Source code for ray.rllib.offline.d4rl_reader

import logging
import gym

from ray.rllib.offline.input_reader import InputReader
from ray.rllib.offline.io_context import IOContext
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.annotations import override, PublicAPI
from ray.rllib.utils.typing import SampleBatchType
from typing import Dict

logger = logging.getLogger(__name__)


[docs]@PublicAPI class D4RLReader(InputReader): """Reader object that loads the dataset from the D4RL dataset."""
[docs] @PublicAPI def __init__(self, inputs: str, ioctx: IOContext = None): """Initializes a D4RLReader instance. Args: inputs: String corresponding to the D4RL environment name. ioctx: Current IO context object. """ import d4rl self.env = gym.make(inputs) self.dataset = _convert_to_batch(d4rl.qlearning_dataset(self.env)) assert self.dataset.count >= 1 self.counter = 0
[docs] @override(InputReader) def next(self) -> SampleBatchType: if self.counter >= self.dataset.count: self.counter = 0 self.counter += 1 return self.dataset.slice(start=self.counter, end=self.counter + 1)
def _convert_to_batch(dataset: Dict) -> SampleBatchType: # Converts D4RL dataset to SampleBatch d = {} d[SampleBatch.OBS] = dataset["observations"] d[SampleBatch.ACTIONS] = dataset["actions"] d[SampleBatch.NEXT_OBS] = dataset["next_observations"] d[SampleBatch.REWARDS] = dataset["rewards"] d[SampleBatch.DONES] = dataset["terminals"] return SampleBatch(d)