Reinforcement Learning utils

Eligibility trace

EligibilityTrace(shape, name='replacing')[source]

Factory method to create an eligibility trace of the provided type.

Parameters:

  • shape (list) – shape of the eligibility trace table;

  • name (str, 'replacing') – type of the eligibility trace.

Returns:

The eligibility trace table of the provided shape and type.

class ReplacingTrace(shape, initial_value=0.0, dtype=None)[source]

Bases: Table

Replacing trace.

reset()[source]
update(state, action)[source]
__init__(shape, initial_value=0.0, dtype=None)

Constructor.

Parameters:

  • shape (tuple) – the shape of the tabular regressor.

  • initial_value (float, 0.) – the initial value for each entry of the tabular regressor.

  • dtype ([int, float], None) – the dtype of the table array.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

static _append_folder(folder, name)
static _get_serialization_method(class_name)
static _load_json(zip_file, name)
classmethod _load_list(zip_file, folder, length)
static _load_mushroom(zip_file, name)
static _load_numpy(zip_file, name)
static _load_pickle(zip_file, name)
static _load_torch(zip_file, name)
_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

static _save_json(zip_file, name, obj, folder, **_)
static _save_mushroom(zip_file, name, obj, folder, full_save)
static _save_numpy(zip_file, name, obj, folder, **_)
static _save_pickle(zip_file, name, obj, folder, **_)
static _save_torch(zip_file, name, obj, folder, **_)
copy()
Returns:

A deepcopy of the agent.

fit(x, y)
Parameters:

  • x (int) – index of the table to be filled;

  • y (float) – value to fill in the table.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

classmethod load_zip(zip_file, folder='')
property n_actions

Returns: The number of actions considered by the table.

predict(*z)

Predict the output of the table given an input.

Parameters:

  • *z (list) – list of input of the model. If the table is a Q-table,

  • depending (this list may contain states or states and actions) – on whether the call requires to predict all q-values or only one q-value corresponding to the provided action;

Returns:

The table prediction.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

property shape

Returns: The shape of the table.

class AccumulatingTrace(shape, initial_value=0.0, dtype=None)[source]

Bases: Table

Accumulating trace.

reset()[source]
update(state, action)[source]
__init__(shape, initial_value=0.0, dtype=None)

Constructor.

Parameters:

  • shape (tuple) – the shape of the tabular regressor.

  • initial_value (float, 0.) – the initial value for each entry of the tabular regressor.

  • dtype ([int, float], None) – the dtype of the table array.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

static _append_folder(folder, name)
static _get_serialization_method(class_name)
static _load_json(zip_file, name)
classmethod _load_list(zip_file, folder, length)
static _load_mushroom(zip_file, name)
static _load_numpy(zip_file, name)
static _load_pickle(zip_file, name)
static _load_torch(zip_file, name)
_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

static _save_json(zip_file, name, obj, folder, **_)
static _save_mushroom(zip_file, name, obj, folder, full_save)
static _save_numpy(zip_file, name, obj, folder, **_)
static _save_pickle(zip_file, name, obj, folder, **_)
static _save_torch(zip_file, name, obj, folder, **_)
copy()
Returns:

A deepcopy of the agent.

fit(x, y)
Parameters:

  • x (int) – index of the table to be filled;

  • y (float) – value to fill in the table.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

classmethod load_zip(zip_file, folder='')
property n_actions

Returns: The number of actions considered by the table.

predict(*z)

Predict the output of the table given an input.

Parameters:

  • *z (list) – list of input of the model. If the table is a Q-table,

  • depending (this list may contain states or states and actions) – on whether the call requires to predict all q-values or only one q-value corresponding to the provided action;

Returns:

The table prediction.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

property shape

Returns: The shape of the table.

Optimizers

class Optimizer(lr=0.001, maximize=True, *params)[source]

Bases: Serializable

Base class for gradient optimizers. These objects take the current parameters and the gradient estimate to compute the new parameters.

__init__(lr=0.001, maximize=True, *params)[source]

Constructor

Parameters:

  • lr ([float, Parameter]) – the learning rate;

  • maximize (bool, True) – by default Optimizers do a gradient ascent step. Set to False for gradient descent.

