agent Package

class explauto.agent.agent.Agent(conf, sm_model, im_model, n_bootstrap=0)

Bases: explauto.utils.observer.Observable

classmethod from_classes(im_model_cls, im_model_config, expl_dims, sm_model_cls, sm_model_config, inf_dims, m_mins, m_maxs, s_mins, s_maxs, n_bootstrap=0)

Initialize agent class

Parameters:

• im_model_cls (class) – a subclass of InterestedModel, as those registered in the interest_model package
• im_model_config (dict) – a configuration dict as those registered in the interest_model package
• expl_dims (list) – the sensorimotor dimensions where exploration is driven in the interest model
• inf_dims (list) – the output sensorimotor dimensions of the sensorimotor model (input being expl_dims)
• sm_model_cls (class) – a subclass of SensorimotorModel, as those registered in the sensorimotor_model package
• sensorimotor_model_config (dict) – a configuration dict as those registered in the sensorimotor_model package
• m_mins, m_maxs, s_mins, s_max (list) – lower and upper bounds of motor and sensory values on each dimension

choose()

Returns a point chosen by the interest model

infer(expl_dims, inf_dims, x)

Use the sensorimotor model to compute the expected value on inf_dims given that the value on expl_dims is x.

Note

This corresponds to a prediction if expl_dims=self.conf.m_dims and inf_dims=self.conf.s_dims and to inverse prediction if expl_dims=self.conf.s_dims and inf_dims=self.conf.m_dims.

extract_ms(x, y)

Returns the motor and sensory parts from a point in the exploration space (expl_dims) and a point in the inference space (inf_dims).

motor_primitive(m)

Prepare the movement from a command m. To be overridded in order to generate more complex movement (tutorial to come). This version simply bounds the command.

sensory_primitive(s)

Extract features from a sensory effect s. To be overridded in order to process more complex feature extraction (tutorial to come). This version simply bounds the sensory effect.

produce()

Exploration (see the Explauto introduction for more detail):

• Choose a value x on expl_dims according to the interest model
• Infer a value y on inf_dims from x using the infer() method
• Extract the motor m and sensory s parts of x, y
• generate a movement from m

Returns:the generated movement

Note

This correspond to motor babbling if expl_dims=self.conf.m_dims and inf_dims=self.conf.s_dims and to goal babbling if expl_dims=self.conf.s_dims and inf_dims=self.conf.m_dims.

perceive(s_)

Learning (see the Explauto introduction for more detail):

• update the sensorimotor model with (m, s)
• update the interest model with (x, y, m, s) (x, y are stored in self.ms in production())