Policy¶

class
Policy
[source]¶ Bases:
mushroom_rl.core.serialization.Serializable
Interface representing a generic policy. A policy is a probability distribution that gives the probability of taking an action given a specified state. A policy is used by mushroom agents to interact with the environment.

__call__
(*args)[source]¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

draw_action
(state)[source]¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

reset
()[source]¶ Useful when the policy needs a special initialization at the beginning of an episode.

__init__
¶ Initialize self. See help(type(self)) for accurate signature.

_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
ParametricPolicy
[source]¶ Bases:
mushroom_rl.policy.policy.Policy
Interface for a generic parametric policy. A parametric policy is a policy that depends on set of parameters, called the policy weights. If the policy is differentiable, the derivative of the probability for a specified stateaction pair can be provided.

diff_log
(state, action)[source]¶ Compute the gradient of the logarithm of the probability density function, in the specified state and action pair, i.e.:
\[\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the gradient is computed
 action (np.ndarray) – the action where the gradient is computed
Returns: The gradient of the logarithm of the pdf w.r.t. the policy weights

diff
(state, action)[source]¶ Compute the derivative of the probability density function, in the specified state and action pair. Normally it is computed w.r.t. the derivative of the logarithm of the probability density function, exploiting the likelihood ratio trick, i.e.:
\[\nabla_{\theta}p(s,a)=p(s,a)\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the derivative is computed
 action (np.ndarray) – the action where the derivative is computed
Returns: The derivative w.r.t. the policy weights

set_weights
(weights)[source]¶ Setter.
Parameters: weights (np.ndarray) – the vector of the new weights to be used by the policy.

weights_size
¶ Property.
Returns: The size of the policy weights.

__call__
(*args)¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

__init__
¶ Initialize self. See help(type(self)) for accurate signature.

_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.

draw_action
(state)¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

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
()¶ Useful when the policy needs a special initialization at the beginning of an episode.

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.

Deterministic policy¶

class
DeterministicPolicy
(mu)[source]¶ Bases:
mushroom_rl.policy.policy.ParametricPolicy
Simple parametric policy representing a deterministic policy. As deterministic policies are degenerate probability functions where all the probability mass is on the deterministic action,they are not differentiable, even if the mean value approximator is differentiable.

__init__
(mu)[source]¶ Constructor.
Parameters: mu (Regressor) – the regressor representing the action to select in each state.

__call__
(state, action)[source]¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

draw_action
(state)[source]¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

set_weights
(weights)[source]¶ Setter.
Parameters: weights (np.ndarray) – the vector of the new weights to be used by the policy.

weights_size
¶ Property.
Returns: The size of the policy weights.

_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.

diff
(state, action)¶ Compute the derivative of the probability density function, in the specified state and action pair. Normally it is computed w.r.t. the derivative of the logarithm of the probability density function, exploiting the likelihood ratio trick, i.e.:
\[\nabla_{\theta}p(s,a)=p(s,a)\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the derivative is computed
 action (np.ndarray) – the action where the derivative is computed
Returns: The derivative w.r.t. the policy weights

diff_log
(state, action)¶ Compute the gradient of the logarithm of the probability density function, in the specified state and action pair, i.e.:
\[\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the gradient is computed
 action (np.ndarray) – the action where the gradient is computed
Returns: The gradient of the logarithm of the pdf w.r.t. the policy weights

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
()¶ Useful when the policy needs a special initialization at the beginning of an episode.

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.

Gaussian policy¶

class
AbstractGaussianPolicy
[source]¶ Bases:
mushroom_rl.policy.policy.ParametricPolicy
Abstract class of Gaussian policies.

