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)	Method called by any world when it starts.
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)	Method called by any world when it starts.
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)	Method called by any world when it starts.
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