From 6ab40376dae4dc215e45426987035938a366180d Mon Sep 17 00:00:00 2001
From: Kilian Verhetsel <kilian.verhetsel@student.uclouvain.be>
Date: Tue, 29 Nov 2016 08:36:35 +0000
Subject: [PATCH] Pick best move by average, not upper bound, at the end of MC
simulations
---
Mesh/search.hpp | 21 +++++++++++++++------
1 file changed, 15 insertions(+), 6 deletions(-)
diff --git a/Mesh/search.hpp b/Mesh/search.hpp
index b4c6e054d9..4a954c583f 100644
--- a/Mesh/search.hpp
+++ b/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);
}
}
}
--
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