concepts.dm.crow.planners.regression_planning_impl.crow_regression_planner_astar_v3.CrowRegressionPlannerAStarv3

class CrowRegressionPlannerAStarv3

Bases: CrowRegressionPlanner

Methods

bfs(planning_state)
bfs_add_queue(planning_state)
bfs_expand(planning_state)	The BFS algorithm is simulating a hierarchical planning algorithm.
evaluate(expression, state[, csp, ...])
main()
main_entry(program)

Attributes

queue

__init__(executor, state, goal_expr, *, simulation_interface=None, enable_reordering=True, max_search_depth=20, max_beam_size=20, is_goal_serialized=True, is_goal_refinement_compressible=True, enable_csp=True, max_csp_trials=10, max_global_csp_trials=100, max_csp_branching_factor=5, use_generator_manager=False, store_generator_manager_history=False, verbose=True)

Parameters:

• executor (CrowExecutor) –

• state (CrowState) –

• goal_expr (str | ValueOutputExpression) –

• simulation_interface (CrowSimulationControllerInterface | None) –

• enable_reordering (bool) –

• max_search_depth (int) –

• max_beam_size (int) –

• is_goal_serialized (bool) –

• is_goal_refinement_compressible (bool) –

• enable_csp (bool) –

• max_csp_trials (int) –

• max_global_csp_trials (int) –

• max_csp_branching_factor (int) –

• use_generator_manager (bool) –

• store_generator_manager_history (bool) –

• verbose (bool) –

__new__(**kwargs)

bfs(planning_state)

Parameters:

planning_state (CrowPlanningState2) –

Return type:

Sequence[CrowPlanningResult]

bfs_add_queue(planning_state)

Parameters:

planning_state (CrowPlanningState2) –

bfs_expand(planning_state)

The BFS algorithm is simulating a hierarchical planning algorithm. The current state can be encoded as:

left_actions = (a1, a2, a3, ...)
middle_program = CrowActionOrderingSuite(...)
right_statements = [RegressionStatement2(...), RegressionStatement2(...), ...]

It corresponds to this figure:

a1 -> a2 -> a3 -> ... -> {middle_program} -> [right_statements]

Therefore,

• state.left_actions: the actions that have been executed (a1, a2, a3, …).

• state.program: the current program that is being expanded ({middle_program}).

• state.right_statements: the statements that are waiting to be expanded ([right_statements]).

At each step,

• If the program is empty, we will pop up the first right statement and expand it.

• If the program is not empty, we will randomly pop a statement from the middle program, and prepend it to the right statements.

Parameters:

planning_state (CrowPlanningState2) –

evaluate(expression, state, csp=None, bounded_variables=None, clone_csp=True)

Parameters:

• expression (ObjectOrValueOutputExpression | VariableAssignmentExpression) –

• state (CrowState) –

• csp (ConstraintSatisfactionProblem | None) –

• bounded_variables (Dict[str, TensorValue | ObjectConstant] | None) –

• clone_csp (bool) –

Return type:

Tuple[None | StateObjectReference | StateObjectList | TensorValue | OptimisticValue, ConstraintSatisfactionProblem | None]

main()

Return type:

Tuple[List[Tuple[CrowControllerApplier, …]], dict]

main_entry(program)

Parameters:

program (CrowActionOrderingSuite) –

Return type:

List[Tuple[CrowControllerApplier, …]]

queue: PriorityQueue