__call__(*args, **kwargs)[source]

Call self as a function.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

static _append_folder(folder, name)
static _get_serialization_method(class_name)
static _load_json(zip_file, name)
classmethod _load_list(zip_file, folder, length)
static _load_mushroom(zip_file, name)
static _load_numpy(zip_file, name)
static _load_pickle(zip_file, name)
static _load_torch(zip_file, name)
_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

static _save_json(zip_file, name, obj, folder, **_)
static _save_mushroom(zip_file, name, obj, folder, full_save)
static _save_numpy(zip_file, name, obj, folder, **_)
static _save_pickle(zip_file, name, obj, folder, **_)
static _save_torch(zip_file, name, obj, folder, **_)
copy()
Returns:

A deepcopy of the agent.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

classmethod load_zip(zip_file, folder='')
save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

class AdaptiveOptimizer(eps, maximize=True)[source]

Bases: Optimizer

This class implements an adaptive gradient step optimizer. Instead of moving of a step proportional to the gradient, takes a step limited by a given metric M. To specify the metric, the natural gradient has to be provided. If natural gradient is not provided, the identity matrix is used.

The step rule is:

\[ \begin{align}\begin{aligned}\Delta\theta=\underset{\Delta\vartheta}{argmax}\Delta\vartheta^{t}\nabla_{\theta}J\\s.t.:\Delta\vartheta^{T}M\Delta\vartheta\leq\varepsilon\end{aligned}\end{align} \]

Lecture notes, Neumann G. http://www.ias.informatik.tu-darmstadt.de/uploads/Geri/lecture-notes-constraint.pdf

__init__(eps, maximize=True)[source]

Constructor.

Parameters:

  • eps (float) – the maximum step defined by the metric;

  • maximize (bool, True) – by default Optimizers do a gradient ascent step. Set to False for gradient descent.

__call__(params, *args, **kwargs)[source]

Call self as a function.

get_value(*args, **kwargs)[source]
_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

static _append_folder(folder, name)
static _get_serialization_method(class_name)
static _load_json(zip_file, name)
classmethod _load_list(zip_file, folder, length)
static _load_mushroom(zip_file, name)
static _load_numpy(zip_file, name)
static _load_pickle(zip_file, name)
static _load_torch(zip_file, name)
_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

static _save_json(zip_file, name, obj, folder, **_)
static _save_mushroom(zip_file, name, obj, folder, full_save)
static _save_numpy(zip_file, name, obj, folder, **_)
static _save_pickle(zip_file, name, obj, folder, **_)
static _save_torch(zip_file, name, obj, folder, **_)
copy()
Returns:

A deepcopy of the agent.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

classmethod load_zip(zip_file, folder='')
save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

class SGDOptimizer(lr=0.001, maximize=True)[source]

Bases: Optimizer

This class implements the SGD optimizer.

__init__(lr=0.001, maximize=True)[source]

Constructor.

Parameters:

  • lr ([float, Parameter], 0.001) – the learning rate;

  • maximize (bool, True) – by default Optimizers do a gradient ascent step. Set to False for gradient descent.

__call__(params, grads)[source]

Call self as a function.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

static _append_folder(folder, name)
static _get_serialization_method(class_name)
static _load_json(zip_file, name)
classmethod _load_list(zip_file, folder, length)
static _load_mushroom(zip_file, name)
static _load_numpy(zip_file, name)
static _load_pickle(zip_file, name)
static _load_torch(zip_file, name)
_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

static _save_json(zip_file, name, obj, folder, **_)
static _save_mushroom(zip_file, name, obj, folder, full_save)
static _save_numpy(zip_file, name, obj, folder, **_)
static _save_pickle(zip_file, name, obj, folder, **_)
static _save_torch(zip_file, name, obj, folder, **_)
copy()
Returns:

A deepcopy of the agent.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

classmethod load_zip(zip_file, folder='')
save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

class AdamOptimizer(lr=0.001, beta1=0.9, beta2=0.999, eps=1e-07, maximize=True)[source]

Bases: Optimizer

This class implements the Adam optimizer.

__init__(lr=0.001, beta1=0.9, beta2=0.999, eps=1e-07, maximize=True)[source]

Constructor.

Parameters:

  • lr ([float, Parameter], 0.001) – the learning rate;

  • beta1 (float, 0.9) – Adam beta1 parameter;

  • beta2 (float, 0.999) – Adam beta2 parameter;

  • maximize (bool, True) – by default Optimizers do a gradient ascent step. Set to False for gradient descent.

