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