Configuration

navground.learning.config

Actions

Base class

class ActionConfig

Bases: ABC, Registrable

The abstract base time of actions configuration.

Describes how the agent should convert control commands from navground navground.core.Behavior to actions to be applied a gymnasium.Env and, in the opposite direction), actions computed by a ML-policy to control commands to be applied in navground.

abstract configure(behavior: Behavior) → None

Complete the configuration for a target behavior is known.

Parameters:

behavior (Behavior) – The behavior

Return type:

None

abstract get_action(behavior: Behavior, time_step: float) → Action

Gets the action from the command computed by a behavior.

Parameters:

• behavior (Behavior) – The behavior

• time_step (float) – The time step

Returns:

The action.

Return type:

Action

abstract get_cmd_from_action(action: Action, behavior: Behavior | None, time_step: float) → Twist2

Convert action to navground command.

Parameters:

• action (Action) – The action

• behavior (Behavior | None) – The behavior that is requesting the command

• time_step (float) – The time step

Returns:

A control command.

Return type:

Twist2

abstract is_configured(warn: bool = False) → bool

Whether is the configuration is complete.

Returns:

True if complete, False otherwise.

Parameters:

warn (bool)

Return type:

bool

abstract property space: gym.Space[Any]

The action space

Returns:

A valid Gymnasium space

End-to-end

Continuous

class ControlActionConfig(dtype: str = '', max_speed: float = inf, max_angular_speed: float = inf, dof: int | None = None, max_acceleration: float = inf, max_angular_acceleration: float = inf, use_acceleration_action: bool = False, fix_orientation: bool = False, use_wheels: bool = False, has_wheels: bool | None = None)

Bases: ConfigWithKinematic, ActionConfig

Configuration of the conversion between control actions and control commands. Actions are either command accelerations or command velocities, depending on use_acceleration_action().

Parameters:

• dtype (str) – The data type

• dof (int | None) – The number of degrees of freedom of the agent

• max_speed (float) – The upper bound of the speed.

• max_angular_speed (float) – The upper bound of the angular speed.

• max_acceleration (float) – The upper bound of the acceleration.

• max_angular_acceleration (float) – The upper bound of the angular acceleration.

• use_acceleration_action (bool) – Whether actions are accelerations. If not set, actions are velocities.

• use_wheels (bool) – Whether action uses wheel speeds/acceleration instead of body speeds/acceleration. Only effective if the b behavior has a wheeled kinematics.

• fix_orientation (bool) – Whether to force the agent not to control orientation, i.e., to not include the angular command in actions.

• has_wheels (bool | None) – Whether the agent as wheels. If None, it will defer to the agent kinematics.

configure_kinematics(behavior: Behavior) → None

Configure the fields that are not yet set from the behavior’s kinematics.

Parameters:

behavior (Behavior) – The behavior

Return type:

None

classmethod from_dict(value: Mapping[str, Any]) → Self

Load an instance from a dictionary.

Selects a class from field type and calls its method _make_from_dict().

Parameters:

value (Mapping[str, Any]) – The value

Raises:

ValueError – If type is not defined or not registered.

Returns:

An instance initialized from the content of value.

Return type:

Self

property action_size: int

The number of control dimensions

property asdict: dict[str, Any]

The YAML-able dictionary representation.

Adds the class type name to the dictionary returned by _get_dict().

Returns:

A YAML-able dictionary

fix_orientation: bool = False

Whether to keep the agent orientation fixed

has_wheels: bool | None = None

Whether the agent as wheels. If None, it will defer to the agent kinematics.

max_acceleration: float = inf

The maximal agent acceleration module; only relevant if use_acceleration_action is set.

max_angular_acceleration: float = inf

The maximal agent angular acceleration; only relevant if use_acceleration_action is set.

property space: Space[Any]

The action space.

use_acceleration_action: bool = False

Whether to output acceleration commands instead of velocity commands.

use_wheels: bool = False

Whether to output control commands that refer to wheel speeds or accelerations.

Discrete

class DiscreteControlActionConfig(dtype: str = '', max_speed: float = inf, max_angular_speed: float = inf, dof: int | None = None, max_acceleration: float = inf, max_angular_acceleration: float = inf, use_acceleration_action: bool = False, fix_orientation: bool = False, use_wheels: bool = False, has_wheels: bool | None = None)

Bases: ControlActionConfig

Configuration of the conversion between discrete control actions and control commands. Actions are either command accelerations or command velocities, depending on ControlActionConfig.use_acceleration_action.

