schola.core.spaces.discrete.MultiDiscreteSpace

class schola.core.spaces.discrete.MultiDiscreteSpace(nvec) : Bases: MultiDiscrete, UnrealSpace

A Space representing a vector of discrete values.

Parameters: : nvec (List*[int]*) – The number of discrete values in each dimension of the space.

nvec : The number of discrete values in each dimension of the space.

Type: : List[int]

See also

gymnasium.spaces.MultiDiscrete : The gym space object that this class is analogous to.

proto_spaces.MultiDiscreteSpace : The protobuf representation of this space.

Methods


`__init__`(nvec)	Constructor of `MultiDiscrete` space.
`contains`(x)	Return boolean specifying if x is a valid member of this space.
`fill_proto`(msg, values)	Convert a python representation of point in this space to a protobuf message.
`from_jsonable`(sample_n)	Convert a JSONable data type to a batch of samples from this space.
`from_proto`(message)	Create a Space Object from a protobuf representation.
`is_empty_definition`(message)	Returns True iff this space has magnitude 0.
`merge`(*spaces)	Merge multiple DiscreteSpaces into a single space.
`process_data`(msg)	Convert a protobuf message corresponding to a point in this space to a pythonic representation.
`sample`([mask])	Generates a single random sample this space.
`seed`([seed])	Seed the PRNG of this space and possibly the PRNGs of subspaces.
`to_jsonable`(sample_n)	Convert a batch of samples from this space to a JSONable data type.
`to_normalized`()	Returns a normalized version of the space.

Attributes


`is_np_flattenable`	Checks whether this space can be flattened to a `spaces.Box`.
`np_random`	Lazily seed the PRNG since this is expensive and only needed if sampling from this space.
`shape`	Has stricter type than `gym.Space` - never None.

__init__(nvec) : Constructor of MultiDiscrete space.

The argument nvec will determine the number of values each categorical variable can take. If start is provided, it will define the minimal values corresponding to each categorical variable.

Parameters: : - nvec (List[int]) – vector of counts of each categorical variable. This will usually be a list of integers. However, you may also pass a more complicated numpy array if you’d like the space to have several axes.

dtype – This should be some kind of integer type.
seed – Optionally, you can use this argument to seed the RNG that is used to sample from the space.
start – Optionally, the starting value the element of each class will take (defaults to 0).

fill_proto(msg, values) : Convert a python representation of point in this space to a protobuf message. Mutates msg with the result.

Parameters: : - msg (proto_points.FundamentalPoint) – The protobuf message to fill.

value (Any) – The pythonic representation of the point.
values (ndarray)

Return type: : None

classmethod from_proto(message) : Create a Space Object from a protobuf representation.

Parameters: : message (proto_space) – The protobuf message to convert.

Returns: : The Space subclass created from the protobuf message

Return type: : UnrealSpace

classmethod is_empty_definition(message) : Returns True iff this space has magnitude 0.

Parameters: : message (proto_space) – The protobuf message to check for emptiness.

Returns: : True iff the space is empty.

Return type: : bool

classmethod merge(*spaces) : Merge multiple DiscreteSpaces into a single space.

Parameters: : *spaces (List*[Union[DiscreteSpace,* MultiDiscreteSpace]**]) – The spaces to merge.

Returns: : The merged space.

Return type: : MultiDiscreteSpace starlight-sidebar-topics.ts Raises: : TypeError – If any of the spaces are not Discrete or MultiDiscrete.