class Brain(object):
"""
A biologically motivated learning algorithm.
Becca's Brain contains all of its learning algorithms,
integrated into a single whole.
Check out connector.py for an example for how to attach a world
to a brain.
"""
def __init__(self, world, config=None):
"""
Configure the Brain.
There are some superficial parameters that individual worlds
might like to choose, like how often to visualize and
how often to back things up. These can be changed by passing
the appropriate key-value pairs in a dictionary.
Parameters
----------
world: World
An environment with an appropriate step() function.
config: dict
Keys are brain parameters, values are desired values.
Configuration parameters
------------------------
backup_interval: int
How often the brain will save a pickle backup of itself,
in timesteps.
debug: boolean
Print informative error messages?
log_directory : str
The full path name to a directory where information and
backups for the world can be stored and retrieved.
n_features: int
The limit on the number of features passed to the model.
If this is smaller, Becca will run faster. If it is larger
Becca will have more capacity to learn. It's an important
input for determining performance.
name: str
A descriptive string identifying the brain.
reporting_interval: int
How often the brain will report on performance.
restore : bool, optional
If restore is True, try to restore the brain
from a previously saved
version, picking up where it left off.
Otherwise it create a new one.
visualize_interval: int
The number of time steps between creating a new performance
calculation and visualization of the brain.
"""
defaults = {
"backup_interval": 1e5,
"debug": True,
"log_directory": None,
"n_features": None,
"name": None,
"reporting_interval": 1e3,
"restore": True,
"visualize_interval": 1e4,
}
if config is None:
config = {}
if config.get("name") is not None:
self.name = config.get("name")
else:
self.name = '{0}_brain'.format(world.name)
if config.get("debug") is not None:
self.debug = config.get("debug")
else:
self.debug = defaults.get("debug")
if config.get("log_directory") is not None:
self.log_dir = config.get("log_directory")
else:
# Identify the full local path of the brain.py module.
# This trick is used to conveniently locate
# other Becca resources.
module_path = os.path.dirname(os.path.abspath(__file__))
# log_dir : str
# Relative path to the log directory.
# This is where backups
# and images of the brain's state and performance are kept.
self.log_dir = os.path.normpath(
os.path.join(module_path, 'log'))
# Check whether the directory is already there. If not, create it.
if not os.path.isdir(self.log_dir):
os.makedirs(self.log_dir)
# pickle_filename : str
# Relative path and filename of the backup pickle file.
self.pickle_filename = os.path.join(
self.log_dir, '{0}.pickle'.format(self.name))
# One of the few constraints on the world is that it has to have
# n_actions and n_sensors members.
# n_actions: int
# This is the total number of action outputs that
# the world is expecting.
# n_sensors: int
# The number of distinct sensors that the world
# will be passing in to the brain.
self.n_actions = world.n_actions
self.n_sensors = world.n_sensors
self.timestep = 0
if config.get("restore") is not None:
restore_flag = config.get("restore")
else:
restore_flag = defaults.get("restore")
if restore_flag:
restored_brain = restore(self)
if restore_flag and restored_brain is not None:
self.timestep = restored_brain.timestep
self.input_activities = restored_brain.input_activities
self.actions = restored_brain.actions
self.n_features = restored_brain.n_features
self.postprocessor = restored_brain.postprocessor
self.n_commands = restored_brain.n_commands
self.commands = restored_brain.commands
self.preprocessor = restored_brain.preprocessor
self.affect = restored_brain.affect
self.satisfaction = restored_brain.satisfaction
self.featurizer = restored_brain.featurizer
self.model = restored_brain.model
self.actor = restored_brain.actor
else:
# Initialize everything.
# The preprocessor takes raw sensors and commands and converts
# them into discrete inputs.
# Assume all actions are in a continuous space.
# This means that it can be repeatedly subdivided to
# generate actions of various magnitudes and increase control.
self.preprocessor = Preprocessor(n_sensors=self.n_sensors)
# The postprocessor converts actions to discretized actions
# and back.
self.postprocessor = Postprocessor(n_actions=self.n_actions)
# actions: array of floats
# The set of actions to execute this time step.
# Initializing them to non-zero helps to kick start the
# act-sense-decide loop.
self.actions = np.ones(self.n_actions) * .1
self.affect = Affect()
# satisfaction: float
# The level of contentment experienced by the brain.
# Higher contentment dampens curiosity and
# the drive to explore.
self.satisfaction = 0.