Discrete actions are first converted to continuous action and then, by the super class ControlActionConfig, to control commands.

The action space gymnasium.spaces.Discrete is discrete. Values are encoded using a ternary numeral system, where for every dimension:

Title

Discrete	Continuous
0	space.low
1	0
2	space.high

Parameters:

• dtype (str) – The data type

• dof (int | None) – The number of degrees of freedom of the agent

• max_speed (float) – The upper bound of the speed.

• max_angular_speed (float) – The upper bound of the angular speed.

• max_acceleration (float) – The upper bound of the acceleration.

• max_angular_acceleration (float) – The upper bound of the angular acceleration.

• use_acceleration_action (bool) – Whether actions are accelerations. If not set, actions are velocities.

• use_wheels (bool) – Whether action uses wheel speeds/acceleration instead of body speeds/acceleration. Only effective if the b behavior has a wheeled kinematics.

• fix_orientation (bool) – Whether to force the agent not to control orientation, i.e., to not include the angular command in actions.

• has_wheels (bool | None) – Whether the agent as wheels. If None, it will defer to the agent kinematics.

property space: Space[Any]

The action space.

• 0: backwards

• 1: do-nothing

• 2: forwards

[Multi-]Binary

class BinaryControlActionConfig(dtype: str = '', max_speed: float = inf, max_angular_speed: float = inf, dof: int | None = None, max_acceleration: float = inf, max_angular_acceleration: float = inf, use_acceleration_action: bool = False, fix_orientation: bool = False, use_wheels: bool = False, has_wheels: bool | None = None)

Bases: ControlActionConfig

Configuration of the conversion between discrete control actions and control commands. Actions are either command accelerations or command velocities, depending on ControlActionConfig.use_acceleration_action.

Discrete actions are first converted to continuous action and then, by the super class ControlActionConfig, to control commands.

The action space gymnasium.spaces.MultiBinary is discrete, with two values for each continuous dimension: the first bit refers to moving/accelerating backwards (yes/no), and the second bit to moving/accelerating forwards (yes/no).

Discrete values are encoded using a 2-bits per dimension, where

Title

Discrete	Continuous
[1, 0]	space.low
[0, 0]	0
[1, 1]	0
[0, 1]	space.high

Parameters:

• dtype (str) – The data type

• dof (int | None) – The number of degrees of freedom of the agent

• max_speed (float) – The upper bound of the speed.

• max_angular_speed (float) – The upper bound of the angular speed.

• max_acceleration (float) – The upper bound of the acceleration.

• max_angular_acceleration (float) – The upper bound of the angular acceleration.

• use_acceleration_action (bool) – Whether actions are accelerations. If not set, actions are velocities.

• use_wheels (bool) – Whether action uses wheel speeds/acceleration instead of body speeds/acceleration. Only effective if the b behavior has a wheeled kinematics.

• fix_orientation (bool) – Whether to force the agent not to control orientation, i.e., to not include the angular command in actions.

• has_wheels (bool | None) – Whether the agent as wheels. If None, it will defer to the agent kinematics.

property space: Space[Any]

The action space.

brake Y/N x accelerate Y/N

Communication

Continuous

class ControlActionWithCommConfig(dtype: str = '', max_speed: float = inf, max_angular_speed: float = inf, dof: int | None = None, max_acceleration: float = inf, max_angular_acceleration: float = inf, use_acceleration_action: bool = False, fix_orientation: bool = False, use_wheels: bool = False, has_wheels: bool | None = None, comm_size: int = 2)

Bases: ControlActionConfig

Configuration of the conversion between actions and control commands.

Actions are composed of control values (accelerations or command velocities, depending on ControlActionConfig.use_acceleration_action()) and the message to be transmitted.

Action are decomposed as:

• the control is converted to a navground.core.Twist2 (and later actuated)

• the message is set as attribute Behavior._comm (and later broadcasted)

Parameters:

• dtype (str) – The data type

• dof (int | None) – The number of degrees of freedom of the agent

• max_speed (float) – The upper bound of the speed.

• max_angular_speed (float) – The upper bound of the angular speed.

• max_acceleration (float) – The upper bound of the acceleration.

• max_angular_acceleration (float) – The upper bound of the angular acceleration.

• use_acceleration_action (bool) – Whether actions are accelerations. If not set, actions are velocities.

• use_wheels (bool) – Whether action uses wheel speeds/acceleration instead of body speeds/acceleration. Only effective if the b behavior has a wheeled kinematics.