__call__
(state, action)[source]¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

draw_action
(state)[source]¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

__init__
¶ Initialize self. See help(type(self)) for accurate signature.

_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.

diff
(state, action)¶ Compute the derivative of the probability density function, in the specified state and action pair. Normally it is computed w.r.t. the derivative of the logarithm of the probability density function, exploiting the likelihood ratio trick, i.e.:
\[\nabla_{\theta}p(s,a)=p(s,a)\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the derivative is computed
 action (np.ndarray) – the action where the derivative is computed
Returns: The derivative w.r.t. the policy weights

diff_log
(state, action)¶ Compute the gradient of the logarithm of the probability density function, in the specified state and action pair, i.e.:
\[\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the gradient is computed
 action (np.ndarray) – the action where the gradient is computed
Returns: The gradient of the logarithm of the pdf w.r.t. the policy weights

get_weights
()¶ Getter.
Returns: The current policy weights.

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
()¶ Useful when the policy needs a special initialization at the beginning of an episode.

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.

set_weights
(weights)¶ Setter.
Parameters: weights (np.ndarray) – the vector of the new weights to be used by the policy.

weights_size
¶ Property.
Returns: The size of the policy weights.


class
GaussianPolicy
(mu, sigma)[source]¶ Bases:
mushroom_rl.policy.gaussian_policy.AbstractGaussianPolicy
Gaussian policy. This is a differentiable policy for continuous action spaces. The policy samples an action in every state following a gaussian distribution, where the mean is computed in the state and the covariance matrix is fixed.

__init__
(mu, sigma)[source]¶ Constructor.
Parameters:  mu (Regressor) – the regressor representing the mean w.r.t. the state;
 sigma (np.ndarray) – a square positive definite matrix representing the covariance matrix. The size of this matrix must be n x n, where n is the action dimensionality.

set_sigma
(sigma)[source]¶ Setter.
Parameters: sigma (np.ndarray) – the new covariance matrix. Must be a square positive definite matrix.

diff_log
(state, action)[source]¶ Compute the gradient of the logarithm of the probability density function, in the specified state and action pair, i.e.:
\[\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the gradient is computed
 action (np.ndarray) – the action where the gradient is computed
Returns: The gradient of the logarithm of the pdf w.r.t. the policy weights

set_weights
(weights)[source]¶ Setter.
Parameters: weights (np.ndarray) – the vector of the new weights to be used by the policy.

weights_size
¶ Property.
Returns: The size of the policy weights.

__call__
(state, action)¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

_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.

diff
(state, action)¶ Compute the derivative of the probability density function, in the specified state and action pair. Normally it is computed w.r.t. the derivative of the logarithm of the probability density function, exploiting the likelihood ratio trick, i.e.:
\[\nabla_{\theta}p(s,a)=p(s,a)\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the derivative is computed
 action (np.ndarray) – the action where the derivative is computed
Returns: The derivative w.r.t. the policy weights

draw_action
(state)¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

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
()¶ Useful when the policy needs a special initialization at the beginning of an episode.

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
DiagonalGaussianPolicy
(mu, std)[source]¶ Bases:
mushroom_rl.policy.gaussian_policy.AbstractGaussianPolicy
Gaussian policy with learnable standard deviation. The Covariance matrix is constrained to be a diagonal matrix, where the diagonal is the squared standard deviation vector. This is a differentiable policy for continuous action spaces. This policy is similar to the gaussian policy, but the weights includes also the standard deviation.

__init__
(mu, std)[source]¶ Constructor.
Parameters:  mu (Regressor) – the regressor representing the mean w.r.t. the state;
 std (np.ndarray) – a vector of standard deviations. The length of this vector must be equal to the action dimensionality.

set_std
(std)[source]¶ Setter.
Parameters: std (np.ndarray) – the new standard deviation. Must be a square positive definite matrix.

diff_log
(state, action)[source]¶ Compute the gradient of the logarithm of the probability density function, in the specified state and action pair, i.e.:
\[\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the gradient is computed
 action (np.ndarray) – the action where the gradient is computed
Returns: The gradient of the logarithm of the pdf w.r.t. the policy weights

set_weights
(weights)[source]¶ Setter.
Parameters: weights (np.ndarray) – the vector of the new weights to be used by the policy.

weights_size
¶ Property.
Returns: The size of the policy weights.

