import StandardMetricsReporting
from ray.rllib.execution.replay_buffer import LocalReplayBuffer
from ray.rllib.execution.replay_ops import Replay, StoreToReplayBuffer
from ray.rllib.execution.rollout_ops import ParallelRollouts
from ray.rllib.execution.train_ops import TrainOneStep, UpdateTargetNetwork
from ray.rllib.policy.policy import LEARNER_STATS_KEY, Policy
from ray.rllib.utils.typing import TrainerConfigDict
from ray.util.iter import LocalIterator

logger = logging.getLogger(__name__)

# yapf: disable
# __sphinx_doc_begin__
DEFAULT_CONFIG = with_common_config({
    # === Model ===
    # Number of atoms for representing the distribution of return. When
    # this is greater than 1, distributional Q-learning is used.
    # the discrete supports are bounded by v_min and v_max
    "num_atoms": 1,
    "v_min": -10.0,
    "v_max": 10.0,
    # Whether to use noisy network
    "noisy": False,
    # control the initial value of noisy nets
    "sigma0": 0.5,
    # Whether to use dueling dqn
    "dueling": True,
    # Dense-layer setup for each the advantage branch and the value branch
    # in a dueling architecture.
    "hiddens": [256],
    # Whether to use double dqn
    "double_q": True,
    # N-step Q learning
    "n_step": 1,

    # === Exploration Settings ===
    "exploration_config": {
        # The Exploration class to use.
        "type": "EpsilonGreedy",
        # Config for the Exploration class' constructor:
        "initial_epsilon": 1.0,
        "final_epsilon": 0.02,
        "epsilon_timesteps": 10000,  # Timesteps over which to anneal epsilon.

        # For soft_q, use:
        # "exploration_config" = {
        #   "type": "SoftQ"
        #   "temperature": [float, e.g. 1.0]
        # }
    },
    # Switch to greedy actions in evaluation workers.
    "evaluation_config": {
        "explore": False,
    },

    # Minimum env steps to optimize for per train call. This value does
    # not affect learning, only the length of iterations.
    "timesteps_per_iteration": 1000,
    # Update the target network every `target_network_update_freq` steps.
    "target_network_update_freq": 500,
    # === Replay buffer ===
    # Size of the replay buffer. Note that if async_updates is set, then
    # each worker will have a replay buffer of this size.
    "buffer_size": 50000,
    # If True prioritized replay buffer will be used.
    "prioritized_replay": True,
    # Alpha parameter for prioritized replay buffer.
    "prioritized_replay_alpha": 0.6,
    # Beta parameter for sampling from prioritized replay buffer.
    "prioritized_replay_beta": 0.4,
    # Final value of beta (by default, we use constant beta=0.4).
    "final_prioritized_replay_beta": 0.4,
    # Time steps over which the beta parameter is annealed.
    "prioritized_replay_beta_annealing_timesteps": 20000,
    # Epsilon to add to the TD errors when updating priorities.
    "prioritized_replay_eps": 1e-6,
    # Whether to LZ4 compress observations
    "compress_observations": False,
    # Callback to run before learning on a multi-agent batch of experiences.
    "before_learn_on_batch": None,
    # If set, this will fix the ratio of replayed from a buffer and learned on
    # timesteps to sampled from an environment and stored in the replay buffer
    # timesteps. Otherwise, the replay will proceed at the native ratio
    # determined by (train_batch_size / rollout_fragment_length).
    "training_intensity": None,

    # === Optimization ===
    # Learning rate for adam optimizer
    "lr": 5e-4,
    # Learning rate schedule
    "lr_schedule": None,
    # Adam epsilon hyper parameter
    "adam_epsilon": 1e-8,
    # If not None, clip gradients during optimization at this value
    "grad_clip": 40,
    # How many steps of the model to sample before learning starts.
    "learning_starts": 1000,
    # Update the replay buffer with this many samples at once. Note that
    # this setting applies per-worker if num_workers > 1.
    "rollout_fragment_length": 4,
    # Size of a batch sampled from replay buffer for training. Note that
    # if async_updates is set, then each worker returns gradients for a
    # batch of this size.
    "train_batch_size": 32,

    # === Parallelism ===
    # Number of workers for collecting samples with. This only makes sense
    # to increase if your environment is particularly slow to sample, or if
    # you"re using the Async or Ape-X optimizers.
    "num_workers": 0,
    # Whether to compute priorities on workers.
    "worker_side_prioritization": False,
    # Prevent iterations from going lower than this time span
    "min_iter_time_s": 1,
})
# __sphinx_doc_end__
# yapf: enable


def validate_config(config: TrainerConfigDict) -> None:
    """Checks and updates the config based on settings.

    Rewrites rollout_fragment_length to take into account n_step truncation.
    """
    if config["exploration_config"]["type"] == "ParameterNoise":
        if config["batch_mode"] != "complete_episodes":
            logger.warning(
                "ParameterNoise Exploration requires `batch_mode` to be "
                "'complete_episodes'. Setting batch_mode=complete_episodes.")
            config["batch_mode"] = "complete_episodes"
        if config.get("noisy", False):
            raise ValueError(
                "ParameterNoise Exploration and `noisy` network cannot be "
                "used at the same time!")

