void Piece::updateTrajectory(const Board* board, const Position& oldPos, const Position& newPos)
{
Log::Debug::writeln("Updating", 1);
bool foundPos = false;
for (posList::iterator iter = trajectory.begin(); iter != trajectory.end(); iter++)
{
if (*iter == oldPos || *iter == newPos)
{
foundPos = true;
break;
}
}
if (foundPos) makeTrajectory(board);
}
void makeMultiExperimentPOMCP(
unsigned numExperiments, unsigned numTargets,
unsigned modelHorizon, const Model & model, const ap::Belief & modelBelief,
unsigned solverHorizon, std::vector<Solver> & solvers, const ap::Belief & solverBelief,
const std::string & outputFilename, bool useTrajectory = false )
{
static std::default_random_engine rand(AIToolbox::Impl::Seeder::getSeed());
double totalReward = 0.0;
std::vector<double> timestepTotalReward(modelHorizon, 0.0);
double avgReward = 0.0;
std::vector<size_t> obs(numTargets), pos(numTargets);
std::cout << numExperiments << " experiments with: Submodular POMCP! ";
std::cout << "Initial Belief: " << printBelief(modelBelief) << '\n';
std::cout << "Solver Belief: " << printBelief(solverBelief) << '\n';
unsigned experiment = 1;
for ( ; experiment <= numExperiments; ++experiment ) {
// Run pomcp, but don't get actions yet
for ( unsigned p = 0; p < numTargets; ++p ) {
pos[p] = AIToolbox::sampleProbability(model.getS(), modelBelief, rand);
solvers[p].sampleAction(solverBelief, std::min(solverHorizon, modelHorizon));
}
// Extract action
size_t a = extractAction(solvers);
std::vector<std::vector<size_t>> trajectories;
if ( useTrajectory ) {
for ( unsigned p = 0; p < numTargets; ++p ) trajectories.push_back( makeTrajectory(model, modelHorizon + 1, modelBelief) );
}
for ( unsigned i = 1; i <= modelHorizon; ++i ) {
double rew = 0.0;
#ifdef VISUALIZE
auto oldp = pos;
#endif
// Extract observations and rewards, and update positions of targets.
for ( unsigned p = 0; p < numTargets; ++p ) {
rew += ( model.getTrueState(solvers[p].getGuess()) == model.getTrueState(pos[p]) );
if ( useTrajectory ) {
pos[p] = trajectories[p][i];
std::tie(obs[p], std::ignore) = model.sampleOR( trajectories[p][i-1], a, trajectories[p][i] );
}
else
std::tie(pos[p], obs[p], std::ignore) = model.sampleSOR( pos[p], a );
}
totalReward += rew;
timestepTotalReward[i-1] += rew;
if ( experiment == 1 )
avgReward = totalReward;
else
avgReward = totalReward / (experiment - 1 + ((double)i)/modelHorizon);
std::cout // << "[S = " << s << "][A = " << a << "][S1 = " << s1 << "][ O = " << o << " ][ R = " << rew << " ]"
<< "EXPERIMENT " << std::setw(4) << experiment
<< ", TIMESTEP " << std::setw(4) << i
<< "\tTotal rew: " << std::setw(4) << totalReward
<< "\tAvg: " << std::setw(4) << avgReward;
#ifdef VISUALIZE
std::cout << '\n';
model.visualize(oldp, a);
#else
std::cout << '\r' << std::flush;
#endif
for ( unsigned p = 0; p < numTargets; ++p )
solvers[p].sampleAction(a, obs[p], std::min(solverHorizon, modelHorizon - i));
a = extractAction(solvers);
}
if ( processInterrupted ) break;
if ( ! (experiment % 100) )
gnuplotCumulativeSave(timestepTotalReward, outputFilename, experiment);
}
gnuplotCumulativeSave(timestepTotalReward, outputFilename, std::min(experiment, numExperiments));
}