__call__
(state, action)¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

_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.

diff
(state, action)¶ Compute the derivative of the probability density function, in the specified state and action pair. Normally it is computed w.r.t. the derivative of the logarithm of the probability density function, exploiting the likelihood ratio trick, i.e.:
\[\nabla_{\theta}p(s,a)=p(s,a)\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the derivative is computed
 action (np.ndarray) – the action where the derivative is computed
Returns: The derivative w.r.t. the policy weights

draw_action
(state)¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

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
()¶ Useful when the policy needs a special initialization at the beginning of an episode.

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
StateStdGaussianPolicy
(mu, std, eps=1e06)[source]¶ Bases:
mushroom_rl.policy.gaussian_policy.AbstractGaussianPolicy
Gaussian policy with learnable standard deviation. The Covariance matrix is constrained to be a diagonal matrix, where the diagonal is the squared standard deviation, which is computed for each state. This is a differentiable policy for continuous action spaces. This policy is similar to the diagonal gaussian policy, but a parametric regressor is used to compute the standard deviation, so the standard deviation depends on the current state.

__init__
(mu, std, eps=1e06)[source]¶ Constructor.
Parameters:  mu (Regressor) – the regressor representing the mean w.r.t. the state;
 std (Regressor) – the regressor representing the standard deviations w.r.t. the state. The output dimensionality of the regressor must be equal to the action dimensionality;
 eps (float, 1e6) – A positive constant added to the variance to ensure that is always greater than zero.

diff_log
(state, action)[source]¶ Compute the gradient of the logarithm of the probability density function, in the specified state and action pair, i.e.:
\[\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the gradient is computed
 action (np.ndarray) – the action where the gradient is computed
Returns: The gradient of the logarithm of the pdf w.r.t. the policy weights

set_weights
(weights)[source]¶ Setter.
Parameters: weights (np.ndarray) – the vector of the new weights to be used by the policy.

weights_size
¶ Property.
Returns: The size of the policy weights.

__call__
(state, action)¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

_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.

diff
(state, action)¶ Compute the derivative of the probability density function, in the specified state and action pair. Normally it is computed w.r.t. the derivative of the logarithm of the probability density function, exploiting the likelihood ratio trick, i.e.:
\[\nabla_{\theta}p(s,a)=p(s,a)\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the derivative is computed
 action (np.ndarray) – the action where the derivative is computed
Returns: The derivative w.r.t. the policy weights

draw_action
(state)¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

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
()¶ Useful when the policy needs a special initialization at the beginning of an episode.

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
StateLogStdGaussianPolicy
(mu, log_std)[source]¶ Bases:
mushroom_rl.policy.gaussian_policy.AbstractGaussianPolicy
Gaussian policy with learnable standard deviation. The Covariance matrix is constrained to be a diagonal matrix, the diagonal is computed by an exponential transformation of the logarithm of the standard deviation computed in each state. This is a differentiable policy for continuous action spaces. This policy is similar to the State std gaussian policy, but here the regressor represents the logarithm of the standard deviation.

diff_log
(state, action)[source]¶ Compute the gradient of the logarithm of the probability density function, in the specified state and action pair, i.e.:
\[\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the gradient is computed
 action (np.ndarray) – the action where the gradient is computed
Returns: The gradient of the logarithm of the pdf w.r.t. the policy weights

set_weights
(weights)[source]¶ Setter.
Parameters: weights (np.ndarray) – the vector of the new weights to be used by the policy.

weights_size
¶ Property.
Returns: The size of the policy weights.

__call__
(state, action)¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

_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.

diff
(state, action)¶ Compute the derivative of the probability density function, in the specified state and action pair. Normally it is computed w.r.t. the derivative of the logarithm of the probability density function, exploiting the likelihood ratio trick, i.e.:
\[\nabla_{\theta}p(s,a)=p(s,a)\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the derivative is computed
 action (np.ndarray) – the action where the derivative is computed
Returns: The derivative w.r.t. the policy weights

draw_action
(state)¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

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
()¶ Useful when the policy needs a special initialization at the beginning of an episode.

