@Override
protected State performActionHelper(State s, String[] params) {
// get agent and current position
ObjectInstance agent = s.getFirstObjectOfClass(CLASSAGENT);
int curX = agent.getDiscValForAttribute(ATTX);
int curY = agent.getDiscValForAttribute(ATTY);
// sample directon with random roll
double r = Math.random();
double sumProb = 0.;
int dir = 0;
for (int i = 0; i < this.directionProbs.length; i++) {
sumProb += this.directionProbs[i];
if (r < sumProb) {
dir = i;
break; // found direction
}
}
// get resulting position
int[] newPos = this.moveResult(curX, curY, dir);
// set the new position
agent.setValue(ATTX, newPos[0]);
agent.setValue(ATTY, newPos[1]);
// return the state we just modified
return s;
}
@Override
public List<TransitionProbability> getTransitions(State s, String[] params) {
// get agent and current position
ObjectInstance agent = s.getFirstObjectOfClass(CLASSAGENT);
int curX = agent.getDiscValForAttribute(ATTX);
int curY = agent.getDiscValForAttribute(ATTY);
List<TransitionProbability> tps = new ArrayList<TransitionProbability>(4);
TransitionProbability noChangeTransition = null;
for (int i = 0; i < this.directionProbs.length; i++) {
int[] newPos = this.moveResult(curX, curY, i);
if (newPos[0] != curX || newPos[1] != curY) {
// new possible outcome
State ns = s.copy();
ObjectInstance nagent = ns.getFirstObjectOfClass(CLASSAGENT);
nagent.setValue(ATTX, newPos[0]);
nagent.setValue(ATTY, newPos[1]);
// create transition probability object and add to our list of outcomes
tps.add(new TransitionProbability(ns, this.directionProbs[i]));
} else {
// this direction didn't lead anywhere new
// if there are existing possible directions that wouldn't lead anywhere, aggregate with
// them
if (noChangeTransition != null) {
noChangeTransition.p += this.directionProbs[i];
} else {
// otherwise create this new state and transition
noChangeTransition = new TransitionProbability(s.copy(), this.directionProbs[i]);
tps.add(noChangeTransition);
}
}
}
return tps;
}
@Override
public double[] generateFeatureVectorFrom(State s) {
ObjectInstance agent = s.getFirstObjectOfClass(GridWorldDomain.CLASSAGENT);
int ax = agent.getDiscValForAttribute(GridWorldDomain.ATTX);
int ay = agent.getDiscValForAttribute(GridWorldDomain.ATTY);
double[] vec = new double[this.getDim()];
if (this.map[ax][ay] > 0) {
vec[map[ax][ay] - 1] = 1.;
}
return vec;
}
@Override
public boolean isTerminal(State s) {
// get location of agent in next state
ObjectInstance agent = s.getFirstObjectOfClass(CLASSAGENT);
int ax = agent.getDiscValForAttribute(ATTX);
int ay = agent.getDiscValForAttribute(ATTY);
// are they at goal location?
if (ax == this.goalX && ay == this.goalY) {
return true;
}
return false;
}
@Override
public double reward(State s, GroundedAction a, State sprime) {
// get location of agent in next state
ObjectInstance agent = sprime.getFirstObjectOfClass(CLASSAGENT);
int ax = agent.getDiscValForAttribute(ATTX);
int ay = agent.getDiscValForAttribute(ATTY);
// are they at goal location?
if (ax == this.goalX && ay == this.goalY) {
return 100.;
}
return -1;
}
@Override
public double[] generateFeatureVectorFrom(State s) {
ObjectInstance agent = s.getFirstObjectOfClass(GridWorldDomain.CLASSAGENT);
int ax = agent.getDiscValForAttribute(GridWorldDomain.ATTX);
int ay = agent.getDiscValForAttribute(GridWorldDomain.ATTY);
double[] vec = new double[this.getDim()];
if (this.map[ax][ay] > 0) {
vec[map[ax][ay] - 1] = 1.;
}
// now do distances
// first seed to max val
for (int i = this.numCells; i < vec.length; i++) {
vec[i] = 61.;
}
// set goal (type 0) to its goal position assuming only 1 instance of it, so we don't scan
// large distances for it
if (this.gx != -1) {
vec[this.numCells] = Math.abs(this.gx - ax) + Math.abs(this.gy - ay);
}
// now do scan
for (int r = 0; r < 16; r++) {
int x;
// scan top
int y = ay + r;
if (y < 30) {
for (x = Math.max(ax - r, 0); x <= Math.min(ax + r, 29); x++) {
this.updateNearest(vec, ax, ay, x, y);
}
}
// scan bottom
y = ay - r;
if (y > -1) {
for (x = Math.max(ax - r, 0); x <= Math.min(ax + r, 29); x++) {
this.updateNearest(vec, ax, ay, x, y);
}
}
// scan left
x = ax - r;
if (x > -1) {
for (y = Math.max(ay - r, 0); y <= Math.min(ay + r, 29); y++) {
this.updateNearest(vec, ax, ay, x, y);
}
}
// scan right
x = ax + r;
if (x < 30) {
for (y = Math.max(ay - r, 0); y <= Math.min(ay + r, 29); y++) {
this.updateNearest(vec, ax, ay, x, y);
}
}
if (this.foundNearestForAll(vec)) {
break;
}
}
return vec;
}
/**
* Returns the object instance in a state that holds the y-position information.
*
* @param s the state for which to get the y-position
* @return the object instance in a state that holds the y-position information.
*/
protected ObjectInstance yObjectInstance(State s) {
if (this.yClassName != null) {
return s.getFirstObjectOfClass(yClassName);
}
return s.getObject(yObjectName);
}