• fix_orientation (bool) – Whether to force the agent not to control orientation, i.e., to not include the angular command in actions.

• has_wheels (bool | None) – Whether the agent as wheels. If None, it will defer to the agent kinematics.

• comm_size (int) – The size of the message to broadcast

comm_size: int = 2

The size of the message to broadcast

property comm_space: Box

The box space of the broadcasted messages with dimension ControlActionConfig.action_size() and values between -1 and 1.

Returns:

the box space

Discrete

class DiscreteControlActionWithCommConfig(dtype: str = '', max_speed: float = inf, max_angular_speed: float = inf, dof: int | None = None, max_acceleration: float = inf, max_angular_acceleration: float = inf, use_acceleration_action: bool = False, fix_orientation: bool = False, use_wheels: bool = False, has_wheels: bool | None = None, comm_size: int = 1)

Bases: ControlActionConfig

Actions are composed of control values and the message to be transmitted

Message {0, 1} and controls {0, 1, 2} are discrete and encoded using a mixed numerical scheme: - the lower part with a ternary encoding for controls - the upper part with binary encoding for messages.

Action to be actuated are decomposed: - the control is converted to a navground.core.Twist2, - the message is set as attribute Behavior._comm.

Parameters:

• dtype (str) – The data type

• dof (int | None) – The number of degrees of freedom of the agent

• max_speed (float) – The upper bound of the speed.

• max_angular_speed (float) – The upper bound of the angular speed.

• max_acceleration (float) – The upper bound of the acceleration.

• max_angular_acceleration (float) – The upper bound of the angular acceleration.

• use_acceleration_action (bool) – Whether actions are accelerations. If not set, actions are velocities.

• use_wheels (bool) – Whether action uses wheel speeds/acceleration instead of body speeds/acceleration. Only effective if the b behavior has a wheeled kinematics.

• fix_orientation (bool) – Whether to force the agent not to control orientation, i.e., to not include the angular command in actions.

• has_wheels (bool | None) – Whether the agent as wheels. If None, it will defer to the agent kinematics.

• comm_size (int) – The size of the message to broadcast

comm_size: int = 1

The size of the message to broadcast

property comm_space: Box

The box space of the broadcasted messages with dimension ControlActionConfig.action_size() and values in {0, 1}.

Returns:

the box space

property space: Space[Any]

The control space {0 (backwards), 1 (do-nothing), 2 (forwards)} multiplied by the communication space {0, 1}

Behavior Modulation

class ModulationActionConfig(dtype: str = '', params: dict[str, dict[str, ~typing.Any]] = <factory>)

Bases: DataclassConfig, ActionConfig

Configuration of the conversion between modulation actions and control commands.

Parameters:

• params (dict[str, dict[str, Any]]) – The parameters to modulate.

• dtype (str)

Actions are values of parameters of a behavior.

classmethod from_dict(value: Mapping[str, Any]) → Self

Load an instance from a dictionary.

Selects a class from field type and calls its method _make_from_dict().

Parameters:

value (Mapping[str, Any]) – The value

Raises:

ValueError – If type is not defined or not registered.

Returns:

An instance initialized from the content of value.

Return type:

Self

params: dict[str, dict[str, Any]]

The parameters to modulate as a dictionary name -> space configuration

property space: Space[Any]

The action space is a flattened gymnasium.spaces.Dict with one entry per controlled parameter.

Observations

Base class

class ObservationConfig

Bases: ABC, Registrable

Configure how to convert the state of a a navground.sim.Agent to observation consumed in a gymnasium.Env.

Observation may contain data from the agent’s behavior ego state (e.g., velocity) and navground.core.Behavior.target (e.g. relative target direction), and data generated by navground.sim.Sensor.

abstract configure(behavior: core.Behavior | None, sensing_space: gym.spaces.Dict) → None

Complete the configuration for a target behavior is known.

Parameters:

• behavior (core.Behavior | None) – The behavior

• sensing_space (gym.spaces.Dict) – The sensing space

Return type:

None

abstract is_configured(warn: bool = False) → bool

Whether is the configuration is complete.

Returns:

True if complete, False otherwise.

Parameters:

warn (bool)

Return type:

bool

abstract property history: int

The size of observations queue. If larger than 1, recent observations will be first stacked and then flattened.

