cambrian.agents.agent

Defines agent classes.

Classes

MjCambrianAgentConfig	Defines the config for an agent. Used for type hinting.
MjCambrianAgent	The agent class is defined as a physics object with eyes.
MjCambrianAgent2D	Assumes the agent is moving on 2D plane and has a yaw hinge joint which is used

Module Contents

class MjCambrianAgentConfig

Bases: hydra_config.HydraContainerConfig

Defines the config for an agent. Used for type hinting.

Variables:

• instance (Callable[[Self, str, int], "MjCambrianAgent"]) – The class instance for the agent. This is used to create the agent. Takes the config, the name, and the index of the agent as arguments.

• trainable (bool) – Whether the agent is trainable or not. If the agent is trainable, it’s observations will be included in the observation space of the environment and the model’s output actions will be applied to the agent. If the agent is not trainable, the agent’s policy can be defined by overriding the get_action_privileged method.

• use_privileged_action (bool) – This is similar to trainable, but the agent’s action and observation spaces is included in the environment’s action and observation spaces, respectively. This is useful for agents that are trainable, but have some special logic that needs to be implemented in the get_action_privileged method. trainable takes precedence over this attribute, as in if trainable is False, this attribute is ignored.

• overlay_color (Tuple[float, float, float, float]) – The color to use in the visualization of the agent.

• overlay_size (float) – The size of the overlay in the visualization of the agent.

• xml (MjCambrianXMLConfig) – The xml for the agent. This is the xml that will be used to create the agent. You should use ${parent:xml} to generate named attributes. This will search upwards in the yaml file to find the name of the agent.

• body_name (str) – The name of the body that defines the main body of the agent.

• geom_name (str) – The name of the geom that are used for eye placement.

• check_contacts (bool) – Whether to check contacts or not. If this is True, then the contacts will be checked in the environment.

• init_pos (Tuple[float | None]) – The initial position of the agent. Specific indices of the position are set when not None. The length of the tuple should be <= 3. None’s are filled in at the end if the length is less than 3.

• init_quat (Tuple[float | None]) – The initial quaternion of the agent. Specific indices of the quaternion are set when not None. The length of the tuple should be <= 4. None’s are filled in at the end if the length is less than 4.

• perturb_init_pos (bool) – Whether to perturb the initial pos of the agent or not. If this is True, then the initial pos of the agent will be randomly adjusted based on a normal distribution.

• use_action_obs (bool) – Whether to use the action observation or not. NOTE: If the MjCambrianConstantActionWrapper is used, this is not reflected in the observation, as in the actions will vary in the observation.

• use_contact_obs (bool) – Whether to use the contact observation or not. If this is True, then the contacts will be included in the observation space of the agent.

• eyes (Dict[str, MjCambrianEyeConfig]) – The eyes on the agent. The keys are the names of the eyes and the values are the configs for the eyes. The eyes will be placed on the agent at the specified coordinates.

class MjCambrianAgent(config, name)

The agent class is defined as a physics object with eyes.

This object serves as an agent in a multi-agent mujoco environment. Therefore, it must have a uniquely identifiable name.

In our context, an agent has at any number of eyes and a body which an eye can be attached to. This class abstracts away the inner workings of the mujoco model itself to the xml generation/loading. It uses existing xml files that only define the agents, with which are going to be accumulating into one large xml file that will be loaded into mujoco.

To support specific agent types, you should define subclasses that include agent specific configs (i.e. model_path, num_joints).

Parameters:

• config (MjCambrianAgentConfig) – The configuration for the agent.

• name (str) – The name of the agent. This is used to identify the agent in the environment.

generate_xml()

Generates the xml for the agent. Will generate the xml from the model file and then add eyes to it.

apply_action(actions)

Applies the action to the agent. This probably happens before step so that the observations reflect the state of the agent after the new action is applied.

It is assumed that the actions are normalized between -1 and 1.

abstract get_action_privileged(env)

This is a deviation from the standard gym API. This method is similar to step, but it has “privileged” access to information such as the environment. This method can be overridden by agents which are not trainable and need to implement custom step logic.

Parameters:

env (MjCambrianEnv) – The environment that the agent is in. This can be used to get information about the environment.

Returns:

List[float] – The action to take.

reset(spec)

Sets up the agent in the environment. Uses the model/data to update positions during the simulation.

step()

Steps the eyes and returns the observation.

render()

Renders the eyes and returns the debug image.

We don’t know where the eyes are placed, so for simplicity, we’ll just return the first eye’s render.

property has_contacts: bool

Returns whether or not the agent has contacts.

Walks through all the contacts in the environment and checks if any of them involve this agent.

property observation_space: gymnasium.spaces.Space

The observation space is defined on an agent basis. the env should combine the observation spaces such that it’s supported by stable_baselines3/pettingzoo.

The agent has three observation spaces:

• {eye.name}: The eyes observations

• action: The last action that was applied to the agent

• contacts: Whether the agent has contacts or not

property action_space: gymnasium.spaces.Space

The action space is simply the controllable actuators of the agent.

property qpos: numpy.ndarray

Gets the qpos of the agent. The qpos is the state of the joints defined in the agent’s xml. This method is used to get the state of the qpos. It’s actually a masked array where the entries are masked if the qpos adr is not associated with the agent. This allows the return value to be indexed and edited as if it were the full qpos array.

property pos: numpy.ndarray

Returns the position of the agent in the environment.

Note

the returned value, if edited, doesn’t not directly impact the simulation. To set the position of the agent, use the pos setter.

property init_pos: Tuple[float | None, float | None, float | None]

Returns the initial position of the agent in the environment.

property quat: numpy.ndarray

wxyz.

Note

The returned value, if edited, doesn’t not directly impact the simulation. To set the quaternion of the agent, use the quat setter.

Type:

Returns the quaternion of the agent in the environment. Fmt

property trainable: bool

Returns whether the agent is trainable or not.

property num_eyes: int

Returns the number of eyes on the agent.

class MjCambrianAgent2D(config, name)

Bases: MjCambrianAgent

Assumes the agent is moving on 2D plane and has a yaw hinge joint which is used to adjust orientation of the agent.