Source code for ray.rllib.offline.mixed_input

from types import FunctionType
from typing import Dict

import numpy as np
from ray.rllib.offline.input_reader import InputReader
from ray.rllib.offline.io_context import IOContext
from ray.rllib.offline.json_reader import JsonReader
from ray.rllib.utils.annotations import override, DeveloperAPI
from ray.rllib.utils.typing import SampleBatchType
from ray.tune.registry import registry_get_input, registry_contains_input

[docs]@DeveloperAPI class MixedInput(InputReader): """Mixes input from a number of other input sources. Examples: >>> from ray.rllib.offline.io_context import IOContext >>> from ray.rllib.offline.mixed_input import MixedInput >>> ioctx = IOContext(...) # doctest: +SKIP >>> MixedInput({ # doctest: +SKIP ... "sampler": 0.4, # doctest: +SKIP ... "/tmp/experiences/*.json": 0.4, # doctest: +SKIP ... "s3://bucket/expert.json": 0.2, # doctest: +SKIP ... }, ioctx) # doctest: +SKIP """
[docs] @DeveloperAPI def __init__(self, dist: Dict[JsonReader, float], ioctx: IOContext): """Initialize a MixedInput. Args: dist: dict mapping JSONReader paths or "sampler" to probabilities. The probabilities must sum to 1.0. ioctx: current IO context object. """ if sum(dist.values()) != 1.0: raise ValueError("Values must sum to 1.0: {}".format(dist)) self.choices = [] self.p = [] for k, v in dist.items(): if k == "sampler": self.choices.append(ioctx.default_sampler_input()) elif isinstance(k, FunctionType): self.choices.append(k(ioctx)) elif isinstance(k, str) and registry_contains_input(k): input_creator = registry_get_input(k) self.choices.append(input_creator(ioctx)) else: self.choices.append(JsonReader(k, ioctx)) self.p.append(v)
[docs] @override(InputReader) def next(self) -> SampleBatchType: source = np.random.choice(self.choices, p=self.p) return