Default

class DefaultObservationConfig(dtype: str = '', max_speed: float = inf, max_angular_speed: float = inf, dof: int | None = None, flat: bool = False, flat_values: bool = False, history: int = 1, include_target_orientation: bool = False, include_target_orientation_validity: bool = False, include_target_distance: bool = False, include_target_distance_validity: bool = False, max_target_distance: float = inf, include_target_direction: bool = True, include_target_direction_validity: bool = False, include_velocity: bool = False, include_angular_speed: bool = False, include_radius: bool = False, include_target_speed: bool = False, include_target_angular_speed: bool = False, max_radius: float = inf, normalize: bool = False, ignore_keys: list[str] = <factory>, sort_keys: bool = False, keys: ~collections.abc.Sequence[str] | None = None, include_position: bool = False, max_x: float = inf, max_y: float = inf, min_x: float = -inf, min_y: float = -inf, include_orientation: bool = False)

Bases: ConfigWithKinematic, ObservationConfig

This class configures which information from the behavior state to include in the observations.

Parameters:

• flat (bool) – Whether to flatten the observation space

• history (int) – The size of observations queue. If larger than 1, recent observations will be first stacked and then flattened.

• include_target_distance (bool) – Whether to include the target distance in the observations.

• include_target_distance_validity (bool) – Whether to include whether the target distance is valid in the observations.

• max_target_distance (float) – The upper bound of target distance. Only relevant if include_target_distance=True

• include_target_direction (bool) – Whether to include the target direction in the observations.

• include_target_direction_validity (bool) – Whether to include whether the target direction is valid in the observations.

• include_velocity (bool) – Whether to include the current velocity in the observations.

• include_angular_speed (bool) – Whether to include the current angular_speed in the observations.

• include_target_speed (bool) – Whether to include the target speed in the observations.

• include_target_angular_speed (bool) – Whether to include the target angular speed in the observations.

• max_speed (float) – The upper bound of the speed.

• max_angular_speed (float) – The upper bound of the angular speed.

• include_radius (bool) – Whether to include the own radius in the observations.

• max_radius (float) – The upper bound of own radius. Only relevant if include_radius=True.

• normalize (bool) – Whether to normalize observations in [-1, 1]

• ignore_keys (list[str]) – Which keys to ignore

• sort_keys (bool) – Whether to sort the keys

• keys (Sequence[str] | None) – Which keys to select

• include_position (bool) – Whether to include the behavior position Coordinates are only included if the related interval has length > 0, e.g., min_x < max for the x-coordinate.

• max_x (float) – The upper bound of x-coordinate

• max_y (float) – The upper bound of y-coordinate

• min_x (float) – The lower bound of x-coordinate

• min_y (float) – The lower bound of y-coordinate

• include_orientation (bool) – Whether to include the behavior orientation

• dtype (str)

• dof (int | None)

• flat_values (bool)

• include_target_orientation (bool)

• include_target_orientation_validity (bool)

configure_kinematics(behavior: Behavior) → None

Configure the fields that are not yet set from the behavior’s kinematics.

Parameters:

behavior (Behavior) – The behavior

Return type:

None

classmethod from_dict(value: Mapping[str, Any]) → Self

Load an instance from a dictionary.

Selects a class from field type and calls its method _make_from_dict().

Parameters:

value (Mapping[str, Any]) – The value

Raises:

ValueError – If type is not defined or not registered.

Returns:

An instance initialized from the content of value.

Return type:

Self

property asdict: dict[str, Any]

The YAML-able dictionary representation.

Adds the class type name to the dictionary returned by _get_dict().

Returns:

A YAML-able dictionary

flat: bool = False

Whether the observation space is flat

flat_values: bool = False

Whether the flatten the spaces of a dict observation space

history: int = 1

The size of observations queue. If larger than 1, recent observations will be first stacked and then flattened.

ignore_keys: list[str]

Which keys to ignore

include_angular_speed: bool = False

Whether observations include the current angular_speed.

include_orientation: bool = False

Whether to include the behavior orientation

include_position: bool = False

Whether to include the behavior position

include_radius: bool = False

Whether observations include the own radius.

include_target_angular_speed: bool = False

Whether observations include the target angular speed.

include_target_direction: bool = True

Whether observations include the target direction.

include_target_direction_validity: bool = False

Whether observations include whether the validity of the target direction.

include_target_distance: bool = False

Whether observations include the target direction.

include_target_distance_validity: bool = False

Whether observations include the validity of the target direction.

include_target_orientation: bool = False

TODO

include_target_orientation_validity: bool = False

TODO

include_target_speed: bool = False

Whether observations include the target speed.