# n_commands: array of floats
# commands are discretized actions, suitable
# for use within becca. The postprocessor
# translates commands into actions.
self.n_commands = self.postprocessor.n_commands
self.commands = np.zeros(self.n_commands)
if config.get("n_features") is not None:
self.n_features = config.get("n_features")
else:
self.n_features = (2 * self.n_commands
+ 8 * self.n_sensors)
self.input_activities = np.zeros(self.n_features)
# The featurizer is an unsupervised learner that learns
# features from the inputs.
self.featurizer = Featurizer(
debug=self.debug,
n_inputs=self.n_features,
)
# The model builds sequences of features and goals and reward
# for making predictions about its world.
self.model = Model(
brain=self,
debug=self.debug,
n_features=self.n_features,
)
# The actor takes conditional predictions from the model and
# uses them to choose new goals.
self.actor = Actor(self.n_features, self)
# Finish with the superficial configuration.
# This might change from session to session.
if config.get("backup_interval") is not None:
self.backup_interval = config.get("backup_interval")
else:
self.backup_interval = defaults.get("backup_interval")
if config.get("reporting_interval") is not None:
self.reporting_interval = config.get("reporting_interval")
else:
self.reporting_interval = defaults.get("reporting_interval")
if config.get("visualize_interval") is not None:
self.visualize_interval = config.get("visualize_interval")
else:
self.visualize_interval = defaults.get("visualize_interval")
return
def sense_act_learn(self, sensors, reward):
"""
Take sensor and reward data in and use them to choose an action.
Parameters
----------
sensors : array of floats
The information coming from the sensors in the world.
The array should have self.n_sensors inputs.
Whatever the low and high value of each sensor, its value
will be rescaled to fall between 0 and 1.
Sensor values are interpreted as fuzzy binary
values, rather than continuous values. For instance,
the brain doesn't interpret a contact sensor value of .5
to mean that the contact
sensor was only weakly contacted. It interprets it
to mean that the sensor was fully contacted for
50% of the sensing
duration or that there is a 50% chance that the sensor was
fully contacted during the entire sensing duration. For another
example, a light sensor reading of zero won't be
interpreted as by the brain as darkness. It will just be
interpreted as a lack of information about the lightness.
reward : float
The extent to which the brain is being rewarded by the
world. It is expected to be between -1 and 1, inclusive.
-1 is the worst pain ever. 1 is the most intense ecstasy
imaginable. 0 is neutral.
Returns
-------
actions : array of floats
The action commands that the brain is sending to the world
to be executed. The array should have self.n_actions
inputs in it. Each value should be binary: 0 and 1. This
allows the brain to learn most effectively how to interact
with the world to obtain more reward.
"""
self.timestep += 1
# Calculate the "mood" of the agent.
self.satisfaction = self.affect.update(reward)
# Calculate new activities in a bottom-up pass.
input_activities = self.preprocessor.convert_to_inputs(sensors)
feature_activities = self.featurizer.featurize(
np.concatenate((self.postprocessor.consolidated_commands,
input_activities)))
(model_feature_activities,
conditional_predictions,
conditional_rewards,
conditional_curiosities
) = self.model.step(feature_activities, reward)
feature_goals, i_goal = self.actor.choose(
feature_activities=model_feature_activities,
conditional_predictions=conditional_predictions,
conditional_rewards=conditional_rewards,
conditional_curiosities=conditional_curiosities,
)
feature_pool_goals = self.model.update_goals(
feature_goals, i_goal)
debug_local = False
if debug_local:
rep = "Brain"
rep += " last action: " + str(self.actions[0]) + ", "
rep += " reward of " + str(reward) + ", "
rep += " next sensors " + str(sensors)
print(rep)
# Pass goals back down.
input_goals = self.featurizer.defeaturize(feature_pool_goals)
# Isolate the actions from the rest of the goals.
self.actions = (self.postprocessor.convert_to_actions(
input_goals[:self.n_commands]))