__call__(params, grads)[source]

Call self as a function.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

static _append_folder(folder, name)
static _get_serialization_method(class_name)
static _load_json(zip_file, name)
classmethod _load_list(zip_file, folder, length)
static _load_mushroom(zip_file, name)
static _load_numpy(zip_file, name)
static _load_pickle(zip_file, name)
static _load_torch(zip_file, name)
_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

static _save_json(zip_file, name, obj, folder, **_)
static _save_mushroom(zip_file, name, obj, folder, full_save)
static _save_numpy(zip_file, name, obj, folder, **_)
static _save_pickle(zip_file, name, obj, folder, **_)
static _save_torch(zip_file, name, obj, folder, **_)
copy()
Returns:

A deepcopy of the agent.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

classmethod load_zip(zip_file, folder='')
save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

Parameters

class Parameter(value, min_value=None, max_value=None, size=(1,))[source]

Bases: Serializable

This class implements function to manage parameters, such as learning rate. It also allows to have a single parameter for each state of state-action tuple.

__init__(value, min_value=None, max_value=None, size=(1,))[source]

Constructor.

Parameters:

  • value (float) – initial value of the parameter;

  • min_value (float, None) – minimum value that the parameter can reach when decreasing;

  • max_value (float, None) – maximum value that the parameter can reach when increasing;

  • size (tuple, (1,)) – shape of the matrix of parameters; this shape can be used to have a single parameter for each state or state-action tuple.

__call__(*idx, **kwargs)[source]

Update and return the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The updated parameter in the provided index.

get_value(*idx, **kwargs)[source]

Return the current value of the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The current value of the parameter in the provided index.

_compute(*idx, **kwargs)[source]
Returns:

The value of the parameter in the provided index.

update(*idx, **kwargs)[source]

Updates the number of visit of the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter whose number of visits has to be updated.

property shape

Returns: The shape of the table of parameters.

property initial_value

Returns: The initial value of the parameters.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

class LinearParameter(value, threshold_value, n, size=(1,))[source]

Bases: Parameter

This class implements a linearly changing parameter according to the number of times it has been used. The parameter changes following the formula:

\[v_n = \textrm{clip}(v_0 + \dfrac{v_{th} - v_0}{n}, v_{th})\]

where \(v_0\) is the initial value of the parameter, \(n\) is the number of steps and \(v_{th}\) is the upper or lower threshold for the parameter.

__init__(value, threshold_value, n, size=(1,))[source]

Constructor.

Parameters:

  • value (float) – initial value of the parameter;

  • threshold_value (float, None) – minimum or maximum value that the parameter can reach;

  • n (int) – number of time steps needed to reach the threshold value;

  • size (tuple, (1,)) – shape of the matrix of parameters; this shape can be used to have a single parameter for each state or state-action tuple.

_compute(*idx, **kwargs)[source]
Returns:

The value of the parameter in the provided index.

__call__(*idx, **kwargs)

Update and return the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The updated parameter in the provided index.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

get_value(*idx, **kwargs)

Return the current value of the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The current value of the parameter in the provided index.

property initial_value

Returns: The initial value of the parameters.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

property shape

Returns: The shape of the table of parameters.

update(*idx, **kwargs)

Updates the number of visit of the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter whose number of visits has to be updated.

class DecayParameter(value, exp=1.0, min_value=None, max_value=None, size=(1,))[source]

Bases: Parameter

This class implements a decaying parameter. The decay follows the formula:

\[v_n = \dfrac{v_0}{n^p}\]

where \(v_0\) is the initial value of the parameter, \(n\) is the number of steps and \(p\) is an arbitrary exponent.

__init__(value, exp=1.0, min_value=None, max_value=None, size=(1,))[source]

Constructor.

Parameters:

  • value (float) – initial value of the parameter;

  • exp (float, 1.) – exponent for the step decay;

  • min_value (float, None) – minimum value that the parameter can reach when decreasing;

  • max_value (float, None) – maximum value that the parameter can reach when increasing;

  • size (tuple, (1,)) – shape of the matrix of parameters; this shape can be used to have a single parameter for each state or state-action tuple.

_compute(*idx, **kwargs)[source]
Returns:

The value of the parameter in the provided index.