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.

Noise policy¶

class
OrnsteinUhlenbeckPolicy
(mu, sigma, theta, dt, x0=None)[source]¶ Bases:
mushroom_rl.policy.policy.ParametricPolicy
OrnsteinUhlenbeck process as implemented in: https://github.com/openai/baselines/blob/master/baselines/ddpg/noise.py.
This policy is commonly used in the Deep Deterministic Policy Gradient algorithm.

__init__
(mu, sigma, theta, dt, x0=None)[source]¶ Constructor.
Parameters:  mu (Regressor) – the regressor representing the mean w.r.t. the state;
 sigma (np.ndarray) – average magnitude of the random flactations per squareroot time;
 theta (float) – rate of mean reversion;
 dt (float) – time interval;
 x0 (np.ndarray, None) – initial values of noise.

__call__
(state, action)[source]¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

draw_action
(state)[source]¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

set_weights
(weights)[source]¶ Setter.
Parameters: weights (np.ndarray) – the vector of the new weights to be used by the policy.

weights_size
¶ Property.
Returns: The size of the policy weights.

reset
()[source]¶ Useful when the policy needs a special initialization at the beginning of an episode.

_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.

diff
(state, action)¶ Compute the derivative of the probability density function, in the specified state and action pair. Normally it is computed w.r.t. the derivative of the logarithm of the probability density function, exploiting the likelihood ratio trick, i.e.:
\[\nabla_{\theta}p(s,a)=p(s,a)\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the derivative is computed
 action (np.ndarray) – the action where the derivative is computed
Returns: The derivative w.r.t. the policy weights

diff_log
(state, action)¶ Compute the gradient of the logarithm of the probability density function, in the specified state and action pair, i.e.:
\[\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the gradient is computed
 action (np.ndarray) – the action where the gradient is computed
Returns: The gradient of the logarithm of the pdf w.r.t. the policy weights

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
ClippedGaussianPolicy
(mu, sigma, low, high)[source]¶ Bases:
mushroom_rl.policy.policy.ParametricPolicy
Clipped Gaussian policy, as used in:
“Addressing Function Approximation Error in ActorCritic Methods”. Fujimoto S. et al.. 2018.
This is a nondifferentiable policy for continuous action spaces. The policy samples an action in every state following a gaussian distribution, where the mean is computed in the state and the covariance matrix is fixed. The action is then clipped using the given action range. This policy is not a truncated Gaussian, as it simply clips the action if the value is bigger than the boundaries. Thus, the nondifferentiability.

__init__
(mu, sigma, low, high)[source]¶ Constructor.
Parameters:  mu (Regressor) – the regressor representing the mean w.r.t. the state;
 sigma (np.ndarray) – a square positive definite matrix representing the covariance matrix. The size of this matrix must be n x n, where n is the action dimensionality;
 low (np.ndarray) – a vector containing the minimum action for each component;
 high (np.ndarray) – a vector containing the maximum action for each component.

__call__
(state, action)[source]¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

draw_action
(state)[source]¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

set_weights
(weights)[source]¶ Setter.
Parameters: weights (np.ndarray) – the vector of the new weights to be used by the policy.

weights_size
¶ Property.
Returns: The size of the policy weights.

_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.

diff
(state, action)¶ Compute the derivative of the probability density function, in the specified state and action pair. Normally it is computed w.r.t. the derivative of the logarithm of the probability density function, exploiting the likelihood ratio trick, i.e.:
\[\nabla_{\theta}p(s,a)=p(s,a)\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the derivative is computed
 action (np.ndarray) – the action where the derivative is computed
Returns: The derivative w.r.t. the policy weights

diff_log
(state, action)¶ Compute the gradient of the logarithm of the probability density function, in the specified state and action pair, i.e.:
\[\nabla_{\theta}\log p(s,a)\]Parameters:  state (np.ndarray) – the state where the gradient is computed
 action (np.ndarray) – the action where the gradient is computed
Returns: The gradient of the logarithm of the pdf w.r.t. the policy weights

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
()¶ Useful when the policy needs a special initialization at the beginning of an episode.