include_velocity: bool = False

Whether observations include the current velocity.

keys: Sequence[str] | None = None

Which keys to select

max_radius: float = inf

The upper bound of own radius. Only relevant if include_radius is set.

max_target_distance: float = inf

The upper bound of target distance. Only relevant if include_target_distance is set.

max_x: float = inf

The maximal x-coordinate

max_y: float = inf

The maximal y-coordinate

min_x: float = -inf

The minimal x-coordinate

min_y: float = -inf

The minimal y-coordinate

normalize: bool = False

Whether to normalize observations in [-1, 1]

sort_keys: bool = False

Whether to sort the keys

State

Base class

class StateConfig

Bases: ABC, Registrable

Configure how to get the environment state from the current state of a navground.sim.World

abstract get_space(world: sim.World) → gym.spaces.Box

Returns the space of the global state

Parameters:

world (sim.World) – The world

Returns:

The space.

Return type:

gym.spaces.Box

abstract get_state(world: World) → Array

Computes the global state from the current world state

Parameters:

world (World) – The world

Returns:

an array with the global state

Return type:

Array

Default

class DefaultStateConfig(dtype: str = '', include_position: bool = False, include_orientation: bool = False, include_radius: bool = False, max_radius: float = inf, include_velocity: bool = False, include_angular_speed: bool = False, include_x: bool = True, include_y: bool = True, use_absolute_frame: bool = True, include_target_orientation: bool = False, include_target_orientation_validity: bool = False, include_target_distance: bool = False, max_target_distance: float = inf, include_target_distance_validity: bool = False, include_target_direction: bool = False, include_target_direction_validity: bool = False, include_target_speed: bool = False, include_target_angular_speed: bool = False, include_all: dataclasses.InitVar[bool] = False)

Bases: DataclassConfig, StateConfig

This class configures which information from the behavior state to include in the global environement state.

Parameters:

• include_position (bool) – Whether observations include the position

• include_orientation (bool) – Whether observations include the orientation

• include_radius (bool) – Whether to include the radius in the observations.

• max_radius (float) – Maximal radius.

• include_velocity (bool) – Whether to include the velocity

• include_angular_speed (bool) – Whether to include the angular_speed

• include_x (bool) – Whether observations include the x-coordinates

• include_y (bool) – Whether observations include the y-coordinates

• include_target_distance (bool) – Whether to include the target distance in the observations.

• max_target_distance (float) – Maximal target distance

• include_target_distance_validity (bool) – Whether to include whether the target distance is valid in the observations.

• include_target_direction (bool) – Whether to include the target direction in the observations.

• include_target_direction_validity (bool) – Whether to include whether the target direction is valid in the observations.

• include_target_speed (bool) – Whether to include the target speed in the observations.

• include_target_angular_speed (bool) – Whether to include the target angular speed in the observations.

• dtype (str)

• use_absolute_frame (bool)

• include_target_orientation (bool)

• include_target_orientation_validity (bool)

• include_all (dataclasses.InitVar[bool])

classmethod from_dict(value: Mapping[str, Any]) → Self

Load an instance from a dictionary.

Selects a class from field type and calls its method _make_from_dict().

Parameters:

value (Mapping[str, Any]) – The value

Raises:

ValueError – If type is not defined or not registered.

Returns:

An instance initialized from the content of value.

Return type:

Self

property asdict: dict[str, Any]

The YAML-able dictionary representation.

Adds the class type name to the dictionary returned by _get_dict().

Returns:

A YAML-able dictionary

include_all: dataclasses.InitVar[bool] = False

Whether to include all fields

include_angular_speed: bool = False

Whether to include the angular_speed.

include_orientation: bool = False

Whether observations include the orientation.

include_position: bool = False

Whether observations include the position.

include_radius: bool = False

Whether observations include the radius.

include_target_angular_speed: bool = False

Whether observations include the target angular speed.

include_target_direction: bool = False

Whether observations include the target direction.

include_target_direction_validity: bool = False

Whether observations include whether the validity of the target direction.

include_target_distance: bool = False

Whether observations include the target direction.

include_target_distance_validity: bool = False

Whether observations include the validity of the target direction.

include_target_orientation: bool = False

TODO

include_target_orientation_validity: bool = False

TODO

include_target_speed: bool = False

Whether observations include the target speed.

include_velocity: bool = False

Whether to include the velocity.

include_x: bool = True

Whether observations include the x-coordinates

include_y: bool = True

Whether observations include the y-coordinates

max_radius: float = inf

Maximal radius.

