Pick best move by average, not upper bound, at the end of MC simulations

Mesh/search.hpp

@@ -213,13 +213,22 @@ struct mcts_node {
 
     return std::distance(children.begin(), it);
   }
+
+  std::size_t best_move(std::size_t n) const {
+    auto it = std::max_element(children.begin(), children.end(),
+                               [&](const mcts_node<Score> &a, const mcts_node<Score> &b) {
+                                 return a.x_1 < b.x_1;
+                               });
+
+    return std::distance(children.begin(), it);
+  }
 };
 
 /**
  * Runs a single Monte Carlo Simulation as part of a Monte Carlo Tree Search.
  *
  * This operation is done as follows:
- *   1. Explore the seach tree of statistics (described by node), playing
+ *   1. Explore the tree of statistics (node being a pointer to its root), playing
  *      optimially according to those statistics, until reaching
  *      one of its leaves.
  *   2. Perform a completely random simulation, starting from the leaf found at
@@ -244,12 +253,12 @@ void mcts_simulation(mcts_node<Score> *node,
 
   ancestors.push_back(node);
 
-  bool explore = true;
+  bool selection = true;
 
   while (!actions.empty()) {
     std::size_t i;
 
-    if (explore && node->children.size() == 0) {
+    if (selection && node->children.size() == 0) {
       node->children.resize(actions.size());
 
       i = rand() % actions.size();
@@ -257,9 +266,9 @@ void mcts_simulation(mcts_node<Score> *node,
       node = &node->children[i];
       ancestors.push_back(node);
 
-      explore = false;
+      selection = false;
     }
-    else if (explore) {
+    else if (selection) {
       i = node->best_child(n);
       node = &node->children[i];
       ancestors.push_back(node);
@@ -321,7 +330,7 @@ void monte_carlo_tree_search(State &state, Successor &fn, Evaluator &eval) {
                         actions.begin(), actions.end());
       }
 
-      actions[node.best_child(i)](state).apply(state);
+      actions[node.best_move(i)](state).apply(state);
     }
   }
 }