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.

TD policy¶

class
TDPolicy
[source]¶ Bases:
mushroom_rl.policy.policy.Policy

__call__
(*args)¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

_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.

draw_action
(state)¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

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
()¶ Useful when the policy needs a special initialization at the beginning of an episode.

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
EpsGreedy
(epsilon)[source]¶ Bases:
mushroom_rl.policy.td_policy.TDPolicy
Epsilon greedy policy.

__init__
(epsilon)[source]¶ Constructor.
Parameters: epsilon ([float, Parameter]) – the exploration coefficient. It indicates the probability of performing a random actions in the current step.

__call__
(*args)[source]¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

draw_action
(state)[source]¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

set_epsilon
(epsilon)[source]¶ Setter.
Parameters:  epsilon ([float, Parameter]) – the exploration coefficient. It indicates the
 of performing a random actions in the current step. (probability) –

update
(*idx)[source]¶ Update the value of the epsilon parameter at the provided index (e.g. in case of different values of epsilon for each visited state according to the number of visits).
Parameters: *idx (list) – index of the parameter to be updated.

_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_q
()¶ Returns: The approximator used by the policy.

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
()¶ Useful when the policy needs a special initialization at the beginning of an episode.

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.

set_q
(approximator)¶ Parameters: approximator (object) – the approximator to use.


class
Boltzmann
(beta)[source]¶ Bases:
mushroom_rl.policy.td_policy.TDPolicy
Boltzmann softmax policy.

__init__
(beta)[source]¶ Constructor.
Parameters:  beta ([float, Parameter]) – the inverse of the temperature distribution. As
 temperature approaches infinity, the policy becomes more and (the) –
 random. As the temperature approaches 0.0, the policy becomes (more) –
 and more greedy. (more) –

__call__
(*args)[source]¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

draw_action
(state)[source]¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

set_beta
(beta)[source]¶ Setter.
Parameters: beta ((float, Parameter)) – the inverse of the temperature distribution.

update
(*idx)[source]¶ Update the value of the beta parameter at the provided index (e.g. in case of different values of beta for each visited state according to the number of visits).
Parameters: *idx (list) – index of the parameter to be updated.

_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_q
()¶ Returns: The approximator used by the policy.

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
()¶ Useful when the policy needs a special initialization at the beginning of an episode.

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.

set_q
(approximator)¶ Parameters: approximator (object) – the approximator to use.


class
Mellowmax
(omega, beta_min=10.0, beta_max=10.0)[source]¶ Bases:
mushroom_rl.policy.td_policy.Boltzmann
Mellowmax policy. “An Alternative Softmax Operator for Reinforcement Learning”. Asadi K. and Littman M.L.. 2017.

class
MellowmaxParameter
(outer, omega, beta_min, beta_max)[source]¶ Bases:
mushroom_rl.utils.parameters.Parameter

__init__
(outer, omega, beta_min, beta_max)[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 stateaction tuple.

__call__
(state)[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.

_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.

initial_value
¶ The initial value of the parameters.
Type: Returns

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.

shape
¶ The shape of the table of parameters.
Type: Returns

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.


__init__
(omega, beta_min=10.0, beta_max=10.0)[source]¶ Constructor.
Parameters:  omega (Parameter) – the omega parameter of the policy from which beta of the Boltzmann policy is computed;
 beta_min (float, 10.) – one end of the bracketing interval for minimization with Brent’s method;
 beta_max (float, 10.) – the other end of the bracketing interval for minimization with Brent’s method.

set_beta
(beta)[source]¶ Setter.
Parameters: beta ((float, Parameter)) – the inverse of the temperature distribution.

update
(*idx)[source]¶ Update the value of the beta parameter at the provided index (e.g. in case of different values of beta for each visited state according to the number of visits).
Parameters: *idx (list) – index of the parameter to be updated.

