Source code for akro.dict

"""Cartesian product of multiple named Spaces (also known as a dict of Spaces).

This Space produces samples which are dicts, where the values of those dicts
are drawn from the values of this Space.
from collections import OrderedDict

from import Space

[docs]class Dict(Space): """ A dictionary of simpler spaces, e.g. Discrete, Box. Example usage: self.observation_space = spaces.Dict({"position": spaces.Discrete(2), "velocity": spaces.Discrete(3)}) """ def __init__(self, spaces): """ Convert and store the incoming spaces into an OrderedDict. Note: classes inheriting from akro.Dict need to convert each space in spaces to a akro.<class>.space. """ if isinstance(spaces, dict): spaces = OrderedDict(sorted(list(spaces.items()))) if isinstance(spaces, list): spaces = OrderedDict(spaces) self.spaces = spaces
[docs] def contains(self, x): """ Check if x is contained within self.spaces. Returns: Boolean """ if isinstance(x, dict): return all(item in self.spaces.items() for item in x.items()) else: return False
[docs] def to_jsonable(self, sample_n): """ Serialize as a dict-representation of vectors. Returns: JSON (dict) """ return { key: space.to_jsonable([sample[key] for sample in sample_n]) for key, space in self.spaces.items() }
[docs] def from_jsonable(self, sample_n): """ Convert information from a JSON format into a list. Returns: ret (list) """ dict_of_list = {} for key, space in self.spaces.items(): dict_of_list[key] = space.from_jsonable(sample_n[key]) ret = [] for i, _ in enumerate(dict_of_list[key]): entry = {} for key, value in dict_of_list.items(): entry[key] = value[i] ret.append(entry) return ret
@property def flat_dim(self): """ Return a flat dimension of the dict space. Returns: flat_dim (int) """ raise NotImplementedError
[docs] def flatten(self, x): """ Return a flattened observation x. Returns: x (flattened) """ raise NotImplementedError
[docs] def unflatten(self, x): """ Return an unflattened observation x. Returns: x (unflattened) """ raise NotImplementedError
[docs] def flatten_n(self, xs): """ Return flattened observations xs. Returns: xs (flattened) """ raise NotImplementedError
[docs] def unflatten_n(self, xs): """ Return unflattened observations xs. Returns: xs (unflattened) """ raise NotImplementedError
[docs] def sample(self): """ Return a sample from each space in spaces. Returns: OrderedDict """ raise NotImplementedError
[docs] def new_tensor_variable(self, name, extra_dims): """ Create a tensor variable given the name and extra dimensions. :param name: name of the variable :param extra_dims: extra dimensions in the front :return: the created tensor variable """ raise NotImplementedError