city_of_gold.vec.sampler

Vectorized action samplers

Members

class city_of_gold.vec.sampler.vec_sampler_1

Bases: pybind11_object

Vectorized random agent for 1 environments

Parameters:

seed (unsigned integer or None) – (Optional) Set the random generator seed for the sampler. Unique seeds in the form seed+i are used to initialize the individual samplers, with i being the index of the sampler.

Returns:

The instantiated vector of action samplers

get_actions(self: city_of_gold.vec.sampler.vec_sampler_1) → numpy.ndarray[city_of_gold.ActionData]

Get a reference to the samplers internal vector of sampled actions.

The contents are overwritten every time sample() is called. :returns: The vector where sampled actions are placed :rtype: numpy.ndarray of ActionData

sample(self: city_of_gold.vec.sampler.vec_sampler_1, action_mask: numpy.ndarray[city_of_gold.ActionMask]) → None

Generate a uniform sample of the valid action space for each environment.

Update the contents of actions with a new sample masked using the input action mask. :param action_mask: mask specifying valid actions for each environment :type action_mask: numpy.ndarray of ActionMask :return: None

class city_of_gold.vec.sampler.vec_sampler_128

Bases: pybind11_object

Vectorized random agent for 128 environments

Parameters:

seed (unsigned integer or None) – (Optional) Set the random generator seed for the sampler. Unique seeds in the form seed+i are used to initialize the individual samplers, with i being the index of the sampler.

Returns:

The instantiated vector of action samplers

get_actions(self: city_of_gold.vec.sampler.vec_sampler_128) → numpy.ndarray[city_of_gold.ActionData]

Get a reference to the samplers internal vector of sampled actions.

The contents are overwritten every time sample() is called. :returns: The vector where sampled actions are placed :rtype: numpy.ndarray of ActionData

sample(self: city_of_gold.vec.sampler.vec_sampler_128, action_mask: numpy.ndarray[city_of_gold.ActionMask]) → None

Generate a uniform sample of the valid action space for each environment.

Update the contents of actions with a new sample masked using the input action mask. :param action_mask: mask specifying valid actions for each environment :type action_mask: numpy.ndarray of ActionMask :return: None

class city_of_gold.vec.sampler.vec_sampler_16

Bases: pybind11_object

Vectorized random agent for 16 environments

Parameters:

seed (unsigned integer or None) – (Optional) Set the random generator seed for the sampler. Unique seeds in the form seed+i are used to initialize the individual samplers, with i being the index of the sampler.

Returns:

The instantiated vector of action samplers

get_actions(self: city_of_gold.vec.sampler.vec_sampler_16) → numpy.ndarray[city_of_gold.ActionData]

Get a reference to the samplers internal vector of sampled actions.

The contents are overwritten every time sample() is called. :returns: The vector where sampled actions are placed :rtype: numpy.ndarray of ActionData

sample(self: city_of_gold.vec.sampler.vec_sampler_16, action_mask: numpy.ndarray[city_of_gold.ActionMask]) → None

Generate a uniform sample of the valid action space for each environment.

Update the contents of actions with a new sample masked using the input action mask. :param action_mask: mask specifying valid actions for each environment :type action_mask: numpy.ndarray of ActionMask :return: None

class city_of_gold.vec.sampler.vec_sampler_2

Bases: pybind11_object

Vectorized random agent for 2 environments

Parameters:

seed (unsigned integer or None) – (Optional) Set the random generator seed for the sampler. Unique seeds in the form seed+i are used to initialize the individual samplers, with i being the index of the sampler.

Returns:

The instantiated vector of action samplers

get_actions(self: city_of_gold.vec.sampler.vec_sampler_2) → numpy.ndarray[city_of_gold.ActionData]

Get a reference to the samplers internal vector of sampled actions.

The contents are overwritten every time sample() is called. :returns: The vector where sampled actions are placed :rtype: numpy.ndarray of ActionData

sample(self: city_of_gold.vec.sampler.vec_sampler_2, action_mask: numpy.ndarray[city_of_gold.ActionMask]) → None

Generate a uniform sample of the valid action space for each environment.

Update the contents of actions with a new sample masked using the input action mask. :param action_mask: mask specifying valid actions for each environment :type action_mask: numpy.ndarray of ActionMask :return: None

class city_of_gold.vec.sampler.vec_sampler_256

Bases: pybind11_object

Vectorized random agent for 256 environments

Parameters:

seed (unsigned integer or None) – (Optional) Set the random generator seed for the sampler. Unique seeds in the form seed+i are used to initialize the individual samplers, with i being the index of the sampler.