__call__(*idx, **kwargs)

Update and return the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The updated parameter in the provided index.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

get_value(*idx, **kwargs)

Return the current value of the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The current value of the parameter in the provided index.

property initial_value

Returns: The initial value of the parameters.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

property shape

Returns: The shape of the table of parameters.

update(*idx, **kwargs)

Updates the number of visit of the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter whose number of visits has to be updated.

Preprocessors

class Preprocessor[source]

Bases: Serializable

Abstract preprocessor class.

__call__(obs)[source]

Preprocess the observations.

Parameters:

obs (Array) – observations to be preprocessed.

Returns:

Preprocessed observations.

update(obs)[source]

Update internal state of the preprocessor using the current observations.

Parameters:

obs (Array) – observations to be preprocessed.

__init__()
_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

class StandardizationPreprocessor(mdp_info, backend, clip_obs=10.0, alpha=1e-32)[source]

Bases: Preprocessor

Preprocess observations from the environment using a running standardization.

__init__(mdp_info, backend, clip_obs=10.0, alpha=1e-32)[source]

Constructor.

Parameters:

  • mdp_info (MDPInfo) – information of the MDP;

  • backend (str) – name of the backend to be used;

  • clip_obs (float, 10.) – values to clip the normalized observations;

  • alpha (float, 1e-32) – moving average catchup parameter for the normalization.

__call__(obs)[source]

Preprocess the observations.

Parameters:

obs (Array) – observations to be preprocessed.

Returns:

Preprocessed observations.

update(obs)[source]

Update internal state of the preprocessor using the current observations.

Parameters:

obs (Array) – observations to be preprocessed.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

class MinMaxPreprocessor(mdp_info, backend, clip_obs=10.0, alpha=1e-32)[source]

Bases: StandardizationPreprocessor

Preprocess observations from the environment using the bounds of the observation space of the environment. For observations that are not limited falls back to using running mean standardization.

__init__(mdp_info, backend, clip_obs=10.0, alpha=1e-32)[source]

Constructor.

Parameters:

  • mdp_info (MDPInfo) – information of the MDP;

  • backend (str) – name of the backend to be used;

  • clip_obs (float, 10.) – values to clip the normalized observations;

  • alpha (float, 1e-32) – moving average catchup parameter for the normalization.

__call__(obs)[source]

Preprocess the observations.

Parameters:

obs (Array) – observations to be preprocessed.

Returns:

Preprocessed observations.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

update(obs)

Update internal state of the preprocessor using the current observations.

Parameters:

obs (Array) – observations to be preprocessed.

Replay memory

class ReplayMemory(mdp_info, agent_info, initial_size, max_size)[source]

Bases: Serializable

This class implements function to manage a replay memory as the one used in “Human-Level Control Through Deep Reinforcement Learning” by Mnih V. et al..

__init__(mdp_info, agent_info, initial_size, max_size)[source]

Constructor.

Parameters:

  • mdp_info (MDPInfo) – information about the MDP;

  • agent_info (AgentInfo) – information about the agent;

  • initial_size (int) – initial size of the replay buffer;

  • max_size (int) – maximum size of the replay buffer;

add(dataset, n_steps_return=1, gamma=1.0)[source]

Add elements to the replay memory.

Parameters:

  • dataset (Dataset) – dataset class elements to add to the replay memory;

  • n_steps_return (int, 1) – number of steps to consider for computing n-step return;

  • gamma (float, 1.) – discount factor for n-step return.

get(n_samples)[source]

Returns the provided number of states from the replay memory.

Parameters:

n_samples (int) – the number of samples to return.

Returns:

The requested number of samples.

reset()[source]

Reset the replay memory.

property initialized

Returns: Whether the replay memory has reached the number of elements that allows it to be used.

property size

Returns: The number of elements contained in the replay memory.

_post_load()[source]

This method can be overwritten to implement logic that is executed after the loading of the agent.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

copy()
Returns:

A deepcopy of the agent.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

class SequenceReplayMemory(mdp_info, agent_info, initial_size, max_size, truncation_length)[source]

Bases: ReplayMemory

This class extend the base replay memory to allow sampling sequences of a certain length. This is useful for training recurrent agents or agents operating on a window of states etc.

__init__(mdp_info, agent_info, initial_size, max_size, truncation_length)[source]

Constructor.

