Source code for ray.rllib.utils.exploration.ornstein_uhlenbeck_noise

import numpy as np
from typing import Optional, Union

from ray.rllib.utils.annotations import PublicAPI
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.utils.annotations import override
from ray.rllib.utils.exploration.gaussian_noise import GaussianNoise
from ray.rllib.utils.framework import (
from ray.rllib.utils.numpy import convert_to_numpy
from ray.rllib.utils.schedules import Schedule
from ray.rllib.utils.tf_utils import zero_logps_from_actions

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

[docs]@PublicAPI class OrnsteinUhlenbeckNoise(GaussianNoise): """An exploration that adds Ornstein-Uhlenbeck noise to continuous actions. If explore=True, returns sampled actions plus a noise term X, which changes according to this formula: Xt+1 = -theta*Xt + sigma*N[0,stddev], where theta, sigma and stddev are constants. Also, some completely random period is possible at the beginning. If explore=False, returns the deterministic action. """
[docs] def __init__( self, action_space, *, framework: str, ou_theta: float = 0.15, ou_sigma: float = 0.2, ou_base_scale: float = 0.1, random_timesteps: int = 1000, initial_scale: float = 1.0, final_scale: float = 0.02, scale_timesteps: int = 10000, scale_schedule: Optional[Schedule] = None, **kwargs ): """Initializes an Ornstein-Uhlenbeck Exploration object. Args: action_space: The gym action space used by the environment. ou_theta: The theta parameter of the Ornstein-Uhlenbeck process. ou_sigma: The sigma parameter of the Ornstein-Uhlenbeck process. ou_base_scale: A fixed scaling factor, by which all OU- noise is multiplied. NOTE: This is on top of the parent GaussianNoise's scaling. 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). 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). framework: One of None, "tf", "torch". """ # The current OU-state value (gets updated each time, an eploration # action is computed). self.ou_state = get_variable( np.array(action_space.low.size * [0.0], dtype=np.float32), framework=framework, tf_name="ou_state", torch_tensor=True, device=None, ) super().__init__( action_space, framework=framework, random_timesteps=random_timesteps, initial_scale=initial_scale, final_scale=final_scale, scale_timesteps=scale_timesteps, scale_schedule=scale_schedule, stddev=1.0, # Force `self.stddev` to 1.0. **kwargs ) self.ou_theta = ou_theta self.ou_sigma = ou_sigma self.ou_base_scale = ou_base_scale # Now that we know the device, move ou_state there, in case of PyTorch. if self.framework == "torch" and self.device is not None: self.ou_state =
@override(GaussianNoise) def _get_tf_exploration_action_op( self, action_dist: ActionDistribution, explore: Union[bool, TensorType], timestep: Union[int, TensorType], ): ts = timestep if timestep is not None else self.last_timestep scale = self.scale_schedule(ts) # The deterministic actions (if explore=False). deterministic_actions = action_dist.deterministic_sample() # Apply base-scaled and time-annealed scaled OU-noise to # deterministic actions. gaussian_sample = tf.random.normal( shape=[self.action_space.low.size], stddev=self.stddev ) ou_new = self.ou_theta * -self.ou_state + self.ou_sigma * gaussian_sample if self.framework == "tf2": self.ou_state.assign_add(ou_new) ou_state_new = self.ou_state else: ou_state_new = tf1.assign_add(self.ou_state, ou_new) high_m_low = self.action_space.high - self.action_space.low high_m_low = tf.where( tf.math.is_inf(high_m_low), tf.ones_like(high_m_low), high_m_low ) noise = scale * self.ou_base_scale * ou_state_new * high_m_low stochastic_actions = tf.clip_by_value( deterministic_actions + noise, self.action_space.low * tf.ones_like(deterministic_actions), self.action_space.high * tf.ones_like(deterministic_actions), ) # Stochastic actions could either be: random OR action + noise. random_actions, _ = self.random_exploration.get_tf_exploration_action_op( action_dist, explore ) exploration_actions = tf.cond( pred=tf.convert_to_tensor(ts < self.random_timesteps), true_fn=lambda: random_actions, false_fn=lambda: stochastic_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: exploration_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)) 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, ou_state_new]): action = tf.identity(action) logp = tf.identity(logp) return action, logp @override(GaussianNoise) 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 ) # Apply base-scaled and time-annealed scaled OU-noise to # deterministic actions. else: det_actions = action_dist.deterministic_sample() scale = self.scale_schedule(self.last_timestep) gaussian_sample = scale * torch.normal( mean=torch.zeros(self.ou_state.size()), std=1.0 ).to(self.device) ou_new = ( self.ou_theta * -self.ou_state + self.ou_sigma * gaussian_sample ) self.ou_state += ou_new high_m_low = torch.from_numpy( self.action_space.high - self.action_space.low ).to(self.device) high_m_low = torch.where( torch.isinf(high_m_low), torch.ones_like(high_m_low).to(self.device), high_m_low, ) noise = scale * self.ou_base_scale * self.ou_state * high_m_low action = torch.min( torch.max( det_actions + noise, 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
[docs] @override(GaussianNoise) 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 dict( self._tf_state_op, **{ "ou_state": self.ou_state, } ) ) state = super().get_state() return dict( state, **{ "ou_state": convert_to_numpy(self.ou_state) if self.framework != "tf" else self.ou_state, } )
[docs] @override(GaussianNoise) def set_state(self, state: dict, sess: Optional["tf.Session"] = None) -> None: if self.framework == "tf": self.ou_state.load(state["ou_state"], session=sess) elif isinstance(self.ou_state, np.ndarray): self.ou_state = state["ou_state"] elif torch and torch.is_tensor(self.ou_state): self.ou_state = torch.from_numpy(state["ou_state"]) else: self.ou_state.assign(state["ou_state"]) super().set_state(state, sess=sess)