Source code for ray.rllib.evaluation.interface

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

from ray.rllib.utils.annotations import DeveloperAPI


[docs]@DeveloperAPI class EvaluatorInterface(object): """This is the interface between policy optimizers and policy evaluation. See also: RolloutWorker """
[docs] @DeveloperAPI def sample(self): """Returns a batch of experience sampled from this evaluator. This method must be implemented by subclasses. Returns: SampleBatch|MultiAgentBatch: A columnar batch of experiences (e.g., tensors), or a multi-agent batch. Examples: >>> print(ev.sample()) SampleBatch({"obs": [1, 2, 3], "action": [0, 1, 0], ...}) """ raise NotImplementedError
[docs] @DeveloperAPI def learn_on_batch(self, samples): """Update policies based on the given batch. This is the equivalent to apply_gradients(compute_gradients(samples)), but can be optimized to avoid pulling gradients into CPU memory. Either this or the combination of compute/apply grads must be implemented by subclasses. Returns: info: dictionary of extra metadata from compute_gradients(). Examples: >>> batch = ev.sample() >>> ev.learn_on_batch(samples) """ grads, info = self.compute_gradients(samples) self.apply_gradients(grads) return info
[docs] @DeveloperAPI def compute_gradients(self, samples): """Returns a gradient computed w.r.t the specified samples. Either this or learn_on_batch() must be implemented by subclasses. Returns: (grads, info): A list of gradients that can be applied on a compatible evaluator. In the multi-agent case, returns a dict of gradients keyed by policy ids. An info dictionary of extra metadata is also returned. Examples: >>> batch = ev.sample() >>> grads, info = ev2.compute_gradients(samples) """ raise NotImplementedError
[docs] @DeveloperAPI def apply_gradients(self, grads): """Applies the given gradients to this evaluator's weights. Either this or learn_on_batch() must be implemented by subclasses. Examples: >>> samples = ev1.sample() >>> grads, info = ev2.compute_gradients(samples) >>> ev1.apply_gradients(grads) """ raise NotImplementedError
[docs] @DeveloperAPI def get_weights(self): """Returns the model weights of this Evaluator. This method must be implemented by subclasses. Returns: object: weights that can be set on a compatible evaluator. info: dictionary of extra metadata. Examples: >>> weights = ev1.get_weights() """ raise NotImplementedError
[docs] @DeveloperAPI def set_weights(self, weights): """Sets the model weights of this Evaluator. This method must be implemented by subclasses. Examples: >>> weights = ev1.get_weights() >>> ev2.set_weights(weights) """ raise NotImplementedError
[docs] @DeveloperAPI def get_host(self): """Returns the hostname of the process running this evaluator.""" return os.uname()[1]
[docs] @DeveloperAPI def apply(self, func, *args): """Apply the given function to this evaluator instance.""" return func(self, *args)