Parameters:

  • mdp_info (MDPInfo) – information about the MDP;

  • agent_info (AgentInfo) – information about the agent;

  • initial_size (int) – initial size of the replay buffer;

  • max_size (int) – maximum size of the replay buffer;

  • truncation_length (int) – truncation length to be sampled;

get(n_samples)[source]

Returns the provided number of states from the replay memory. :param n_samples: the number of samples to return. :type n_samples: int

Returns:

The requested number of samples.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

add(dataset, n_steps_return=1, gamma=1.0)

Add elements to the replay memory.

Parameters:

  • dataset (Dataset) – dataset class elements to add to the replay memory;

  • n_steps_return (int, 1) – number of steps to consider for computing n-step return;

  • gamma (float, 1.) – discount factor for n-step return.

copy()
Returns:

A deepcopy of the agent.

property initialized

Returns: Whether the replay memory has reached the number of elements that allows it to be used.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

reset()

Reset the replay memory.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

property size

Returns: The number of elements contained in the replay memory.

class SumTree(mdp_info, agent_info, max_size)[source]

Bases: Serializable

This class implements a sum tree data structure. This is used, for instance, by PrioritizedReplayMemory.

__init__(mdp_info, agent_info, max_size)[source]

Constructor.

Parameters:

  • mdp_info (MDPInfo) – information about the MDP;

  • agent_info (AgentInfo) – information about the agent;

  • max_size (int) – maximum size of the tree;

add(dataset, priority, n_steps_return, gamma)[source]

Add elements to the tree.

Parameters:

  • dataset (Dataset) – dataset class elements to add to the replay memory;

  • priority (np.ndarray) – priority of each sample in the dataset;

  • n_steps_return (int) – number of steps to consider for computing n-step return;

  • gamma (float) – discount factor for n-step return.

get(s)[source]

Returns the provided number of states from the replay memory.

Parameters:

s (float) – the value of the samples to return.

Returns:

The requested sample.

get_ind(s)[source]

Returns the provided number of states from the replay memory.

Parameters:

s (float) – the value of the samples to return.

Returns:

The requested sample.

update(idx, priorities)[source]

Update the priority of the sample at the provided index in the dataset.

Parameters:

  • idx (np.ndarray) – indexes of the transitions in the dataset;

  • priorities (np.ndarray) – priorities of the transitions.

property size

Returns: The current size of the tree.

property max_p

Returns: The maximum priority among the ones in the tree.

property total_p

Returns: The sum of the priorities in the tree, i.e. the value of the root node.

_post_load()[source]

This method can be overwritten to implement logic that is executed after the loading of the agent.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

copy()
Returns:

A deepcopy of the agent.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

class PrioritizedReplayMemory(mdp_info, agent_info, initial_size, max_size, alpha, beta, epsilon=0.01)[source]

Bases: Serializable

This class implements function to manage a prioritized replay memory as the one used in “Prioritized Experience Replay” by Schaul et al., 2015.

__init__(mdp_info, agent_info, initial_size, max_size, alpha, beta, epsilon=0.01)[source]

Constructor.

Parameters:

  • mdp_info (MDPInfo) – information about the MDP;

  • agent_info (AgentInfo) – information about the agent;

  • initial_size (int) – initial number of elements in the replay memory;

  • max_size (int) – maximum number of elements that the replay memory can contain;

  • alpha (float) – prioritization coefficient;

  • beta ([float, Parameter]) – importance sampling coefficient;

  • epsilon (float, .01) – small value to avoid zero probabilities.

add(dataset, p, n_steps_return=1, gamma=1.0)[source]

Add elements to the replay memory.

Parameters:

  • dataset (Dataset) – list of elements to add to the replay memory;

  • p (np.ndarray) – priority of each sample in the dataset.

  • n_steps_return (int, 1) – number of steps to consider for computing n-step return;

  • gamma (float, 1.) – discount factor for n-step return.

get(n_samples)[source]

Returns the provided number of states from the replay memory.

Parameters:

n_samples (int) – the number of samples to return.

Returns:

The requested number of samples.

update(error, idx)[source]

Update the priority of the sample at the provided index in the dataset.

Parameters:

  • error (np.ndarray) – errors to consider to compute the priorities;

  • idx (np.ndarray) – indexes of the transitions in the dataset.

property initialized

Returns: Whether the replay memory has reached the number of elements that allows it to be used.

property max_priority

Returns: The maximum value of priority inside the replay memory.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

Running Statistics

class RunningStandardization(shape, backend, alpha=1e-32)[source]

Bases: Serializable

Compute a running standardization of values according to Welford’s online algorithm: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford’s_online_algorithm

__init__(shape, backend, alpha=1e-32)[source]

Constructor.

