public class StaticWeightedAStar extends AStar
If a terminal function is provided via the setter method defined for OO-MDPs, then the BestFirst search algorithm will not expand any nodes that are terminal states, as if there were no actions that could be executed from that state. Note that terminal states are not necessarily the same as goal states, since there could be a fail condition from which the agent cannot act, but that is not explicitly represented in the transition dynamics. 1. Pohl, Ira (1970). "First results on the effect of error in heuristic search". Machine Intelligence 5: 219-236.
|Modifier and Type||Field and Description|
The > 1 epsilon parameter.
cumulatedRewardMap, heuristic, lastComputedCumR
|Constructor and Description|
|Modifier and Type||Method and Description|
This method returns the f-score for a state given the parent search node, the generating action, the state that was produced.
insertIntoOpen, postPlanPrep, prePlanPrep, updateOpen
deterministicPlannerInit, encodePlanIntoPolicy, hasCachedPlanForState, planContainsOption, planHasDupilicateStates, querySelectedActionForState, resetSolver
addActionType, applicableActions, getActionTypes, getDebugCode, getDomain, getGamma, getHashingFactory, getModel, setActionTypes, setDebugCode, setDomain, setGamma, setHashingFactory, setModel, solverInit, stateHash, toggleDebugPrinting
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
protected double epsilonP1
public StaticWeightedAStar(SADomain domain, StateConditionTest gc, HashableStateFactory hashingFactory, Heuristic heuristic, double epsilon)
domain- the domain in which to plan
gc- should evaluate to true for goal states; false otherwise
hashingFactory- the state hashing factory to use
heuristic- the planning heuristic. Should return non-positive values.
epsilon- parameter > 1. The larger the value the more greedy.
public double computeF(PrioritizedSearchNode parentNode, Action generatingAction, HashableState successorState, double r)
parentNode- the parent search node (and its priority) that from which the next state was generated.
generatingAction- the action that was used to generate the next state.
successorState- the next state that was generated
r- the reward received for the transition