ray/rllib/utils/exploration/gaussian_noise.py

from gym.spaces import Space
import numpy as np
from typing import Union, Optional

from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.utils.annotations import override
from ray.rllib.utils.exploration.exploration import Exploration
from ray.rllib.utils.exploration.random import Random
from ray.rllib.utils.framework import try_import_tf, try_import_torch, \
    get_variable, TensorType
from ray.rllib.utils.numpy import convert_to_numpy
from ray.rllib.utils.schedules import Schedule
from ray.rllib.utils.schedules.piecewise_schedule import PiecewiseSchedule
from ray.rllib.utils.tf_utils import zero_logps_from_actions

tf1, tf, tfv = try_import_tf()
torch, _ = try_import_torch()


class GaussianNoise(Exploration):
    """An exploration that adds white noise to continuous actions.

    If explore=True, returns actions plus scale (annealed over time) x
    Gaussian noise. Also, some completely random period is possible at the
    beginning.

    If explore=False, returns the deterministic action.
    """

    def __init__(self,
                 action_space: Space,
                 *,
                 framework: str,
                 model: ModelV2,
                 random_timesteps: int = 1000,
                 stddev: float = 0.1,
                 initial_scale: float = 1.0,
                 final_scale: float = 0.02,
                 scale_timesteps: int = 10000,
                 scale_schedule: Optional[Schedule] = None,
                 **kwargs):
        """Initializes a GaussianNoise instance.

        Args:
            random_timesteps: The number of timesteps for which to act
                completely randomly. Only after this number of timesteps, the
                `self.scale` annealing process will start (see below).
            stddev: The stddev (sigma) to use for the
                Gaussian noise to be added to the actions.
            initial_scale: The initial scaling weight to multiply
                the noise with.
            final_scale: The final scaling weight to multiply
                the noise with.
            scale_timesteps: The timesteps over which to linearly anneal
                the scaling factor (after(!) having used random actions for
                `random_timesteps` steps).
            scale_schedule: An optional Schedule object
                to use (instead of constructing one from the given parameters).
        """
        assert framework is not None
        super().__init__(
            action_space, model=model, framework=framework, **kwargs)

        # Create the Random exploration module (used for the first n
        # timesteps).
        self.random_timesteps = random_timesteps
        self.random_exploration = Random(
            action_space, model=self.model, framework=self.framework, **kwargs)

        self.stddev = stddev
        # The `scale` annealing schedule.
        self.scale_schedule = scale_schedule or PiecewiseSchedule(
            endpoints=[(random_timesteps, initial_scale),
                       (random_timesteps + scale_timesteps, final_scale)],
            outside_value=final_scale,
            framework=self.framework)

        # The current timestep value (tf-var or python int).
        self.last_timestep = get_variable(
            np.array(0, np.int64),
            framework=self.framework,
            tf_name="timestep",
            dtype=np.int64)

        # Build the tf-info-op.
        if self.framework == "tf":
            self._tf_state_op = self.get_state()

    @override(Exploration)
    def get_exploration_action(self,
                               *,
                               action_distribution: ActionDistribution,
                               timestep: Union[int, TensorType],
                               explore: bool = True):
        # Adds IID Gaussian noise for exploration, TD3-style.
        if self.framework == "torch":
            return self._get_torch_exploration_action(action_distribution,
                                                      explore, timestep)
        else:
            return self._get_tf_exploration_action_op(action_distribution,
                                                      explore, timestep)

    def _get_tf_exploration_action_op(self, action_dist: ActionDistribution,
                                      explore: bool,
                                      timestep: Union[int, TensorType]):
        ts = timestep if timestep is not None else self.last_timestep

        # The deterministic actions (if explore=False).
        deterministic_actions = action_dist.deterministic_sample()

        # Take a Gaussian sample with our stddev (mean=0.0) and scale it.
        gaussian_sample = self.scale_schedule(ts) * tf.random.normal(
            tf.shape(deterministic_actions), stddev=self.stddev)

        # Stochastic actions could either be: random OR action + noise.
        random_actions, _ = \
            self.random_exploration.get_tf_exploration_action_op(
                action_dist, explore)
        stochastic_actions = tf.cond(
            pred=tf.convert_to_tensor(ts < self.random_timesteps),
            true_fn=lambda: random_actions,
            false_fn=lambda: tf.clip_by_value(
                deterministic_actions + gaussian_sample,
                self.action_space.low * tf.ones_like(deterministic_actions),
                self.action_space.high * tf.ones_like(deterministic_actions))
        )

        # Chose by `explore` (main exploration switch).
        action = tf.cond(
            pred=tf.constant(explore, dtype=tf.bool)
            if isinstance(explore, bool) else explore,
            true_fn=lambda: stochastic_actions,
            false_fn=lambda: deterministic_actions)
        # Logp=always zero.
        logp = zero_logps_from_actions(deterministic_actions)

        # Increment `last_timestep` by 1 (or set to `timestep`).
        if self.framework in ["tf2", "tfe"]:
            if timestep is None:
                self.last_timestep.assign_add(1)
            else:
                self.last_timestep.assign(tf.cast(timestep, tf.int64))
            return action, logp
        else:
            assign_op = (tf1.assign_add(self.last_timestep, 1)
                         if timestep is None else tf1.assign(
                             self.last_timestep, timestep))
            with tf1.control_dependencies([assign_op]):
                return action, logp

    def _get_torch_exploration_action(self, action_dist: ActionDistribution,
                                      explore: bool,
                                      timestep: Union[int, TensorType]):
        # Set last timestep or (if not given) increase by one.
        self.last_timestep = timestep if timestep is not None else \
            self.last_timestep + 1

        # Apply exploration.
        if explore:
            # Random exploration phase.
            if self.last_timestep < self.random_timesteps:
                action, _ = \
                    self.random_exploration.get_torch_exploration_action(
                        action_dist, explore=True)
            # Take a Gaussian sample with our stddev (mean=0.0) and scale it.
            else:
                det_actions = action_dist.deterministic_sample()
                scale = self.scale_schedule(self.last_timestep)
                gaussian_sample = scale * torch.normal(
                    mean=torch.zeros(det_actions.size()), std=self.stddev).to(
                        self.device)
                action = torch.min(
                    torch.max(
                        det_actions + gaussian_sample,
                        torch.tensor(
                            self.action_space.low,
                            dtype=torch.float32,
                            device=self.device)),
                    torch.tensor(
                        self.action_space.high,
                        dtype=torch.float32,
                        device=self.device))
        # No exploration -> Return deterministic actions.
        else:
            action = action_dist.deterministic_sample()

        # Logp=always zero.
        logp = torch.zeros(
            (action.size()[0], ), dtype=torch.float32, device=self.device)

        return action, logp

    @override(Exploration)
    def get_state(self, sess: Optional["tf.Session"] = None):
        """Returns the current scale value.

        Returns:
            Union[float,tf.Tensor[float]]: The current scale value.
        """
        if sess:
            return sess.run(self._tf_state_op)
        scale = self.scale_schedule(self.last_timestep)
        return {
            "cur_scale": convert_to_numpy(scale)
            if self.framework != "tf" else scale,
            "last_timestep": convert_to_numpy(self.last_timestep)
            if self.framework != "tf" else self.last_timestep,
        }

    @override(Exploration)
    def set_state(self, state: dict,
                  sess: Optional["tf.Session"] = None) -> None:
        if self.framework == "tf":
            self.last_timestep.load(state["last_timestep"], session=sess)
        elif isinstance(self.last_timestep, int):
            self.last_timestep = state["last_timestep"]
        else:
            self.last_timestep.assign(state["last_timestep"])