__call__
(*args)¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

_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.

draw_action
(state)¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

get_q
()¶ Returns: The approximator used by the policy.

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
()¶ Useful when the policy needs a special initialization at the beginning of an episode.

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.

set_q
(approximator)¶ Parameters: approximator (object) – the approximator to use.

class
Torch policy¶

class
TorchPolicy
(use_cuda)[source]¶ Bases:
mushroom_rl.policy.policy.Policy
Interface for a generic PyTorch policy. A PyTorch policy is a policy implemented as a neural network using PyTorch. Functions ending with ‘_t’ use tensors as input, and also as output when required.

__call__
(state, action)[source]¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

draw_action
(state)[source]¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

distribution
(state)[source]¶ Compute the policy distribution in the given states.
Parameters: state (np.ndarray) – the set of states where the distribution is computed. Returns: The torch distribution for the provided states.

entropy
(state=None)[source]¶ Compute the entropy of the policy.
Parameters: state (np.ndarray, None) – the set of states to consider. If the entropy of the policy can be computed in closed form, then state
can be None.Returns: The value of the entropy of the policy.

draw_action_t
(state)[source]¶ Draw an action given a tensor.
Parameters: state (torch.Tensor) – set of states. Returns: The tensor of the actions to perform in each state.

log_prob_t
(state, action)[source]¶ Compute the logarithm of the probability of taking
action
instate
.Parameters:  state (torch.Tensor) – set of states.
 action (torch.Tensor) – set of actions.
Returns: The tensor of logprobability.

entropy_t
(state)[source]¶ Compute the entropy of the policy.
Parameters: state (torch.Tensor) – the set of states to consider. If the entropy of the policy can be computed in closed form, then state
can be None.Returns: The tensor value of the entropy of the policy.

distribution_t
(state)[source]¶ Compute the policy distribution in the given states.
Parameters: state (torch.Tensor) – the set of states where the distribution is computed. Returns: The torch distribution for the provided states.

set_weights
(weights)[source]¶ Setter.
Parameters: weights (np.ndarray) – the vector of the new weights to be used by the policy.

parameters
()[source]¶ Returns the trainable policy parameters, as expected by torch optimizers.
Returns: List of parameters to be optimized.

reset
()[source]¶ Useful when the policy needs a special initialization at the beginning of an episode.

use_cuda
¶ True if the policy is using cuda_tensors.

_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
GaussianTorchPolicy
(network, input_shape, output_shape, std_0=1.0, use_cuda=False, **params)[source]¶ Bases:
mushroom_rl.policy.torch_policy.TorchPolicy
Torch policy implementing a Gaussian policy with trainable standard deviation. The standard deviation is not statedependent.

__init__
(network, input_shape, output_shape, std_0=1.0, use_cuda=False, **params)[source]¶ Constructor.
Parameters:  network (object) – the network class used to implement the mean regressor;
 input_shape (tuple) – the shape of the state space;
 output_shape (tuple) – the shape of the action space;
 std_0 (float, 1.) – initial standard deviation;
 params (dict) – parameters used by the network constructor.

draw_action_t
(state)[source]¶ Draw an action given a tensor.
Parameters: state (torch.Tensor) – set of states. Returns: The tensor of the actions to perform in each state.

log_prob_t
(state, action)[source]¶ Compute the logarithm of the probability of taking
action
instate
.Parameters:  state (torch.Tensor) – set of states.
 action (torch.Tensor) – set of actions.
Returns: The tensor of logprobability.

entropy_t
(state=None)[source]¶ Compute the entropy of the policy.
Parameters: state (torch.Tensor) – the set of states to consider. If the entropy of the policy can be computed in closed form, then state
can be None.Returns: The tensor value of the entropy of the policy.

distribution_t
(state)[source]¶ Compute the policy distribution in the given states.
Parameters: state (torch.Tensor) – the set of states where the distribution is computed. Returns: The torch distribution for the provided states.

set_weights
(weights)[source]¶ Setter.
Parameters: weights (np.ndarray) – the vector of the new weights to be used by the policy.

parameters
()[source]¶ Returns the trainable policy parameters, as expected by torch optimizers.
Returns: List of parameters to be optimized.