    # Update effective batch size to include n-step
    adjusted_batch_size = max(config["rollout_fragment_length"],
                              config.get("n_step", 1))
    config["rollout_fragment_length"] = adjusted_batch_size

    if config.get("prioritized_replay"):
        if config["multiagent"]["replay_mode"] == "lockstep":
            raise ValueError("Prioritized replay is not supported when "
                             "replay_mode=lockstep.")
        elif config["replay_sequence_length"] > 1:
            raise ValueError("Prioritized replay is not supported when "
                             "replay_sequence_length > 1.")


def execution_plan(workers: WorkerSet,
                   config: TrainerConfigDict) -> LocalIterator[dict]:
    """Execution plan of the DQN algorithm. Defines the distributed dataflow.

    Args:
        workers (WorkerSet): The WorkerSet for training the Polic(y/ies)
            of the Trainer.
        config (TrainerConfigDict): The trainer's configuration dict.

    Returns:
        LocalIterator[dict]: A local iterator over training metrics.
    """
    if config.get("prioritized_replay"):
        prio_args = {
            "prioritized_replay_alpha": config["prioritized_replay_alpha"],
            "prioritized_replay_beta": config["prioritized_replay_beta"],
            "prioritized_replay_eps": config["prioritized_replay_eps"],
        }
    else:
        prio_args = {}

    local_replay_buffer = LocalReplayBuffer(
        num_shards=1,
        learning_starts=config["learning_starts"],
        buffer_size=config["buffer_size"],
        replay_batch_size=config["train_batch_size"],
        replay_mode=config["multiagent"]["replay_mode"],
        replay_sequence_length=config["replay_sequence_length"],
        **prio_args)

    rollouts = ParallelRollouts(workers, mode="bulk_sync")

    # We execute the following steps concurrently:
    # (1) Generate rollouts and store them in our local replay buffer. Calling
    # next() on store_op drives this.
    store_op = rollouts.for_each(
        StoreToReplayBuffer(local_buffer=local_replay_buffer))

    def update_prio(item):
        samples, info_dict = item
        if config.get("prioritized_replay"):
            prio_dict = {}
            for policy_id, info in info_dict.items():
                # TODO(sven): This is currently structured differently for
                #  torch/tf. Clean up these results/info dicts across
                #  policies (note: fixing this in torch_policy.py will
                #  break e.g. DDPPO!).
                td_error = info.get("td_error",
                                    info[LEARNER_STATS_KEY].get("td_error"))
                prio_dict[policy_id] = (samples.policy_batches[policy_id]
                                        .data.get("batch_indexes"), td_error)
            local_replay_buffer.update_priorities(prio_dict)
        return info_dict

    # (2) Read and train on experiences from the replay buffer. Every batch
    # returned from the LocalReplay() iterator is passed to TrainOneStep to
    # take a SGD step, and then we decide whether to update the target network.
    post_fn = config.get("before_learn_on_batch") or (lambda b, *a: b)
    replay_op = Replay(local_buffer=local_replay_buffer) \
        .for_each(lambda x: post_fn(x, workers, config)) \
        .for_each(TrainOneStep(workers)) \
        .for_each(update_prio) \
        .for_each(UpdateTargetNetwork(
            workers, config["target_network_update_freq"]))

    # Alternate deterministically between (1) and (2). Only return the output
    # of (2) since training metrics are not available until (2) runs.
    train_op = Concurrently(
        [store_op, replay_op],
        mode="round_robin",
        output_indexes=[1],
        round_robin_weights=calculate_rr_weights(config))

    return StandardMetricsReporting(train_op, workers, config)


def calculate_rr_weights(config: TrainerConfigDict) -> List[float]:
    """Calculate the round robin weights for the rollout and train steps"""
    if not config["training_intensity"]:
        return [1, 1]
    # e.g., 32 / 4 -> native ratio of 8.0
    native_ratio = (
        config["train_batch_size"] / config["rollout_fragment_length"])
    # Training intensity is specified in terms of
    # (steps_replayed / steps_sampled), so adjust for the native ratio.
    weights = [1, config["training_intensity"] / native_ratio]
    return weights


def get_policy_class(config: TrainerConfigDict) -> Optional[Type[Policy]]:
    """Policy class picker function. Class is chosen based on DL-framework.

    Args:
        config (TrainerConfigDict): The trainer's configuration dict.

    Returns:
        Optional[Type[Policy]]: The Policy class to use with DQNTrainer.
            If None, use `default_policy` provided in build_trainer().
    """
    if config["framework"] == "torch":
        return DQNTorchPolicy


# Build a generic off-policy trainer. Other trainers (such as DDPGTrainer)
# may build on top of it.
GenericOffPolicyTrainer = build_trainer(
    name="GenericOffPolicyAlgorithm",
    default_policy=None,
    get_policy_class=get_policy_class,
    default_config=DEFAULT_CONFIG,
    validate_config=validate_config,
    execution_plan=execution_plan)

# Build a DQN trainer, which uses the framework specific Policy
# determined in `get_policy_class()` above.
DQNTrainer = GenericOffPolicyTrainer.with_updates(
    name="DQN", default_policy=DQNTFPolicy, default_config=DEFAULT_CONFIG)