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- from gymnasium.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, PublicAPI
- 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()
- @PublicAPI
- 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 == "tf2":
- 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"])
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