# Update the inputs in a pair of top-down/bottom-up passes.
# Top-down
candidate_fitness = self.model.calculate_fitness()
self.featurizer.calculate_fitness(candidate_fitness)
# Bottom-up
candidate_resets = self.featurizer.update_inputs()
feature_resets = self.model.update_inputs(candidate_resets)
self.actor.reset(feature_resets)
# Create a set of random actions.
# This is occasionally helpful when debugging.
take_random_actions = False
if take_random_actions:
self.actions = self.random_actions()
# Periodically back up the brain.
if (self.timestep % self.backup_interval) == 0:
self.backup()
# Report on performance.
if self.timestep % self.reporting_interval == 0:
self.affect.visualize(self)
# Create visualization.
if self.timestep % self.visualize_interval == 0:
viz.visualize(self)
return self.actions
def random_actions(self):
"""
Generate a random set of actions.
This is used for debugging. Running a world with random
actions gives a baseline performance floor on a world.
Returns
-------
actions : array of floats
See sense_act_learn.actions.
"""
threshold = .1 / float(self.n_actions)
action_strength = np.random.random_sample(self.n_actions)
actions = np.zeros(self.n_actions)
actions[np.where(action_strength < threshold)] = 1.
return actions
def report_performance(self):
"""
Make a report of how the brain did over its lifetime.
Returns
-------
performance : float
The average reward per time step collected by
the brain over its lifetime.
"""
performance = self.affect.visualize(self)
return performance
def backup(self):
"""
Archive a copy of the brain object for future use.
Returns
-------
success : bool
If the backup process completed without any problems, success
is True, otherwise it is False.
"""
success = False
try:
with open(self.pickle_filename, 'wb') as brain_data:
pickle.dump(self, brain_data)
# Save a second copy. If you only save one, and the user
# happens to ^C out of the program while it is being saved,
# the file becomes corrupted, and all the learning that the
# brain did is lost.
make_second_backup = True
if make_second_backup:
with open('{0}.bak'.format(self.pickle_filename),
'wb') as brain_data_bak:
pickle.dump(self, brain_data_bak)
except IOError as err:
print('File error: {0} encountered while saving brain data'.
format(err))
except pickle.PickleError as perr:
print('Pickling error: {0} encountered while saving brain data'.
format(perr))
except Exception as err:
print('Unknown error: {0} encountered while saving brain data'
.format(err))
else:
success = True
return success
class Brain(object):
"""
A biologically motivated learning algorithm.
Becca's Brain contains all of its learning algorithms,
integrated into a single whole.
Check out connector.py for an example for how to attach a world
to a brain.
"""
def __init__(self, world, config=None):
"""
Configure the Brain.
There are some superficial parameters that individual worlds
might like to choose, like how often to visualize and
how often to back things up. These can be changed by passing
the appropriate key-value pairs in a dictionary.
Parameters
----------
world: World
An environment with an appropriate step() function.
config: dict
Keys are brain parameters, values are desired values.
Configuration parameters
------------------------
backup_interval: int
How often the brain will save a pickle backup of itself,
in timesteps.
debug: boolean
Print informative error messages?
log_directory : str
The full path name to a directory where information and
backups for the world can be stored and retrieved.
n_features: int
The limit on the number of features passed to the model.
If this is smaller, Becca will run faster. If it is larger
Becca will have more capacity to learn. It's an important
input for determining performance.
name: str
A descriptive string identifying the brain.
reporting_interval: int
How often the brain will report on performance.
restore : bool, optional
If restore is True, try to restore the brain
from a previously saved
version, picking up where it left off.
