std::tuple<double, ValueFunction> IncrementalPruning::operator()(const M & model) { // Initialize "global" variables S = model.getS(); A = model.getA(); O = model.getO(); auto v = makeValueFunction(S); // TODO: May take user input unsigned timestep = 0; Pruner prune(S); Projecter projecter(model); const bool useTolerance = checkDifferentSmall(tolerance_, 0.0); double variation = tolerance_ * 2; // Make it bigger while ( timestep < horizon_ && ( !useTolerance || variation > tolerance_ ) ) { ++timestep; // Compute all possible outcomes, from our previous results. // This means that for each action-observation pair, we are going // to obtain the same number of possible outcomes as the number // of entries in our initial vector w. auto projs = projecter(v[timestep-1]); size_t finalWSize = 0; // In this method we split the work by action, which will then // be joined again at the end of the loop. for ( size_t a = 0; a < A; ++a ) { // We prune each outcome separately to be sure // we do not replicate work later. for ( size_t o = 0; o < O; ++o ) { const auto begin = std::begin(projs[a][o]); const auto end = std::end (projs[a][o]); projs[a][o].erase(prune(begin, end, unwrap), end); } // Here we reduce at the minimum the cross-summing, by alternating // merges. We pick matches like a reverse binary tree, so that // we always pick lists that have been merged the least. // // Example for O==7: // // 0 <- 1 2 <- 3 4 <- 5 6 // 0 ------> 2 4 ------> 6 // 2 <---------------- 6 // // In particular, the variables are: // // - oddOld: Whether our starting step has an odd number of elements. // If so, we skip the last one. // - front: The id of the element at the "front" of our current pass. // note that since passes can be backwards this can be high. // - back: Opposite of front, which excludes the last element if we // have odd elements. // - stepsize: The space between each "first" of each new merge. // - diff: The space between each "first" and its match to merge. // - elements: The number of elements we have left to merge. bool oddOld = O % 2; int i, front = 0, back = O - oddOld, stepsize = 2, diff = 1, elements = O; while ( elements > 1 ) { for ( i = front; i != back; i += stepsize ) { projs[a][i] = crossSum(projs[a][i], projs[a][i + diff], a, stepsize > 0); const auto begin = std::begin(projs[a][i]); const auto end = std::end (projs[a][i]); projs[a][i].erase(prune(begin, end, unwrap), end); --elements; } const bool oddNew = elements % 2; const int tmp = back; back = front - ( oddNew ? 0 : stepsize ); front = tmp - ( oddOld ? 0 : stepsize ); stepsize *= -2; diff *= -2; oddOld = oddNew; } // Put the result where we can find it if (front != 0) projs[a][0] = std::move(projs[a][front]); finalWSize += projs[a][0].size(); } VList w; w.reserve(finalWSize); // Here we don't have to do fancy merging since no cross-summing is involved for ( size_t a = 0; a < A; ++a ) w.insert(std::end(w), std::make_move_iterator(std::begin(projs[a][0])), std::make_move_iterator(std::end(projs[a][0]))); // We have them all, and we prune one final time to be sure we have // computed the parsimonious set of value functions. const auto begin = std::begin(w); const auto end = std::end (w); w.erase(prune(begin, end, unwrap), end); v.emplace_back(std::move(w)); // Check convergence if ( useTolerance ) variation = weakBoundDistance(v[timestep-1], v[timestep]); } return std::make_tuple(useTolerance ? variation : 0.0, v); }