Returns:

The instantiated vector of action samplers

get_actions(self: city_of_gold.vec.sampler.vec_sampler_256) → numpy.ndarray[city_of_gold.ActionData]

Get a reference to the samplers internal vector of sampled actions.

The contents are overwritten every time sample() is called. :returns: The vector where sampled actions are placed :rtype: numpy.ndarray of ActionData

sample(self: city_of_gold.vec.sampler.vec_sampler_256, action_mask: numpy.ndarray[city_of_gold.ActionMask]) → None

Generate a uniform sample of the valid action space for each environment.

Update the contents of actions with a new sample masked using the input action mask. :param action_mask: mask specifying valid actions for each environment :type action_mask: numpy.ndarray of ActionMask :return: None

class city_of_gold.vec.sampler.vec_sampler_3

Bases: pybind11_object

Vectorized random agent for 3 environments

Parameters:

seed (unsigned integer or None) – (Optional) Set the random generator seed for the sampler. Unique seeds in the form seed+i are used to initialize the individual samplers, with i being the index of the sampler.

Returns:

The instantiated vector of action samplers

get_actions(self: city_of_gold.vec.sampler.vec_sampler_3) → numpy.ndarray[city_of_gold.ActionData]

Get a reference to the samplers internal vector of sampled actions.

The contents are overwritten every time sample() is called. :returns: The vector where sampled actions are placed :rtype: numpy.ndarray of ActionData

sample(self: city_of_gold.vec.sampler.vec_sampler_3, action_mask: numpy.ndarray[city_of_gold.ActionMask]) → None

Generate a uniform sample of the valid action space for each environment.

Update the contents of actions with a new sample masked using the input action mask. :param action_mask: mask specifying valid actions for each environment :type action_mask: numpy.ndarray of ActionMask :return: None

class city_of_gold.vec.sampler.vec_sampler_32

Bases: pybind11_object

Vectorized random agent for 32 environments

Parameters:

seed (unsigned integer or None) – (Optional) Set the random generator seed for the sampler. Unique seeds in the form seed+i are used to initialize the individual samplers, with i being the index of the sampler.

Returns:

The instantiated vector of action samplers

get_actions(self: city_of_gold.vec.sampler.vec_sampler_32) → numpy.ndarray[city_of_gold.ActionData]

Get a reference to the samplers internal vector of sampled actions.

The contents are overwritten every time sample() is called. :returns: The vector where sampled actions are placed :rtype: numpy.ndarray of ActionData

sample(self: city_of_gold.vec.sampler.vec_sampler_32, action_mask: numpy.ndarray[city_of_gold.ActionMask]) → None

Generate a uniform sample of the valid action space for each environment.

Update the contents of actions with a new sample masked using the input action mask. :param action_mask: mask specifying valid actions for each environment :type action_mask: numpy.ndarray of ActionMask :return: None

class city_of_gold.vec.sampler.vec_sampler_4

Bases: pybind11_object

Vectorized random agent for 4 environments

Parameters:

seed (unsigned integer or None) – (Optional) Set the random generator seed for the sampler. Unique seeds in the form seed+i are used to initialize the individual samplers, with i being the index of the sampler.

Returns:

The instantiated vector of action samplers

get_actions(self: city_of_gold.vec.sampler.vec_sampler_4) → numpy.ndarray[city_of_gold.ActionData]

Get a reference to the samplers internal vector of sampled actions.

The contents are overwritten every time sample() is called. :returns: The vector where sampled actions are placed :rtype: numpy.ndarray of ActionData

sample(self: city_of_gold.vec.sampler.vec_sampler_4, action_mask: numpy.ndarray[city_of_gold.ActionMask]) → None

Generate a uniform sample of the valid action space for each environment.

Update the contents of actions with a new sample masked using the input action mask. :param action_mask: mask specifying valid actions for each environment :type action_mask: numpy.ndarray of ActionMask :return: None

class city_of_gold.vec.sampler.vec_sampler_5

Bases: pybind11_object

Vectorized random agent for 5 environments

Parameters:

seed (unsigned integer or None) – (Optional) Set the random generator seed for the sampler. Unique seeds in the form seed+i are used to initialize the individual samplers, with i being the index of the sampler.

Returns:

The instantiated vector of action samplers

get_actions(self: city_of_gold.vec.sampler.vec_sampler_5) → numpy.ndarray[city_of_gold.ActionData]

Get a reference to the samplers internal vector of sampled actions.

