Utils

navground.learning.utils

Plotting

navground.learning.utils.plot

plot_policy(policy: AnyPolicyPredictor, axs: Iterable[Axes] | None = None, fix: dict[str, float | Iterable[float]] = {}, colors: Iterable[Any] | None = None, variable: dict[str, tuple[float, float]] = {}, actions: dict[int, str] = {}, cmap: str = 'RdYlGn', samples: int = 101, width: float = 5, height: float = 3, label: str = '', **kwargs: Any) → None

Parameters:

• policy (AnyPolicyPredictor)

• axs (Iterable[Axes] | None)

• fix (dict[str, float | Iterable[float]])

• colors (Iterable[Any] | None)

• variable (dict[str, tuple[float, float]])

• actions (dict[int, str])

• cmap (str)

• samples (int)

• width (float)

• height (float)

• label (str)

• kwargs (Any)

Return type:

None

class LogField(key, label, low, high, linestyle, color)

Bases: NamedTuple

Parameters:

• key (str)

• label (str)

• low (float | None)

• high (float | None)

• linestyle (str)

• color (str | None)

plot_logs(logs: DataFrame, key: str, fields: Sequence[LogField], two_axis: bool = False, title: str = '', **kwargs: Any) → None

Plots logged fields.

Parameters:

• logs (DataFrame) – The logs

• key (str) – Common x-axis key

• fields (Sequence[LogField]) – Which fields to plot.

• two_axis (bool) – Whether to use two axis (only if there are two fields)

• title (str)

• kwargs (Any)

Return type:

None

StableBaseLine3

navground.learning.utils.sb3

class ExportOnnxCallback(name_fn: Callable[[BaseCallback], str])

Bases: BaseCallback

Exports the (best) model policy as “best_policy.onnx”.

Parameters:

name_fn (Callable[[BaseCallback], str])

class ProgressBarWithRewardCallback(every: int = 1)

Bases: BaseCallback

Similar to SB3’s own stable_baselines3.common.callbacks.ProgressBarCallback, it displays a progress bar when training SB3 agent using tqdm but includes episodes mean reward and length.

Parameters:

every (int)

class VideoCallback(env: BaseEnv | BaseParallelEnv | VecEnv, save_path: PathLike | None = None, number: int = 1, grouped: bool = False, pre: ObservationTransform | None = None, group_pre: GroupObservationsTransform | None = None, video_format: str = 'mp4', policy: Any = None, **kwargs: Any)

Bases: BaseCallback

A SB3 callback that makes a video of one or more runs of an environment.

Parameters:

• env (BaseEnv | BaseParallelEnv | VecEnv)

• save_path (PathLike | None)

• number (int)

• grouped (bool)

• pre (ObservationTransform | None)

• group_pre (GroupObservationsTransform | None)

• video_format (str)

• policy (Any)

• kwargs (Any)

load_eval_logs(path: PathLike) → DataFrame

Loads the evaluation logs from a csv.

Parameters:

path (PathLike) – The directory with csv logs

Returns:

A dataframe with the logs

Return type:

DataFrame

plot_eval_logs(path: PathLike, two_axis: bool = False, reward: bool = True, reward_color: str = 'k', reward_linestyle: str = '--', success: bool = False, length: bool = False, reward_low: float | None = None, reward_high: float = 0, length_low: float = 0, length_high: float | None = None, **kwargs: Any) → None

Plots reward, success and/or length from the eval logs

Parameters:

• path (PathLike) – The directory with csv logs

• reward (bool) – Whether to plot the reward

• reward_color (str) – The reward color

• reward_linestyle (str) – The reward linestyle

• success (bool) – Whether to plot the success

• length (bool) – Whether to plot the length

• reward_low (float | None) – An optional lower bound to scale the reward

• reward_high (float) – An optional upper bound to scale the reward

• lenght_low – An optional lower bound to scale the reward

• lenght_high – An optional upper bound to scale the reward

• two_axis (bool) – Whether to use two axis (only if there are two fields)

• length_low (float)

• length_high (float | None)

• kwargs (Any)

Return type:

None

BenchMARL

navground.learning.utils.benchmarl