jaclearn.rl.envs.gym_adapter#

Classes

DiscreteToMultiDiscrete

Adapter that adapts the MultiDiscrete action space to a Discrete action space of any size The converted action can be retrieved by calling the adapter with the discrete action discrete_to_multi_discrete = DiscreteToMultiDiscrete(multi_discrete) discrete_action = discrete_to_multi_discrete.sample() multi_discrete_action = discrete_to_multi_discrete(discrete_action)

Class DiscreteToMultiDiscrete

class DiscreteToMultiDiscrete[source]#

Bases: Discrete

Adapter that adapts the MultiDiscrete action space to a Discrete action space of any size The converted action can be retrieved by calling the adapter with the discrete action discrete_to_multi_discrete = DiscreteToMultiDiscrete(multi_discrete) discrete_action = discrete_to_multi_discrete.sample() multi_discrete_action = discrete_to_multi_discrete(discrete_action)

It can be initialized using 3 configurations: Configuration 1) - DiscreteToMultiDiscrete(multi_discrete) [2nd param is empty] Would adapt to a Discrete action space of size (1 + nb of discrete in MultiDiscrete) where

  • 0 returns NOOP [ 0, 0, 0, …]

  • 1 returns max for the first discrete space [max, 0, 0, …]

  • 2 returns max for the second discrete space [ 0, max, 0, …]

  • etc.

Configuration 2) - DiscreteToMultiDiscrete(multi_discrete, list_of_discrete) [2nd param is a list] Would adapt to a Discrete action space of size (1 + nb of items in list_of_discrete) e.g. if list_of_discrete = [0, 2]

  • 0 returns NOOP [ 0, 0, 0, …]

  • 1 returns max for first discrete in list [max, 0, 0, …]

  • 2 returns max for second discrete in list [ 0, 0, max, …]

  • etc.

Configuration 3) - DiscreteToMultiDiscrete(multi_discrete, discrete_mapping) [2nd param is a dict] Would adapt to a Discrete action space of size (nb_keys in discrete_mapping) where discrete_mapping is a dictionnary in the format { discrete_key: multi_discrete_mapping } e.g. for the Nintendo Game Controller [ [0,4], [0,1], [0,1] ] a possible mapping might be;

> mapping = { > 0: [0, 0, 0], # NOOP > 1: [1, 0, 0], # Up > 2: [3, 0, 0], # Down > 3: [2, 0, 0], # Right > 4: [2, 1, 0], # Right + A > 5: [2, 0, 1], # Right + B > 6: [2, 1, 1], # Right + A + B > 7: [4, 0, 0], # Left > 8: [4, 1, 0], # Left + A > 9: [4, 0, 1], # Left + B > 10: [4, 1, 1], # Left + A + B > 11: [0, 1, 0], # A only > 12: [0, 0, 1], # B only, > 13: [0, 1, 1], # A + B > }

__call__(discrete_action)[source]#

Call self as a function.

__init__(multi_discrete, options=None)[source]#
__new__(**kwargs)#
contains(x)#

Return boolean specifying if x is a valid member of this space

Return type:

bool

from_jsonable(sample_n)#

Convert a JSONable data type to a batch of samples from this space.

Parameters:

sample_n (list)

Return type:

list[T_cov]

sample()#

Randomly sample an element of this space. Can be uniform or non-uniform sampling based on boundedness of space.

Return type:

int

seed(seed=None)#

Seed the PRNG of this space.

Parameters:

seed (int | None)

Return type:

list

to_jsonable(sample_n)#

Convert a batch of samples from this space to a JSONable data type.

Parameters:

sample_n (Sequence[T_cov])

Return type:

list

property np_random: RandomNumberGenerator#

Lazily seed the rng since this is expensive and only needed if sampling from this space.

property shape: tuple[int, ...] | None#

Return the shape of the space as an immutable property

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