The contents are overwritten every time sample() is called. :returns: The vector where sampled actions are placed :rtype: numpy.ndarray of ActionData

sample(self: city_of_gold.vec.sampler.vec_sampler_5, action_mask: numpy.ndarray[city_of_gold.ActionMask]) → None

Generate a uniform sample of the valid action space for each environment.

Update the contents of actions with a new sample masked using the input action mask. :param action_mask: mask specifying valid actions for each environment :type action_mask: numpy.ndarray of ActionMask :return: None

class city_of_gold.vec.sampler.vec_sampler_6

Bases: pybind11_object

Vectorized random agent for 6 environments

Parameters:

seed (unsigned integer or None) – (Optional) Set the random generator seed for the sampler. Unique seeds in the form seed+i are used to initialize the individual samplers, with i being the index of the sampler.

Returns:

The instantiated vector of action samplers

get_actions(self: city_of_gold.vec.sampler.vec_sampler_6) → numpy.ndarray[city_of_gold.ActionData]

Get a reference to the samplers internal vector of sampled actions.

The contents are overwritten every time sample() is called. :returns: The vector where sampled actions are placed :rtype: numpy.ndarray of ActionData

sample(self: city_of_gold.vec.sampler.vec_sampler_6, action_mask: numpy.ndarray[city_of_gold.ActionMask]) → None

Generate a uniform sample of the valid action space for each environment.

Update the contents of actions with a new sample masked using the input action mask. :param action_mask: mask specifying valid actions for each environment :type action_mask: numpy.ndarray of ActionMask :return: None

class city_of_gold.vec.sampler.vec_sampler_64

Bases: pybind11_object

Vectorized random agent for 64 environments

Parameters:

seed (unsigned integer or None) – (Optional) Set the random generator seed for the sampler. Unique seeds in the form seed+i are used to initialize the individual samplers, with i being the index of the sampler.

Returns:

The instantiated vector of action samplers

get_actions(self: city_of_gold.vec.sampler.vec_sampler_64) → numpy.ndarray[city_of_gold.ActionData]

Get a reference to the samplers internal vector of sampled actions.

The contents are overwritten every time sample() is called. :returns: The vector where sampled actions are placed :rtype: numpy.ndarray of ActionData

sample(self: city_of_gold.vec.sampler.vec_sampler_64, action_mask: numpy.ndarray[city_of_gold.ActionMask]) → None

Generate a uniform sample of the valid action space for each environment.

Update the contents of actions with a new sample masked using the input action mask. :param action_mask: mask specifying valid actions for each environment :type action_mask: numpy.ndarray of ActionMask :return: None

class city_of_gold.vec.sampler.vec_sampler_7

Bases: pybind11_object

Vectorized random agent for 7 environments

Parameters:

seed (unsigned integer or None) – (Optional) Set the random generator seed for the sampler. Unique seeds in the form seed+i are used to initialize the individual samplers, with i being the index of the sampler.

Returns:

The instantiated vector of action samplers

get_actions(self: city_of_gold.vec.sampler.vec_sampler_7) → numpy.ndarray[city_of_gold.ActionData]

Get a reference to the samplers internal vector of sampled actions.

The contents are overwritten every time sample() is called. :returns: The vector where sampled actions are placed :rtype: numpy.ndarray of ActionData

sample(self: city_of_gold.vec.sampler.vec_sampler_7, action_mask: numpy.ndarray[city_of_gold.ActionMask]) → None

Generate a uniform sample of the valid action space for each environment.

Update the contents of actions with a new sample masked using the input action mask. :param action_mask: mask specifying valid actions for each environment :type action_mask: numpy.ndarray of ActionMask :return: None

class city_of_gold.vec.sampler.vec_sampler_8

Bases: pybind11_object

Vectorized random agent for 8 environments

Parameters:

seed (unsigned integer or None) – (Optional) Set the random generator seed for the sampler. Unique seeds in the form seed+i are used to initialize the individual samplers, with i being the index of the sampler.

Returns:

The instantiated vector of action samplers

get_actions(self: city_of_gold.vec.sampler.vec_sampler_8) → numpy.ndarray[city_of_gold.ActionData]

Get a reference to the samplers internal vector of sampled actions.

The contents are overwritten every time sample() is called. :returns: The vector where sampled actions are placed :rtype: numpy.ndarray of ActionData

sample(self: city_of_gold.vec.sampler.vec_sampler_8, action_mask: numpy.ndarray[city_of_gold.ActionMask]) → None

Generate a uniform sample of the valid action space for each environment.

Update the contents of actions with a new sample masked using the input action mask. :param action_mask: mask specifying valid actions for each environment :type action_mask: numpy.ndarray of ActionMask :return: None