max_target_distance: float = inf

Maximal target distance

use_absolute_frame: bool = True

Whether to use the absolute frame for velocity and target direction

Group

class GroupConfig(indices: Indices = Indices.all(), action: ActionConfig | None = None, observation: ObservationConfig | None = None, sensor: dc.InitVar[SensorLike | None] = None, sensors: SensorSequenceLike = <factory>, reward: Reward | None = None, color: str = '', tag: str = '', policy: AnyPolicyPredictor | PathLike = '', deterministic: bool = False, terminate_on_success: bool | None = None, terminate_on_failure: bool | None = None, success_condition: TerminationCondition | None = None, failure_condition: TerminationCondition | None = None)

Bases: object

Configure a group of navground agents so that they are exposed in a gymnasium.Env (only single agents) or pettingzoo.utils.env.ParallelEnv (one or more agents).

Group configurations can be stuck. For instance, the configuration provided to define the environment for training needs needs to define at least indices, action and observation, and possibly also one sensors. Instead, for evaluation, we may provide just the policy, as the agents are already configured.

Parameters:

• indices (Indices) – The indices of the agents in the navground.sim.World.agents list.

• action (ActionConfig | None) – The actions configuration.

• observation (ObservationConfig | None) – The observations configuration.

• sensor (dc.InitVar[SensorLike | None]) – An optional sensor that will be added to sensors.

• sensors (SensorSequenceLike) – A sequence of sensor to generate observations for the agents or its YAML representation. If Items of class str will be interpreted as the YAML representation of a sensor. Items of class dict will be dumped to YAML and then treated as a str. If empty, it will use the agents’ own sensors.

• reward (Reward | None) – An optional reward function to use.

• color (str)

• tag (str)

• policy (AnyPolicyPredictor | PathLike)

• deterministic (bool)

• terminate_on_success (bool | None)

• terminate_on_failure (bool | None)

• success_condition (TerminationCondition | None)

• failure_condition (TerminationCondition | None)

:param color : An optional color for the agents (only used for displaying)

:param tagAn optional tag added to the agents navground.sim.Agent.tags,

(does not impact simulation, only used to identify the agents)

Parameters:

• policy (AnyPolicyPredictor | PathLike) – The policy assigned to the agents during evaluation.

• deterministic (bool) – Whether the agents apply such policy deterministically.

• terminate_on_success (bool | None) – Whether to terminate when the agent succeeds

• terminate_on_failure (bool | None) – Whether to terminate when the agent fails

• success_condition (TerminationCondition | None) – Optional success condition

• failure_condition (TerminationCondition | None) – Optional failure condition

• indices (Indices)

• action (ActionConfig | None)

• observation (ObservationConfig | None)

• sensor (dc.InitVar[SensorLike | None])

• sensors (SensorSequenceLike)

• reward (Reward | None)

• color (str)

• tag (str)

action: ActionConfig | None = None

The actions configuration

property asdict: dict[str, Any]

A JSON-able representation of the configuration

Returns:

A JSON-able dict

color: str = ''

The agents’ color

deterministic: bool = False

Whether the agents apply such policy deterministically

failure_condition: TerminationCondition | None = None

Failure condition

indices: Indices = Indices.all()

The indices of the agents in the navground.sim.World.agents list

observation: ObservationConfig | None = None

The observations configuration

policy: AnyPolicyPredictor | PathLike = ''

The policy assigned to the agents (during evaluation)

reward: Reward | None = None

The reward function

sensors: SensorSequenceLike

List of sensors to generate observations for the agents

success_condition: TerminationCondition | None = None

Success condition

tag: str = ''

An optional additional tag for the agents

terminate_on_failure: bool | None = None

Whether to terminate when the agent fails

terminate_on_success: bool | None = None

Whether to terminate when the agent succeeds

merge_groups_configs(groups: Collection[GroupConfig], other_groups: Collection[GroupConfig], max_number: int) → list[GroupConfig]

Overlaps two sets of group configurations, letting the first inherit attributes from the second if they are not specified.

Parameters:

• groups (Collection[GroupConfig]) – A collection of groups configuration

• other_groups (Collection[GroupConfig]) – Another collection of groups configuration to be inherited from.

• max_number (int) – The maximum number of agents, needed to intersect the groups GroupConfig.indices to identify which agents belong to groups in both sets.

Returns:

A new list of groups, where the attributes from both groups collections.

Return type:

list[GroupConfig]