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A sampled string from the space Tuple # class gym.spaces. Tuple ( spaces : Iterable [ Space ], seed : int | Sequence [ int ] | Generator | None = None ) # He said: “I have had a lot of people follow my progress from when I was really overweight and have seen the transformation I have gone through and have joined me as clients. For my clients, being able to see that I have been there myself – we can relate to each other. But then I decided to completely change my life.” Steve Brenner from Sellindge, is hoping his weight loss journey will encourage others to join his new gym in Folkestone The team is hoping the gym will be a “safe space” for members. Picture: Mind and Muscle 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 ] # Each space implements the following functions: gym.spaces.Space. sample ( self, mask : Any | None = None ) → T_cov #

Convert a JSONable data type to a batch of samples from this space. Box # class gym.spaces. Box ( low: ~typing.SupportsFloat | ~numpy.ndarray, high: ~typing.SupportsFloat | ~numpy.ndarray, shape: ~typing.Sequence[int] | None = None, dtype: ~typing.Type = , seed: int | ~numpy.random._generator.Generator | None = None ) # Return boolean specifying if x is a valid member of this space. property Space. shape : Tuple [ int , ... ] | None # He also allowed himself one meal of choice off of the plan he had designed for himself, which was often a takeaway. But diets and nutrition are different from person to person, which of course as a nutritionist Steve now understands fully and helps people design a food plan that works for them.NotImplementedError – if the space is not defined in gym.spaces. gym.spaces.utils. flatten ( space : Space [ T ], x : T ) → ndarray | Dict | tuple | GraphInstance # gym.spaces.utils. flatten ( space : MultiBinary, x ) → ndarray gym.spaces.utils. flatten ( space : Box, x ) → ndarray gym.spaces.utils. flatten ( space : Discrete, x ) → ndarray gym.spaces.utils. flatten ( space : MultiDiscrete, x ) → ndarray gym.spaces.utils. flatten ( space : Tuple, x ) → tuple | ndarray gym.spaces.utils. flatten ( space : Dict, x ) → dict | ndarray gym.spaces.utils. flatten ( space : Graph, x ) → GraphInstance gym.spaces.utils. flatten ( space : Text, x : str ) → ndarray gym.spaces.utils. flatten ( space : Sequence, x ) → tuple The argument nvec will determine the number of values each categorical variable can take. Parameters : Seed the PRNG of this space and possibly the PRNGs of subspaces. gym.spaces.Space. to_jsonable ( self, sample_n : Sequence [ T_cov ] ) → list # A sampled value from the Box Dict # class gym.spaces. Dict ( spaces : Dict [ str , Space ] | Sequence [ Tuple [ str , Space ] ] | None = None, seed : dict | int | Generator | None = None, ** spaces_kwargs : Space ) # gym.spaces.utils. flatten_space ( space : Space ) → Dict | Sequence | Tuple | Graph # gym.spaces.utils. flatten_space ( space : Box ) → Box gym.spaces.utils. flatten_space ( space : Discrete | MultiBinary | MultiDiscrete ) → Box gym.spaces.utils. flatten_space ( space : Discrete | MultiBinary | MultiDiscrete ) → Box gym.spaces.utils. flatten_space ( space : Discrete | MultiBinary | MultiDiscrete ) → Box gym.spaces.utils. flatten_space ( space : Tuple ) → Box | Tuple gym.spaces.utils. flatten_space ( space : Dict ) → Box | Dict gym.spaces.utils. flatten_space ( space : Graph ) → Graph gym.spaces.utils. flatten_space ( space : Text ) → Box gym.spaces.utils. flatten_space ( space : Sequence ) → Sequence

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. implemented to deal with Dict actions. __init__ ( spaces : Dict [ str , Space ] | Sequence [ Tuple [ str , Space ] ] | None = None, seed : dict | int | Generator | None = None, ** spaces_kwargs : Space ) # mask – An optional mask for (optionally) the length of the sequence and (optionally) the values in the sequence.

Box ( low = np . array ([ - 1.0 , - 2.0 ]), high = np . array ([ 2.0 , 4.0 ]), dtype = np . float32 ) Box(2,) mask – An optional mask for multi-discrete, expects tuples with a np.ndarray mask in the position of each

This class represents a finite subset of integers, more specifically a set of the form \(\{ a, a+1, \dots, a+n-1 \}\). classes (e.g. Box, Discrete, etc…), and container classes (:class`Tuple` & Dict). Note that parametrized probability distributions (through the spaces_kwargs – If spaces is None, you need to pass the constituent spaces as keyword arguments, as described above.Sampled values from space MultiDiscrete # class gym.spaces. MultiDiscrete ( nvec: ~numpy.ndarray | list, dtype=, seed: int | ~numpy.random._generator.Generator | None = None ) # The awards, organised by Script Events and leading industry publication Workout, with support from headline sponsor ServiceSport, are now in their 12th year and recognise excellence and achievement in all corners of the industry.