Otherwise it create a new one.
visualize_interval: int
The number of time steps between creating a new performance
calculation and visualization of the brain.
"""
defaults = {
"backup_interval": 1e5,
"debug": True,
"log_directory": None,
"n_features": None,
"name": None,
"reporting_interval": 1e3,
"restore": True,
"visualize_interval": 1e4,
}
if config is None:
config = {}
if config.get("name") is not None:
self.name = config.get("name")
else:
self.name = '{0}_brain'.format(world.name)
if config.get("debug") is not None:
self.debug = config.get("debug")
else:
self.debug = defaults.get("debug")
if config.get("log_directory") is not None:
self.log_dir = config.get("log_directory")
else:
# Identify the full local path of the brain.py module.
# This trick is used to conveniently locate
# other Becca resources.
module_path = os.path.dirname(os.path.abspath(__file__))
# log_dir : str
# Relative path to the log directory.
# This is where backups
# and images of the brain's state and performance are kept.
self.log_dir = os.path.normpath(
os.path.join(module_path, 'log'))
# Check whether the directory is already there. If not, create it.
if not os.path.isdir(self.log_dir):
os.makedirs(self.log_dir)
# pickle_filename : str
# Relative path and filename of the backup pickle file.
self.pickle_filename = os.path.join(
self.log_dir, '{0}.pickle'.format(self.name))
# One of the few constraints on the world is that it has to have
# n_actions and n_sensors members.
# n_actions: int
# This is the total number of action outputs that
# the world is expecting.
# n_sensors: int
# The number of distinct sensors that the world
# will be passing in to the brain.
self.n_actions = world.n_actions
self.n_sensors = world.n_sensors
self.timestep = 0
if config.get("restore") is not None:
restore_flag = config.get("restore")
else:
restore_flag = defaults.get("restore")
if restore_flag:
restored_brain = restore(self)
if restore_flag and restored_brain is not None:
self.timestep = restored_brain.timestep
self.input_activities = restored_brain.input_activities
self.actions = restored_brain.actions
self.n_features = restored_brain.n_features
self.postprocessor = restored_brain.postprocessor
self.n_commands = restored_brain.n_commands
self.commands = restored_brain.commands
self.preprocessor = restored_brain.preprocessor
self.affect = restored_brain.affect
self.satisfaction = restored_brain.satisfaction
self.featurizer = restored_brain.featurizer
self.model = restored_brain.model
self.actor = restored_brain.actor
else:
# Initialize everything.
# The preprocessor takes raw sensors and commands and converts
# them into discrete inputs.
# Assume all actions are in a continuous space.
# This means that it can be repeatedly subdivided to
# generate actions of various magnitudes and increase control.
self.preprocessor = Preprocessor(n_sensors=self.n_sensors)
# The postprocessor converts actions to discretized actions
# and back.
self.postprocessor = Postprocessor(n_actions=self.n_actions)
# actions: array of floats
# The set of actions to execute this time step.
# Initializing them to non-zero helps to kick start the
# act-sense-decide loop.
self.actions = np.ones(self.n_actions) * .1
self.affect = Affect()
# satisfaction: float
# The level of contentment experienced by the brain.
# Higher contentment dampens curiosity and
# the drive to explore.
self.satisfaction = 0
# n_commands: array of floats
# commands are discretized actions, suitable
# for use within becca. The postprocessor
# translates commands into actions.
self.n_commands = self.postprocessor.n_commands
self.commands = np.zeros(self.n_commands)
if config.get("n_features") is not None:
self.n_features = config.get("n_features")
else:
self.n_features = (2 * self.n_commands
+ 8 * self.n_sensors)
self.input_activities = np.zeros(self.n_features)
# The featurizer is an unsupervised learner that learns
# features from the inputs.
self.featurizer = Featurizer(
debug=self.debug,
n_inputs=self.n_features,
)
# The model builds sequences of features and goals and reward
# for making predictions about its world.
self.model = Model(
brain=self,
debug=self.debug,
n_features=self.n_features,
)
# The actor takes conditional predictions from the model and
# uses them to choose new goals.
self.actor = Actor(self.n_features, self)
