/**
* Selects a next state for expansion when action a is applied in state s by randomly sampling
* from the transition dynamics weighted by the margin of the lower and upper bound value
* functions.
*
* @param s the source state of the transition
* @param a the action applied in the source state
* @return a {@link StateSelectionAndExpectedGap} object holding the next state to be expanded and
* the expected margin size of this transition.
*/
protected StateSelectionAndExpectedGap getNextStateBySampling(State s, GroundedAction a) {
List<TransitionProbability> tps = a.getTransitions(s);
double sum = 0.;
double[] weightedGap = new double[tps.size()];
HashableState[] hashedStates = new HashableState[tps.size()];
for (int i = 0; i < tps.size(); i++) {
TransitionProbability tp = tps.get(i);
HashableState nsh = this.hashingFactory.hashState(tp.s);
hashedStates[i] = nsh;
double gap = this.getGap(nsh);
weightedGap[i] = tp.p * gap;
sum += weightedGap[i];
}
double roll = RandomFactory.getMapped(0).nextDouble();
double cumSum = 0.;
for (int i = 0; i < weightedGap.length; i++) {
cumSum += weightedGap[i] / sum;
if (roll < cumSum) {
StateSelectionAndExpectedGap select =
new StateSelectionAndExpectedGap(hashedStates[i], sum);
return select;
}
}
throw new RuntimeException("Error: probabilities in state selection did not sum to 1.");
}
/**
* Selects a next state for expansion when action a is applied in state s according to the next
* possible state that has the largest lower and upper bound margin. Ties are broken randomly.
*
* @param s the source state of the transition
* @param a the action applied in the source state
* @return a {@link StateSelectionAndExpectedGap} object holding the next state to be expanded and
* the expected margin size of this transition.
*/
protected StateSelectionAndExpectedGap getNextStateByMaxMargin(State s, GroundedAction a) {
List<TransitionProbability> tps = a.getTransitions(s);
double sum = 0.;
double maxGap = Double.NEGATIVE_INFINITY;
List<HashableState> maxStates = new ArrayList<HashableState>(tps.size());
for (TransitionProbability tp : tps) {
HashableState nsh = this.hashingFactory.hashState(tp.s);
double gap = this.getGap(nsh);
sum += tp.p * gap;
if (gap == maxGap) {
maxStates.add(nsh);
} else if (gap > maxGap) {
maxStates.clear();
maxStates.add(nsh);
maxGap = gap;
}
}
int rint = RandomFactory.getMapped(0).nextInt(maxStates.size());
StateSelectionAndExpectedGap select =
new StateSelectionAndExpectedGap(maxStates.get(rint), sum);
return select;
}
/**
* Returns the action suggested by the planner for the given state. If a plan including this state
* has not already been computed, the planner will be called from this state to find one.
*
* @param s the state for which the suggested action is to be returned.
* @return The suggested action for the given state.
*/
public GroundedAction querySelectedActionForState(State s) {
StateHashTuple sh = this.stateHash(s);
StateHashTuple indexSH = mapToStateIndex.get(sh);
if (indexSH == null) {
this.planFromState(s);
return internalPolicy.get(
sh); // no need to translate because if the state didn't exist then it got indexed with
// this state's rep
}
// otherwise it's already computed
GroundedAction res = internalPolicy.get(sh);
// do object matching from returned result to this query state and return result
res = (GroundedAction) res.translateParameters(indexSH.s, sh.s);
return res;
}
/**
* Selects a next state for expansion when action a is applied in state s.
*
* @param s the source state of the transition
* @param a the action applied in the source state
* @return a {@link StateSelectionAndExpectedGap} object holding the next state to be expanded and
* the expected margin size of this transition.