Parameters:

  • shape (tuple) – shape of the data to standardize;

  • backend (str) – name of the backend to be used;

  • alpha (float, 1e-32) – minimum learning rate.

reset()[source]

Reset the mean and standard deviation.

update_stats(value)[source]

Update the statistics with the current data value.

Parameters:

value (Array) – current data value to use for the update.

property mean

Returns: The estimated mean value.

property std

Returns: The estimated standard deviation value.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

class RunningExpWeightedAverage(shape, alpha, backend, init_value=None)[source]

Bases: Serializable

Compute an exponentially weighted moving average.

__init__(shape, alpha, backend, init_value=None)[source]

Constructor.

Parameters:

  • shape (tuple) – shape of the data to standardize;

  • alpha (float) – learning rate;

  • backend (str) – name of the backend to be used;

  • init_value (np.ndarray) – initial value of the filter.

reset(init_value=None)[source]

Reset the mean and standard deviation.

Parameters:

init_value (Array) – initial value of the filter.

update_stats(value)[source]

Update the statistics with the current data value.

Parameters:

value (Array) – current data value to use for the update.

property mean

Returns: The estimated mean value.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

class RunningAveragedWindow(shape, window_size, backend, init_value=None)[source]

Bases: Serializable

Compute the running average using a window of fixed size.

__init__(shape, window_size, backend, init_value=None)[source]

Constructor.

Parameters:

  • shape (tuple) – shape of the data to standardize;

  • window_size (int) – size of the windows;

  • backend (str) – name of the backend to be used;

  • init_value (np.ndarray) – initial value of the filter.

reset(init_value=None)[source]

Reset the window.

Parameters:

init_value (np.ndarray) – initial value of the filter.

update_stats(value)[source]

Update the statistics with the current data value.

Parameters:

value (np.ndarray) – current data value to use for the update.

property mean

Returns: The estimated mean value.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

Spaces

class Box(low, high, shape=None, data_type=<class 'float'>)[source]

Bases: Serializable

This class implements functions to manage continuous states and action spaces. It is similar to the Box class in gym.spaces.box.

__init__(low, high, shape=None, data_type=<class 'float'>)[source]

Constructor.

Parameters:

  • low ([float, np.ndarray]) – the minimum value of each dimension of the space. If a scalar value is provided, this value is considered as the minimum one for each dimension. If a np.ndarray is provided, each i-th element is considered the minimum value of the i-th dimension;

  • high ([float, np.ndarray]) – the maximum value of dimensions of the space. If a scalar value is provided, this value is considered as the maximum one for each dimension. If a np.ndarray is provided, each i-th element is considered the maximum value of the i-th dimension;

  • shape (np.ndarray, None) – the dimension of the space. Must match the shape of low and high, if they are np.ndarray.

  • data_type (class, float) – the data type to be used.

property low

Returns: The minimum value of each dimension of the space.

property high

Returns: The maximum value of each dimension of the space.

property shape

Returns: The dimensions of the space.

class Discrete(n)[source]

Bases: Serializable

This class implements functions to manage discrete states and action spaces. It is similar to the Discrete class in gym.spaces.discrete.

__init__(n)[source]

Constructor.

Parameters:

n (int) – the number of values of the space.

property size

Returns: The number of elements of the space.

property shape

Returns: The shape of the space that is always (1,).

Value Functions

compute_advantage_montecarlo(V, s, ss, r, absorbing, gamma)[source]

Function to estimate the advantage and new value function target over a dataset. The value function is estimated using rollouts (monte carlo estimation).

Parameters:

  • V (Regressor) – the current value function regressor;

  • s (torch.tensor) – the set of states in which we want to evaluate the advantage;

  • ss (torch.tensor) – the set of next states in which we want to evaluate the advantage;

  • r (torch.tensor) – the reward obtained in each transition from state s to state ss;

  • absorbing (torch.tensor) – an array of boolean flags indicating if the reached state is absorbing;

  • gamma (float) – the discount factor of the considered problem.

Returns:

The new estimate for the value function of the next state and the advantage function.

compute_advantage(V, s, ss, r, absorbing, gamma)[source]

Function to estimate the advantage and new value function target over a dataset. The value function is estimated using bootstrapping.