__call__
(state, action)¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

_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.

distribution
(state)¶ Compute the policy distribution in the given states.
Parameters: state (np.ndarray) – the set of states where the distribution is computed. Returns: The torch distribution for the provided states.

draw_action
(state)¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

entropy
(state=None)¶ Compute the entropy of the policy.
Parameters: state (np.ndarray, None) – the set of states to consider. If the entropy of the policy can be computed in closed form, then state
can be None.Returns: The value of the entropy of the policy.

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
()¶ Useful when the policy needs a special initialization at the beginning of an episode.

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.

use_cuda
¶ True if the policy is using cuda_tensors.


class
BoltzmannTorchPolicy
(network, input_shape, output_shape, beta, use_cuda=False, **params)[source]¶ Bases:
mushroom_rl.policy.torch_policy.TorchPolicy
Torch policy implementing a Boltzmann policy.

__init__
(network, input_shape, output_shape, beta, use_cuda=False, **params)[source]¶ Constructor.
Parameters:  network (object) – the network class used to implement the mean regressor;
 input_shape (tuple) – the shape of the state space;
 output_shape (tuple) – the shape of the action space;
 beta ((float, Parameter)) – the inverse of the temperature distribution. As the temperature approaches infinity, the policy becomes more and more random. As the temperature approaches 0.0, the policy becomes more and more greedy.
 params (dict) – parameters used by the network constructor.

draw_action_t
(state)[source]¶ Draw an action given a tensor.
Parameters: state (torch.Tensor) – set of states. Returns: The tensor of the actions to perform in each state.

log_prob_t
(state, action)[source]¶ Compute the logarithm of the probability of taking
action
instate
.Parameters:  state (torch.Tensor) – set of states.
 action (torch.Tensor) – set of actions.
Returns: The tensor of logprobability.

entropy_t
(state)[source]¶ Compute the entropy of the policy.
Parameters: state (torch.Tensor) – the set of states to consider. If the entropy of the policy can be computed in closed form, then state
can be None.Returns: The tensor value of the entropy of the policy.

distribution_t
(state)[source]¶ Compute the policy distribution in the given states.
Parameters: state (torch.Tensor) – the set of states where the distribution is computed. Returns: The torch distribution for the provided states.

set_weights
(weights)[source]¶ Setter.
Parameters: weights (np.ndarray) – the vector of the new weights to be used by the policy.

parameters
()[source]¶ Returns the trainable policy parameters, as expected by torch optimizers.
Returns: List of parameters to be optimized.

__call__
(state, action)¶ Compute the probability of taking action in a certain state following the policy.
Parameters: *args (list) – list containing a state or a state and an action. Returns: The probability of all actions following the policy in the given state if the list contains only the state, else the probability of the given action in the given state following the policy. If the action space is continuous, state and action must be provided

_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.

distribution
(state)¶ Compute the policy distribution in the given states.
Parameters: state (np.ndarray) – the set of states where the distribution is computed. Returns: The torch distribution for the provided states.

draw_action
(state)¶ Sample an action in
state
using the policy.Parameters: state (np.ndarray) – the state where the agent is. Returns: The action sampled from the policy.

entropy
(state=None)¶ Compute the entropy of the policy.
Parameters: state (np.ndarray, None) – the set of states to consider. If the entropy of the policy can be computed in closed form, then state
can be None.Returns: The value of the entropy of the policy.

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
()¶ Useful when the policy needs a special initialization at the beginning of an episode.

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.

use_cuda
¶ True if the policy is using cuda_tensors.