*/
protected StateSelectionAndExpectedGap getNextState(State s, GroundedAction a) {
if (this.selectionMode == StateSelectionMode.MODELBASED) {
HashableState nsh = this.hashingFactory.hashState(a.executeIn(s));
double gap = this.getGap(nsh);
return new StateSelectionAndExpectedGap(nsh, gap);
} else if (this.selectionMode == StateSelectionMode.WEIGHTEDMARGIN) {
return this.getNextStateBySampling(s, a);
} else if (this.selectionMode == StateSelectionMode.MAXMARGIN) {
return this.getNextStateByMaxMargin(s, a);
}
throw new RuntimeException("Unknown state selection mode.");
}
@Override
public EpisodeAnalysis runLearningEpisodeFrom(State initialState, int maxSteps) {
EpisodeAnalysis ea = new EpisodeAnalysis(initialState);
State curState = initialState;
int steps = 0;
while (!this.tf.isTerminal(curState) && steps < maxSteps) {
GroundedAction ga = (GroundedAction) policy.getAction(curState);
State nextState = ga.executeIn(curState);
double r = this.rf.reward(curState, ga, nextState);
ea.recordTransitionTo(nextState, ga, r);
this.model.updateModel(curState, ga, nextState, r, this.tf.isTerminal(nextState));
this.modelPlanner.performBellmanUpdateOn(curState);
curState = nextState;
steps++;
}
return ea;
}
/**
* 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;
}
@Override
public EpisodeAnalysis runLearningEpisodeFrom(State initialState) {
EpisodeAnalysis ea = new EpisodeAnalysis(initialState);
maxWeightChangeInLastEpisode = 0.;
State curState = initialState;
eStepCounter = 0;
Map<Integer, EligibilityTraceVector> traces =
new HashMap<Integer, GradientDescentSarsaLam.EligibilityTraceVector>();
GroundedAction action = this.learningPolicy.getAction(curState);
List<ActionApproximationResult> allCurApproxResults = this.getAllActionApproximations(curState);
ActionApproximationResult curApprox =
ActionApproximationResult.extractApproximationForAction(allCurApproxResults, action);
while (!tf.isTerminal(curState) && eStepCounter < maxEpisodeSize) {
WeightGradient gradient = this.vfa.getWeightGradient(curApprox.approximationResult);
State nextState = action.executeIn(curState);
GroundedAction nextAction = this.learningPolicy.getAction(nextState);
List<ActionApproximationResult> allNextApproxResults =
this.getAllActionApproximations(nextState);
ActionApproximationResult nextApprox =
ActionApproximationResult.extractApproximationForAction(allNextApproxResults, nextAction);
double nextQV = nextApprox.approximationResult.predictedValue;
if (tf.isTerminal(nextState)) {
nextQV = 0.;
}
// manage option specifics
double r = 0.;
double discount = this.gamma;
if (action.action.isPrimitive()) {
r = rf.reward(curState, action, nextState);
eStepCounter++;
ea.recordTransitionTo(nextState, action, r);
} else {
Option o = (Option) action.action;
r = o.getLastCumulativeReward();
int n = o.getLastNumSteps();
discount = Math.pow(this.gamma, n);
eStepCounter += n;
if (this.shouldDecomposeOptions) {
ea.appendAndMergeEpisodeAnalysis(o.getLastExecutionResults());
} else {
ea.recordTransitionTo(nextState, action, r);
}
}
// delta
double delta = r + (discount * nextQV) - curApprox.approximationResult.predictedValue;
if (useReplacingTraces) {
// then first clear traces of unselected action and reset the trace for the selected one
for (ActionApproximationResult aar : allCurApproxResults) {
if (!aar.ga.equals(action)) { // clear unselected action trace
for (FunctionWeight fw : aar.approximationResult.functionWeights) {
traces.remove(fw.weightId());
}
