tfrddlsim.viz package

Submodules

tfrddlsim.viz.abstract_visualizer module

tfrddlsim.viz.generic_visualizer module

class tfrddlsim.viz.generic_visualizer.GenericVisualizer(compiler: rddl2tf.compilers.compiler.Compiler, verbose: bool)

Bases: tfrddlsim.viz.abstract_visualizer.Visualizer

GenericVisualizer is a generic text-based trajectory visualizer.

Parameters:

• compiler (rddl2tf.compiler.Compiler) – RDDL2TensorFlow compiler
• verbose (bool) – Verbosity flag

_render_batch(non_fluents: Sequence[Tuple[str, Union[int, float, <built-in function array>]]], states: Sequence[Tuple[str, <built-in function array>]], actions: Sequence[Tuple[str, <built-in function array>]], interms: Sequence[Tuple[str, <built-in function array>]], rewards: <built-in function array>, horizon: Union[int, NoneType] = None) → None

Prints non_fluents, states, actions, interms and rewards for given horizon.

Parameters:

• states (Sequence[Tuple[str, np.array]]) – A state trajectory.
• actions (Sequence[Tuple[str, np.array]]) – An action trajectory.
• interms (Sequence[Tuple[str, np.array]]) – An interm state trajectory.
• rewards (np.array) – Sequence of rewards (1-dimensional array).
• horizon (Optional[int]) – Number of timesteps.

_render_fluent_timestep(fluent_type: str, fluents: Sequence[Tuple[str, <built-in function array>]], fluent_variables: Sequence[Tuple[str, List[str]]]) → None

Prints fluents of given fluent_type as list of instantiated variables with corresponding values.

Parameters:

• fluent_type (str) – Fluent type.
• fluents (Sequence[Tuple[str, np.array]]) – List of pairs (fluent_name, fluent_values).
• fluent_variables (Sequence[Tuple[str, List[str]]]) – List of pairs (fluent_name, args).

_render_reward(r: numpy.float32) → None

Prints reward r.

_render_round_end(rewards: <built-in function array>) → None

Prints round end information about rewards.

_render_round_init(horizon: int, non_fluents: Sequence[Tuple[str, Union[int, float, <built-in function array>]]]) → None

Prints round init information about horizon and non_fluents.

_render_timestep(t: int, s: Sequence[Tuple[str, <built-in function array>]], a: Sequence[Tuple[str, <built-in function array>]], f: Sequence[Tuple[str, <built-in function array>]], r: numpy.float32) → None

Prints fluents and rewards for the given timestep t.

Parameters:

• t (int) – timestep
• (Sequence[Tuple[str], np.array] (f) – State fluents.
• (Sequence[Tuple[str], np.array] – Action fluents.
• (Sequence[Tuple[str], np.array] – Interm state fluents.
• r (np.float32) – Reward.

_render_trajectories(trajectories: Tuple[Sequence[Tuple[str, Union[int, float, <built-in function array>]]], Sequence[Tuple[str, <built-in function array>]], Sequence[Tuple[str, <built-in function array>]], Sequence[Tuple[str, <built-in function array>]], <built-in function array>]) → None

Prints the first batch of simulated trajectories.

Parameters:trajectories – NonFluents, states, actions, interms and rewards.

render(trajectories: Tuple[Sequence[Tuple[str, Union[int, float, <built-in function array>]]], Sequence[Tuple[str, <built-in function array>]], Sequence[Tuple[str, <built-in function array>]], Sequence[Tuple[str, <built-in function array>]], <built-in function array>], batch: Union[int, NoneType] = None) → None

Prints the simulated trajectories.

Parameters:

• trajectories – NonFluents, states, actions, interms and rewards.
• batch – Number of batches to render.

tfrddlsim.viz.navigation_visualizer module

Module contents