About this deal
seed – Optionally, you can use this argument to seed the RNGs of the spaces to ensure reproducible sampling.
Box ( low = np . array ([ - 1.0 , - 2.0 ]), high = np . array ([ 2.0 , 4.0 ]), dtype = np . float32 ) Box(2,) I.e., the space that is constructed will be the product of the intervals \([\text{low}[i], \text{high}[i]]\). dtype – The dtype of the elements of the space. If this is an integer type, the Box is essentially a discrete space. observation_space = MultiBinary ( 5 ) >>> observation_space . sample () array([0, 1, 0, 1, 0], dtype=int8) >>> observation_space = MultiBinary ([ 3 , 2 ]) >>> observation_space . sample () array([[0, 0], [0, 1], [1, 1]], dtype=int8) __init__ ( n : ndarray | Sequence [ int ] | int, seed : int | Generator | None = None ) #B5", "hello", ...} >>> Text ( 5 ) >>> # {"0", "42", "0123456789", ...} >>> import string >>> Text ( min_length = 1 , ... max_length = 10 , ... charset = string . digits ) __init__ ( max_length : int, *, min_length : int = 1, charset : Set [ str ] | str = frozenset({'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'}), seed : int | Generator | None = None ) # seed – Optionally, you can use this argument to seed the RNGs of the spaces that make up the Dict space. An `np.ndarray` of shape `space.shape` Sequence # class gym.spaces. Sequence ( space : Space, seed : int | Generator | None = None ) # A sample is drawn by independent, fair coin tosses (one toss per binary variable of the space). Parameters : mask – An optional mask for (optionally) the length of the sequence and (optionally) the values in the sequence.
ValueError – If no shape information is provided (shape is None, low is None and high is None) then a Mr Brenner’s partner, Kayleigh Dawkins is the operational manager at Mind and Muscle Fitness and tells of how Steve changed his diet when on his weight loss journey.
A NamedTuple representing a graph with attributes .nodes, .edges, and .edge_links. MultiBinary # class gym.spaces. MultiBinary ( n : ndarray | Sequence [ int ] | int, seed : int | Generator | None = None ) #
NotImplementedError – If the space is not defined in gym.spaces. gym.spaces.utils. unflatten ( space : Space [ T ], x : ndarray | Dict | tuple | GraphInstance ) → T # gym.spaces.utils. unflatten ( space : Box | MultiBinary, x : ndarray ) → ndarray gym.spaces.utils. unflatten ( space : Box | MultiBinary, x : ndarray ) → ndarray gym.spaces.utils. unflatten ( space : Discrete, x : ndarray ) → int gym.spaces.utils. unflatten ( space : MultiDiscrete, x : ndarray ) → ndarray gym.spaces.utils. unflatten ( space : Tuple, x : ndarray | tuple ) → tuple gym.spaces.utils. unflatten ( space : Dict, x : ndarray | Dict ) → dict gym.spaces.utils. unflatten ( space : Graph, x : GraphInstance ) → GraphInstance gym.spaces.utils. unflatten ( space : Text, x : ndarray ) → str gym.spaces.utils. unflatten ( space : Sequence, x : tuple ) → tupleseed – Optionally, you can use this argument to seed the RNG that is used to sample from the space. The flywheel technology used in Space Gym allows the user to perform Inertial exercises which is a type of resistance training first used by NASA astronauts because it doesn't require the lifting of weights against gravity, this is why we call it Space Gym. Get pro-level training without all the costs!
If you specify mask, it is expected to be a tuple of the form (length_mask, sample_mask) where length_mask This class represents a finite subset of integers, more specifically a set of the form \(\{ a, a+1, \dots, a+n-1 \}\).
Membership
Convert a batch of samples from this space to a JSONable data type. gym.spaces.Space. from_jsonable ( self, sample_n : list ) → List [ T_cov ] # The length is expected to be between the min_length and max_length otherwise a random integer between min_length and max_length is selected. low: ~typing.SupportsFloat | ~numpy.ndarray, high: ~typing.SupportsFloat | ~numpy.ndarray, shape: ~typing.Sequence[int] | None = None, dtype: ~typing.Type =