} else { // reset trace of selected action
for (FunctionWeight fw : aar.approximationResult.functionWeights) {
EligibilityTraceVector storedTrace = traces.get(fw.weightId());
if (storedTrace != null) {
storedTrace.eligibilityValue = 0.;
}
}
}
}
}
// update all traces
Set<Integer> deletedSet = new HashSet<Integer>();
for (EligibilityTraceVector et : traces.values()) {
int weightId = et.weight.weightId();
et.eligibilityValue += gradient.getPartialDerivative(weightId);
double newWeight =
et.weight.weightValue() + this.learningRate * delta * et.eligibilityValue;
et.weight.setWeight(newWeight);
double deltaW = Math.abs(et.initialWeightValue - newWeight);
if (deltaW > maxWeightChangeInLastEpisode) {
maxWeightChangeInLastEpisode = deltaW;
}
et.eligibilityValue *= this.lambda * discount;
if (et.eligibilityValue < this.minEligibityForUpdate) {
deletedSet.add(weightId);
}
}
// add new traces if need be
for (FunctionWeight fw : curApprox.approximationResult.functionWeights) {
int weightId = fw.weightId();
if (!traces.containsKey(fw)) {
// then it's new and we need to add it
EligibilityTraceVector et =
new EligibilityTraceVector(fw, gradient.getPartialDerivative(weightId));
double newWeight = fw.weightValue() + this.learningRate * delta * et.eligibilityValue;
fw.setWeight(newWeight);
double deltaW = Math.abs(et.initialWeightValue - newWeight);
if (deltaW > maxWeightChangeInLastEpisode) {
maxWeightChangeInLastEpisode = deltaW;
}
et.eligibilityValue *= this.lambda * discount;
if (et.eligibilityValue >= this.minEligibityForUpdate) {
traces.put(weightId, et);
}
}
}
// delete any traces
for (Integer t : deletedSet) {
traces.remove(t);
}
// move on
curState = nextState;
action = nextAction;
curApprox = nextApprox;
allCurApproxResults = allNextApproxResults;
}
if (episodeHistory.size() >= numEpisodesToStore) {
episodeHistory.poll();
episodeHistory.offer(ea);
}
return ea;
}
@Override
public EpisodeAnalysis runLearningEpisodeFrom(State initialState, int maxSteps) {
this.toggleShouldAnnotateOptionDecomposition(shouldAnnotateOptions);
EpisodeAnalysis ea = new EpisodeAnalysis(initialState);
StateHashTuple curState = this.stateHash(initialState);
eStepCounter = 0;
maxQChangeInLastEpisode = 0.;
while (!tf.isTerminal(curState.s) && eStepCounter < maxSteps) {
GroundedAction action = (GroundedAction) learningPolicy.getAction(curState.s);
QValue curQ = this.getQ(curState, action);
StateHashTuple nextState = this.stateHash(action.executeIn(curState.s));
double maxQ = 0.;
if (!tf.isTerminal(nextState.s)) {
maxQ = this.getMaxQ(nextState);
}
// manage option specifics
double r = 0.;
double discount = this.gamma;
if (action.action.isPrimitive()) {
r = rf.reward(curState.s, action, nextState.s);
eStepCounter++;
ea.recordTransitionTo(nextState.s, action, r);
} else {
Option o = (Option) action.action;
r = o.getLastCumulativeReward();
int n = o.getLastNumSteps();
discount = Math.pow(this.gamma, n);
eStepCounter += n;
if (this.shouldDecomposeOptions) {
ea.appendAndMergeEpisodeAnalysis(o.getLastExecutionResults());
} else {
ea.recordTransitionTo(nextState.s, action, r);
}
}
double oldQ = curQ.q;
// update Q-value
curQ.q =
curQ.q
+ this.learningRate.pollLearningRate(curState.s, action)
* (r + (discount * maxQ) - curQ.q);
double deltaQ = Math.abs(oldQ - curQ.q);
if (deltaQ > maxQChangeInLastEpisode) {
maxQChangeInLastEpisode = deltaQ;
}
// move on
curState = nextState;
}
if (episodeHistory.size() >= numEpisodesToStore) {
episodeHistory.poll();
}
episodeHistory.offer(ea);
return ea;
}