Parameters:

  • V (Regressor) – the current value function regressor;

  • s (torch.tensor) – the set of states in which we want to evaluate the advantage;

  • ss (torch.tensor) – the set of next states in which we want to evaluate the advantage;

  • r (torch.tensor) – the reward obtained in each transition from state s to state ss;

  • absorbing (torch.tensor) – an array of boolean flags indicating if the reached state is absorbing;

  • gamma (float) – the discount factor of the considered problem.

Returns:

The new estimate for the value function of the next state and the advantage function.

compute_gae(V, s, ss, r, absorbing, last, gamma, lam)[source]

Function to compute Generalized Advantage Estimation (GAE) and new value function target over a dataset.

“High-Dimensional Continuous Control Using Generalized Advantage Estimation”. Schulman J. et al.. 2016.

Parameters:

  • V (Regressor) – the current value function regressor;

  • s (torch.tensor) – the set of states in which we want to evaluate the advantage;

  • ss (torch.tensor) – the set of next states in which we want to evaluate the advantage;

  • r (torch.tensor) – the reward obtained in each transition from state s to state ss;

  • absorbing (torch.tensor) – an array of boolean flags indicating if the reached state is absorbing;

  • last (torch.tensor) – an array of boolean flags indicating if the reached state is the last of the trajectory;

  • gamma (float) – the discount factor of the considered problem;

  • lam (float) – the value for the lamba coefficient used by GEA algorithm.

Returns:

The new estimate for the value function of the next state and the estimated generalized advantage.

Variance parameters

class VarianceParameter(value, exponential=False, min_value=None, tol=1.0, size=(1,))[source]

Bases: Parameter

Abstract class to implement variance-dependent parameters. A target parameter is expected.

__init__(value, exponential=False, min_value=None, tol=1.0, size=(1,))[source]

Constructor.

Parameters:

tol (float) – value of the variance of the target variable such that The parameter value is 0.5.

_compute(*idx, **kwargs)[source]
Returns:

The value of the parameter in the provided index.

update(*idx, **kwargs)[source]

Updates the value of the parameter in the provided index.

Parameters:

  • *idx (list) – index of the parameter whose number of visits has to be updated.

  • target (float) – Value of the target variable;

  • factor (float) – Multiplicative factor for the parameter value, useful when the parameter depend on another parameter value.

__call__(*idx, **kwargs)

Update and return the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The updated parameter in the provided index.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

get_value(*idx, **kwargs)

Return the current value of the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The current value of the parameter in the provided index.

property initial_value

Returns: The initial value of the parameters.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

property shape

Returns: The shape of the table of parameters.

class VarianceIncreasingParameter(value, exponential=False, min_value=None, tol=1.0, size=(1,))[source]

Bases: VarianceParameter

Class implementing a parameter that increases with the target variance.

__call__(*idx, **kwargs)

Update and return the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The updated parameter in the provided index.

__init__(value, exponential=False, min_value=None, tol=1.0, size=(1,))

Constructor.

Parameters:

tol (float) – value of the variance of the target variable such that The parameter value is 0.5.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_compute(*idx, **kwargs)
Returns:

The value of the parameter in the provided index.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

get_value(*idx, **kwargs)

Return the current value of the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The current value of the parameter in the provided index.

property initial_value

Returns: The initial value of the parameters.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

property shape

Returns: The shape of the table of parameters.

update(*idx, **kwargs)

Updates the value of the parameter in the provided index.

Parameters:

  • *idx (list) – index of the parameter whose number of visits has to be updated.

  • target (float) – Value of the target variable;

  • factor (float) – Multiplicative factor for the parameter value, useful when the parameter depend on another parameter value.

class VarianceDecreasingParameter(value, exponential=False, min_value=None, tol=1.0, size=(1,))[source]

Bases: VarianceParameter

Class implementing a parameter that decreases with the target variance.

__call__(*idx, **kwargs)

Update and return the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The updated parameter in the provided index.

__init__(value, exponential=False, min_value=None, tol=1.0, size=(1,))

Constructor.

Parameters:

tol (float) – value of the variance of the target variable such that The parameter value is 0.5.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_compute(*idx, **kwargs)
Returns:

The value of the parameter in the provided index.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

get_value(*idx, **kwargs)

Return the current value of the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The current value of the parameter in the provided index.

property initial_value

Returns: The initial value of the parameters.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

property shape

Returns: The shape of the table of parameters.

update(*idx, **kwargs)

Updates the value of the parameter in the provided index.

