matrx.agents.agent_types.human_agent.HumanAgentBrain

class HumanAgentBrain(memorize_for_ticks=None, fov_occlusion=False, max_carry_objects=3, grab_range=1, drop_range=1, door_range=1, remove_range=1)

Creates an Human Agent which is an agent that can be controlled by a human.

Attributes
memorize_for_ticks
state

Methods

create_context_menu_for_other(self, …)

Generate options for a context menu for a specific object/location that a user NOT controlling this human agent opened.

create_context_menu_for_self(self, …)

Generate options for a context menu for a specific object/location which the user controlling this human agent opened.

decide_on_action(self, state, user_input)

Contains the decision logic of the agent.

filter_observations(self, state)

All our agent work through the OODA-loop paradigm; first you observe, then you orient/pre-process, followed by a decision process of an action after which we act upon the action.

filter_user_input(self, user_input)

From the received userinput, only keep those which are actually connected to a specific agent action.

get_log_data(self)

Provides a dictionary of data for any Logger

initialize(self)

To initialize an agent’s brain.

is_action_possible(self, action, action_kwargs)

Checks if an action would be possible.

send_message(self, message)

Sends a Message from this agent to others

Creates an Human Agent which is an agent that can be controlled by a human.

Attributes
memorize_for_ticks
state

Methods

create_context_menu_for_other(self, …)

Generate options for a context menu for a specific object/location that a user NOT controlling this human agent opened.

create_context_menu_for_self(self, …)

Generate options for a context menu for a specific object/location which the user controlling this human agent opened.

decide_on_action(self, state, user_input)

Contains the decision logic of the agent.

filter_observations(self, state)

All our agent work through the OODA-loop paradigm; first you observe, then you orient/pre-process, followed by a decision process of an action after which we act upon the action.

filter_user_input(self, user_input)

From the received userinput, only keep those which are actually connected to a specific agent action.

get_log_data(self)

Provides a dictionary of data for any Logger

initialize(self)

To initialize an agent’s brain.

is_action_possible(self, action, action_kwargs)

Checks if an action would be possible.

send_message(self, message)

Sends a Message from this agent to others

__init__(self, memorize_for_ticks=None, fov_occlusion=False, max_carry_objects=3, grab_range=1, drop_range=1, door_range=1, remove_range=1)

Creates an Human Agent which is an agent that can be controlled by a human.

Methods

__init__(self[, memorize_for_ticks, …])

Creates an Human Agent which is an agent that can be controlled by a human.

create_context_menu_for_other(self, …)

Generate options for a context menu for a specific object/location that a user NOT controlling this human agent opened.

create_context_menu_for_self(self, …)

Generate options for a context menu for a specific object/location which the user controlling this human agent opened.

decide_on_action(self, state, user_input)

Contains the decision logic of the agent.

filter_observations(self, state)

All our agent work through the OODA-loop paradigm; first you observe, then you orient/pre-process, followed by a decision process of an action after which we act upon the action.

filter_user_input(self, user_input)

From the received userinput, only keep those which are actually connected to a specific agent action.

get_log_data(self)

Provides a dictionary of data for any Logger

initialize(self)

To initialize an agent’s brain.

is_action_possible(self, action, action_kwargs)

Checks if an action would be possible.

send_message(self, message)

Sends a Message from this agent to others

Attributes

memorize_for_ticks

state