# Finish with the superficial configuration.
# This might change from session to session.
if config.get("backup_interval") is not None:
self.backup_interval = config.get("backup_interval")
else:
self.backup_interval = defaults.get("backup_interval")
if config.get("reporting_interval") is not None:
self.reporting_interval = config.get("reporting_interval")
else:
self.reporting_interval = defaults.get("reporting_interval")
if config.get("visualize_interval") is not None:
self.visualize_interval = config.get("visualize_interval")
else:
self.visualize_interval = defaults.get("visualize_interval")
return
def sense_act_learn(self, sensors, reward):
"""
Take sensor and reward data in and use them to choose an action.
Parameters
----------
sensors : array of floats
The information coming from the sensors in the world.
The array should have self.n_sensors inputs.
Whatever the low and high value of each sensor, its value
will be rescaled to fall between 0 and 1.
Sensor values are interpreted as fuzzy binary
values, rather than continuous values. For instance,
the brain doesn't interpret a contact sensor value of .5
to mean that the contact
sensor was only weakly contacted. It interprets it
to mean that the sensor was fully contacted for
50% of the sensing
duration or that there is a 50% chance that the sensor was
fully contacted during the entire sensing duration. For another
example, a light sensor reading of zero won't be
interpreted as by the brain as darkness. It will just be
interpreted as a lack of information about the lightness.
reward : float
The extent to which the brain is being rewarded by the
world. It is expected to be between -1 and 1, inclusive.
-1 is the worst pain ever. 1 is the most intense ecstasy
imaginable. 0 is neutral.
Returns
-------
actions : array of floats
The action commands that the brain is sending to the world
to be executed. The array should have self.n_actions
inputs in it. Each value should be binary: 0 and 1. This
allows the brain to learn most effectively how to interact
with the world to obtain more reward.
"""
self.timestep += 1
# Calculate the "mood" of the agent.
self.satisfaction = self.affect.update(reward)
# Calculate new activities in a bottom-up pass.
input_activities = self.preprocessor.convert_to_inputs(sensors)
feature_activities = self.featurizer.featurize(
np.concatenate((self.postprocessor.consolidated_commands,
input_activities)))
(model_feature_activities,
conditional_predictions,
conditional_rewards,
conditional_curiosities
) = self.model.step(feature_activities, reward)
feature_goals, i_goal = self.actor.choose(
feature_activities=model_feature_activities,
conditional_predictions=conditional_predictions,
conditional_rewards=conditional_rewards,
conditional_curiosities=conditional_curiosities,
)
feature_pool_goals = self.model.update_goals(
feature_goals, i_goal)
debug_local = False
if debug_local:
rep = "Brain"
rep += " last action: " + str(self.actions[0]) + ", "
rep += " reward of " + str(reward) + ", "
rep += " next sensors " + str(sensors)
logger.debug(rep)
# Pass goals back down.
input_goals = self.featurizer.defeaturize(feature_pool_goals)
# Isolate the actions from the rest of the goals.
self.actions = (self.postprocessor.convert_to_actions(
input_goals[:self.n_commands]))
# Update the inputs in a pair of top-down/bottom-up passes.
# Top-down
candidate_fitness = self.model.calculate_fitness()
self.featurizer.calculate_fitness(candidate_fitness)
# Bottom-up
candidate_resets = self.featurizer.update_inputs()
feature_resets = self.model.update_inputs(candidate_resets)
self.actor.reset(feature_resets)
# Create a set of random actions.
# This is occasionally helpful when debugging.
take_random_actions = False
if take_random_actions:
self.actions = self.random_actions()
# Periodically back up the brain.
if (self.timestep % self.backup_interval) == 0:
self.backup()
# Report on performance.
if self.timestep % self.reporting_interval == 0:
self.affect.visualize(self)
# Create visualization.
if self.timestep % self.visualize_interval == 0:
viz.visualize(self)
return self.actions
def random_actions(self):
"""
Generate a random set of actions.
