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/*
* Copyright (c) 2020, the SerenityOS developers.
*
* SPDX-License-Identifier: BSD-2-Clause
*/
#include "MCTSTree.h"
#include <AK/DeprecatedString.h>
#include <stdlib.h>
MCTSTree::MCTSTree(Chess::Board const& board, MCTSTree* parent)
: m_parent(parent)
, m_board(make<Chess::Board>(board))
, m_last_move(board.last_move())
, m_turn(board.turn())
{
}
MCTSTree::MCTSTree(MCTSTree&& other)
: m_children(move(other.m_children))
, m_parent(other.m_parent)
, m_white_points(other.m_white_points)
, m_simulations(other.m_simulations)
, m_board(move(other.m_board))
, m_last_move(move(other.m_last_move))
, m_turn(other.m_turn)
, m_moves_generated(other.m_moves_generated)
{
other.m_parent = nullptr;
}
MCTSTree& MCTSTree::select_leaf()
{
if (!expanded() || m_children.size() == 0)
return *this;
MCTSTree* node = nullptr;
double max_uct = -double(INFINITY);
for (auto& child : m_children) {
double uct = child.uct(m_turn);
if (uct >= max_uct) {
max_uct = uct;
node = &child;
}
}
VERIFY(node);
return node->select_leaf();
}
MCTSTree& MCTSTree::expand()
{
VERIFY(!expanded() || m_children.size() == 0);
if (!m_moves_generated) {
m_board->generate_moves([&](Chess::Move chess_move) {
auto clone = m_board->clone_without_history();
clone.apply_move(chess_move);
m_children.append(make<MCTSTree>(move(clone), this));
return IterationDecision::Continue;
});
m_moves_generated = true;
if (m_children.size() != 0)
m_board = nullptr; // Release the board to save memory.
}
if (m_children.size() == 0) {
return *this;
}
for (auto& child : m_children) {
if (child.m_simulations == 0) {
return child;
}
}
VERIFY_NOT_REACHED();
}
int MCTSTree::simulate_game() const
{
Chess::Board clone = *m_board;
while (!clone.game_finished()) {
clone.apply_move(clone.random_move());
}
return clone.game_score();
}
int MCTSTree::heuristic() const
{
if (m_board->game_finished())
return m_board->game_score();
double winchance = max(min(double(m_board->material_imbalance()) / 6, 1.0), -1.0);
double random = double(rand()) / RAND_MAX;
if (winchance >= random)
return 1;
if (winchance <= -random)
return -1;
return 0;
}
void MCTSTree::apply_result(int game_score)
{
m_simulations++;
m_white_points += game_score;
if (m_parent)
m_parent->apply_result(game_score);
}
void MCTSTree::do_round()
{
// Note: Limit expansion to spare some memory
// Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search.
// Rémi Coulom.
auto* node_ptr = &select_leaf();
if (node_ptr->m_simulations > s_number_of_visit_parameter)
node_ptr = &select_leaf().expand();
auto& node = *node_ptr;
int result;
if constexpr (s_eval_method == EvalMethod::Simulation) {
result = node.simulate_game();
} else {
result = node.heuristic();
}
node.apply_result(result);
}
Optional<MCTSTree&> MCTSTree::child_with_move(Chess::Move chess_move)
{
for (auto& node : m_children) {
if (node.last_move() == chess_move)
return node;
}
return {};
}
MCTSTree& MCTSTree::best_node()
{
int score_multiplier = (m_turn == Chess::Color::White) ? 1 : -1;
MCTSTree* best_node_ptr = nullptr;
double best_score = -double(INFINITY);
VERIFY(m_children.size());
for (auto& node : m_children) {
double node_score = node.expected_value() * score_multiplier;
if (node_score >= best_score) {
best_node_ptr = &node;
best_score = node_score;
}
}
VERIFY(best_node_ptr);
return *best_node_ptr;
}
Chess::Move MCTSTree::last_move() const
{
return m_last_move.value();
}
double MCTSTree::expected_value() const
{
if (m_simulations == 0)
return 0;
return double(m_white_points) / m_simulations;
}
double MCTSTree::uct(Chess::Color color) const
{
// UCT: Upper Confidence Bound Applied to Trees.
// Kocsis, Levente; Szepesvári, Csaba (2006). "Bandit based Monte-Carlo Planning"
// Fun fact: Szepesvári was my data structures professor.
double expected = expected_value() * ((color == Chess::Color::White) ? 1 : -1);
return expected + s_exploration_parameter * sqrt(log(m_parent->m_simulations) / m_simulations);
}
bool MCTSTree::expanded() const
{
if (!m_moves_generated)
return false;
for (auto& child : m_children) {
if (child.m_simulations == 0)
return false;
}
return true;
}
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