@Override
public void planFromState(State initialState) {
StateHashTuple sih = this.stateHash(initialState);
if (mapToStateIndex.containsKey(sih)) {
return; // no need to plan since this is already solved
}
PrioritizedSearchNode initialPSN = new PrioritizedSearchNode(sih, heuristic.h(initialState));
double nextMinR = initialPSN.priority;
PrioritizedSearchNode solutionNode = null;
while (solutionNode == null) {
PrioritizedSearchNode cand = this.FLimtedDFS(initialPSN, nextMinR, 0.);
if (cand == null) {
return; // FAIL CONDITION, EVERY PATH LEADS TO A DEAD END
}
// was the goal found within the limit?
if (this.planEndNode(cand) && cand.priority >= nextMinR) {
solutionNode = cand;
}
nextMinR = cand.priority;
if (solutionNode == null) {
DPrint.cl(debugCode, "Increase depth to F: " + nextMinR);
}
}
// search to goal complete now follow back pointers to set policy
this.encodePlanIntoPolicy(solutionNode);
}
/**
* Recursive method to perform A* up to a f-score depth
*
* @param lastNode the node to expand
* @param minR the minimum cumulative reward at which to stop the search (in other terms the
* maximum cost)
* @param cumulatedReward the amount of reward accumulated at this node
* @return a search node with the goal state, or null if there is no path within the reward
* requirements from this node
*/
protected PrioritizedSearchNode FLimtedDFS(
PrioritizedSearchNode lastNode, double minR, double cumulatedReward) {
if (lastNode.priority < minR) {
return lastNode; // fail condition (either way return the last point to which you got)
}
if (this.planEndNode(lastNode)) {
return lastNode; // succeed condition
}
if (this.tf.isTerminal(lastNode.s.s)) {
return null; // treat like a dead end if we're at a terminal state
}
State s = lastNode.s.s;
// get all actions
/*List <GroundedAction> gas = new ArrayList<GroundedAction>();
for(Action a : actions){
gas.addAll(s.getAllGroundedActionsFor(a));
}*/
List<GroundedAction> gas =
Action.getAllApplicableGroundedActionsFromActionList(this.actions, s);
// generate successor nodes
List<PrioritizedSearchNode> successors = new ArrayList<PrioritizedSearchNode>(gas.size());
List<Double> successorGs = new ArrayList<Double>(gas.size());
for (GroundedAction ga : gas) {
State ns = ga.executeIn(s);
StateHashTuple nsh = this.stateHash(ns);
double r = rf.reward(s, ga, ns);
double g = cumulatedReward + r;
double hr = heuristic.h(ns);
double f = g + hr;
PrioritizedSearchNode pnsn = new PrioritizedSearchNode(nsh, ga, lastNode, f);
// only add if this does not exist on our path already
if (this.lastStateOnPathIsNew(pnsn)) {
successors.add(pnsn);
successorGs.add(g);
}
}
// sort the successors by f-score to travel the most promising ones first
Collections.sort(successors, nodeComparator);
double maxCandR = Double.NEGATIVE_INFINITY;
PrioritizedSearchNode bestCand = null;
// note that since we want to expand largest expected rewards first, we should go reverse order
// of the f-ordered successors
for (int i = successors.size() - 1; i >= 0; i--) {
PrioritizedSearchNode snode = successors.get(i);
PrioritizedSearchNode cand = this.FLimtedDFS(snode, minR, successorGs.get(i));
if (cand != null) {
if (cand.priority > maxCandR) {
bestCand = cand;
maxCandR = cand.priority;
}
}
}
return bestCand;
}