This is used for debugging. Running a world with random
actions gives a baseline performance floor on a world.
Returns
-------
actions : array of floats
See sense_act_learn.actions.
"""
threshold = .1 / float(self.n_actions)
action_strength = np.random.random_sample(self.n_actions)
actions = np.zeros(self.n_actions)
actions[np.where(action_strength < threshold)] = 1
return actions
def report_performance(self):
"""
Make a report of how the brain did over its lifetime.
Returns
-------
performance : float
The average reward per time step collected by
the brain over its lifetime.
"""
performance = self.affect.visualize(self)
return performance
def backup(self):
"""
Archive a copy of the brain object for future use.
Returns
-------
success : bool
If the backup process completed without any problems, success
is True, otherwise it is False.
"""
success = False
try:
with open(self.pickle_filename, 'wb') as brain_data:
pickle.dump(self, brain_data)
# Save a second copy. If you only save one, and the user
# happens to ^C out of the program while it is being saved,
# the file becomes corrupted, and all the learning that the
# brain did is lost.
make_second_backup = True
if make_second_backup:
with open('{0}.bak'.format(self.pickle_filename),
'wb') as brain_data_bak:
pickle.dump(self, brain_data_bak)
except IOError as err:
logger.error('File error: {0} encountered while saving brain data'.
format(err))
except pickle.PickleError as perr:
logger.error('Pickling error: {0} encountered while saving brain data'.
format(perr))
except Exception as err:
logger.error('Unknown error: {0} encountered while saving brain data'
.format(err))
else:
success = True
return success
class Brain(object):
"""
A biologically motivated learning algorithm.
Becca's Brain contains all of its learning algorithms,
integrated into a single whole.
Check out connector.py for an example for how to attach a world
to a brain.
"""
def __init__(
self,
backup_interval=int(2**20),
brain_name='test_brain',
debug=True,
log_directory=None,
n_actions=4,
n_features=64,
n_sensors=4,
timestep=0,
visualize_interval=int(2**18),
):
"""
Configure the Brain.
Parameters
----------
backup_interval: int
How often the brain will save a pickle backup of itself,
in timesteps.
brain_name: str
A descriptive string identifying the brain.
debug: boolean
Print informative error messages?
log_directory : str
The full path name to a directory where information and
backups for the world can be stored and retrieved.
n_actions: int
This is the total number of action outputs that
the world is expecting.
n_sensors: int
The number of distinct sensors that the world will be passing in
to the brain.
n_features: int
The limit on the number of features passed to the model.
If this is smaller, Becca will run faster. If it is larger
Becca will have more capacity to learn. It's an important
input for determining performance.
timestep: int
The age of the brain in discrete time steps.
visualize_interval: int
How often to visualize the world, in time steps.
"""
self.debug = debug
self.n_sensors = n_sensors
self.n_actions = n_actions
self.n_features = np.maximum(
n_features, self.n_actions + 4 * self.n_sensors)
self.input_activities = np.zeros(self.n_features)
# actions: array of floats
# The set of actions to execute this time step.
# Initializing them to non-zero helps to kick start the
# act-sense-decide loop.
self.actions = np.ones(self.n_actions) * .1
# The postprocessor converts actions to discretized actions
# and back.
self.postprocessor = Postprocessor(n_actions=self.n_actions)
# n_commands: array of floats
# commands are discretized actions, suitable for use within
# becca. The postprocessor translates commands into actions.
self.n_commands = self.postprocessor.n_commands
# previous_commands: array of floats
# The discretized actions executed on the previous time step.
self.previous_commands = np.zeros(self.n_commands)
self.commands = np.zeros(self.n_commands)
# The preprocessor takes raw sensors and commands and converts
# them into discrete inputs.
# Assume all actions are in a continuous space.
# This means that it can be repeatedly subdivided to
# generate actions of various magnitudes and increase control.
self.preprocessor = Preprocessor(
n_commands=self.n_commands,
n_sensors=self.n_sensors,
)
self.affect = Affect()
# satisfaction: float
# The level of contentment experienced by the brain.
# Higher contentment dampens curiosity and the drive to explore.
self.satisfaction = 0.