Parameters:

  • *idx (list) – index of the parameter whose number of visits has to be updated.

  • target (float) – Value of the target variable;

  • factor (float) – Multiplicative factor for the parameter value, useful when the parameter depend on another parameter value.

class WindowedVarianceParameter(value, exponential=False, min_value=None, tol=1.0, window=100, size=(1,))[source]

Bases: Parameter

Abstract class to implement variance-dependent parameters. A target parameter is expected. differently from the “Variance Parameter” class the variance is computed in a window interval.

__init__(value, exponential=False, min_value=None, tol=1.0, window=100, size=(1,))[source]

Constructor.

Parameters:

  • tol (float) – value of the variance of the target variable such that the parameter value is 0.5.

  • window (int) –

_compute(*idx, **kwargs)[source]
Returns:

The value of the parameter in the provided index.

update(*idx, **kwargs)[source]

Updates the value of the parameter in the provided index.

Parameters:

  • *idx (list) – index of the parameter whose number of visits has to be updated.

  • target (float) – Value of the target variable;

  • factor (float) – Multiplicative factor for the parameter value, useful when the parameter depend on another parameter value.

__call__(*idx, **kwargs)

Update and return the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The updated parameter in the provided index.

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

get_value(*idx, **kwargs)

Return the current value of the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The current value of the parameter in the provided index.

property initial_value

Returns: The initial value of the parameters.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

property shape

Returns: The shape of the table of parameters.

class WindowedVarianceIncreasingParameter(value, exponential=False, min_value=None, tol=1.0, window=100, size=(1,))[source]

Bases: WindowedVarianceParameter

Class implementing a parameter that decreases with the target variance, where the variance is computed in a fixed length window.

__call__(*idx, **kwargs)

Update and return the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The updated parameter in the provided index.

__init__(value, exponential=False, min_value=None, tol=1.0, window=100, size=(1,))

Constructor.

Parameters:

  • tol (float) – value of the variance of the target variable such that the parameter value is 0.5.

  • window (int) –

_add_save_attr(**attr_dict)

Add attributes that should be saved for an agent. For every attribute, it is necessary to specify the method to be used to save and load. Available methods are: numpy, mushroom, torch, json, pickle, primitive and none. The primitive method can be used to store primitive attributes, while the none method always skip the attribute, but ensure that it is initialized to None after the load. The mushroom method can be used with classes that implement the Serializable interface. All the other methods use the library named. If a “!” character is added at the end of the method, the field will be saved only if full_save is set to True.

Parameters:

**attr_dict – dictionary of attributes mapped to the method that should be used to save and load them.

_compute(*idx, **kwargs)
Returns:

The value of the parameter in the provided index.

_post_load()

This method can be overwritten to implement logic that is executed after the loading of the agent.

copy()
Returns:

A deepcopy of the agent.

get_value(*idx, **kwargs)

Return the current value of the parameter in the provided index.

Parameters:

*idx (list) – index of the parameter to return.

Returns:

The current value of the parameter in the provided index.

property initial_value

Returns: The initial value of the parameters.

classmethod load(path)

Load and deserialize the agent from the given location on disk.

Parameters:

path (Path, string) – Relative or absolute path to the agents save location.

Returns:

The loaded agent.

save(path, full_save=False)

Serialize and save the object to the given path on disk.

Parameters:

  • path (Path, str) – Relative or absolute path to the object save location;

  • full_save (bool) – Flag to specify the amount of data to save for MushroomRL data structures.

save_zip(zip_file, full_save, folder='')

Serialize and save the agent to the given path on disk.

Parameters:

  • zip_file (ZipFile) – ZipFile where te object needs to be saved;

  • full_save (bool) – flag to specify the amount of data to save for MushroomRL data structures;

  • folder (string, '') – subfolder to be used by the save method.

property shape

Returns: The shape of the table of parameters.

update(*idx, **kwargs)

Updates the value of the parameter in the provided index.

Parameters:

  • *idx (list) – index of the parameter whose number of visits has to be updated.

  • target (float) – Value of the target variable;

  • factor (float) – Multiplicative factor for the parameter value, useful when the parameter depend on another parameter value.