# The featurizer is an unsupervised learner that learns
# features from the inputs.
self.featurizer = Featurizer(
debug=self.debug,
n_inputs=self.n_features,
threshold=1e3,
)
# The model builds sequences of features and goals and reward
# for making predictions about its world.
self.model = Model(
brain=self,
debug=self.debug,
n_features=self.n_features,
)
# The actor takes conditional predictions from the model and
# uses them to choose new goals.
self.actor = Actor(self.n_features, self)
self.timestep = timestep
self.visualize_interval = visualize_interval
self.backup_interval = backup_interval
self.name = brain_name
if log_directory:
self.log_dir = log_directory
else:
# Identify the full local path of the brain.py module.
# This trick is used to conveniently locate other Becca resources.
module_path = os.path.dirname(os.path.abspath(__file__))
# log_dir : str
# Relative path to the log directory. This is where backups
# and images of the brain's state and performance are kept.
self.log_dir = os.path.normpath(os.path.join(module_path, 'log'))
# Check whether the directory is already there. If not, create it.
if not os.path.isdir(self.log_dir):
os.makedirs(self.log_dir)
# pickle_filename : str
# Relative path and filename of the backup pickle file.
self.pickle_filename = os.path.join(
self.log_dir, '{0}.pickle'.format(brain_name))
def sense_act_learn(self, sensors, reward):
"""
Take sensor and reward data in and use them to choose an action.
Parameters
----------
sensors : array of floats
The information coming from the sensors in the world.
The array should have self.n_sensors inputs.
Whatever the low and high value of each sensor, its value
will be rescaled to fall between 0 and 1.
Sensor values are interpreted as fuzzy binary
values, rather than continuous values. For instance,
the brain doesn't interpret a contact sensor value of .5
to mean that the contact
sensor was only weakly contacted. It interprets it
to mean that the sensor was fully contacted for 50% of the sensing
duration or that there is a 50% chance that the sensor was
fully contacted during the entire sensing duration. For another
example, a light sensor reading of zero won't be
interpreted as by the brain as darkness. It will just be
interpreted as a lack of information about the lightness.
reward : float
The extent to which the brain is being rewarded by the
world. It is expected to be between -1 and 1, inclusive.
-1 is the worst pain ever. 1 is the most intense ecstasy
imaginable. 0 is neutral.
Returns
-------
actions : array of floats
The action commands that the brain is sending to the world
to be executed. The array should have self.n_actions
inputs in it. Each value should be binary: 0 and 1. This
allows the brain to learn most effectively how to interact
with the world to obtain more reward.
"""
self.timestep += 1
# Calculate the "mood" of the agent.
self.satisfaction = self.affect.update(reward)
# Calculate new activities in a bottom-up pass.
input_activities = self.preprocessor.convert_to_inputs(
self.previous_commands, sensors)
feature_activities = self.featurizer.featurize(input_activities)
(conditional_predictions,
conditional_rewards,
conditional_curiosities
) = self.model.step(feature_activities, reward)
feature_goals, i_goal = self.actor.choose(
conditional_predictions=conditional_predictions,
conditional_rewards=conditional_rewards,
conditional_curiosities=conditional_curiosities,
)
feature_pool_goals = self.model.update_goals(feature_goals, i_goal)
# Pass goals back down.
input_goals = self.featurizer.defeaturize(feature_pool_goals)
# Isolate the actions from the rest of the goals.
# self.previous_actions = self.actions
# self.actions = input_goals[:self.n_actions]
self.previous_commands, self.actions = (
self.postprocessor.convert_to_actions(
input_goals[:self.n_commands]))
# Update the inputs in a pair of top-down/bottom-up passes.
# Top-down
candidate_fitness = self.model.calculate_fitness()
self.featurizer.calculate_fitness(candidate_fitness)
# Bottom-up
candidate_resets = self.featurizer.update_inputs()
feature_resets = self.model.update_inputs(candidate_resets)
self.actor.reset(feature_resets)
# Create a set of random actions.
# This is occasionally helpful when debugging.
take_random_actions = False
if take_random_actions:
self.actions = self.random_actions()
# Periodically back up the brain.
if (self.timestep % self.backup_interval) == 0:
self.backup()
return self.actions
def random_actions(self):
"""
Generate a random set of actions.
This is used for debugging. Running a world with random
actions gives a baseline performance floor on a world.
Returns
-------
actions : array of floats
See sense_act_learn.actions.
"""
threshold = .1 / float(self.n_actions)
action_strength = np.random.random_sample(self.n_actions)
actions = np.zeros(self.n_actions)
actions[np.where(action_strength < threshold)] = 1.
return actions
def report_performance(self):
"""
Make a report of how the brain did over its lifetime.
Returns
-------
performance : float
The average reward per time step collected by
the brain over its lifetime.
"""
performance = self.affect.visualize(self)
return performance
def backup(self):
"""
Archive a copy of the brain object for future use.
Returns
-------
success : bool
If the backup process completed without any problems, success
is True, otherwise it is False.
"""
success = False
try:
with open(self.pickle_filename, 'wb') as brain_data:
pickle.dump(self, brain_data)
# Save a second copy. If you only save one, and the user
# happens to ^C out of the program while it is being saved,
# the file becomes corrupted, and all the learning that the
# brain did is lost.
make_second_backup = True
if make_second_backup:
with open('{0}.bak'.format(self.pickle_filename),
'wb') as brain_data_bak:
pickle.dump(self, brain_data_bak)
except IOError as err:
print('File error: {0} encountered while saving brain data'.
format(err))
except pickle.PickleError as perr:
print('Pickling error: {0} encountered while saving brain data'.
format(perr))
except Exception as err:
print('Unknown error: {0} encountered while saving brain data'.
format(err))
else:
success = True
return success
def restore(self):
"""
Reconstitute the brain from a previously saved brain.
Returns
-------
restored_brain : Brain
If restoration was successful, the saved brain is returned.
Otherwise a notification prints and a new brain is returned.
"""
restored_brain = self
try:
with open(self.pickle_filename, 'rb') as brain_data:
loaded_brain = pickle.load(brain_data)
# Compare the number of channels in the restored brain with
# those in the already initialized brain. If it matches,
# accept the brain. If it doesn't,
# print a message, and keep the just-initialized brain.
# Sometimes the pickle file is corrputed. When this is the case
# you can manually overwrite it by removing the .bak from the
# .pickle.bak file. Then you can restore from the backup pickle.
if ((loaded_brain.n_sensors == self.n_sensors) and
(loaded_brain.n_actions == self.n_actions)):
print('Brain restored at timestep {0} from {1}'.format(
str(loaded_brain.timestep), self.pickle_filename))
restored_brain = loaded_brain
else:
print('The brain {0} does not have the same number'.format(
self.pickle_filename))
print('of sensors and actions as the world.')
print('Creating a new brain from scratch.')
except IOError:
print('Couldn\'t open {0} for loading'.format(self.pickle_filename))
except pickle.PickleError:
print('Error unpickling world')
return restored_brain
def visualize(self):
"""
Show the current state and some history of the brain.
This is typically called from a world's visualize method.
"""
print(' ')
print('{0} is {1} time steps old'.format(self.name, self.timestep))
viz.brain_activity(self)
self.affect.visualize(self)