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/*
* Copyright (c) 2020, the SerenityOS developers.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "MCTSTree.h"
#include <AK/String.h>
#include <stdlib.h>
MCTSTree::MCTSTree(const Chess::Board& board, double exploration_parameter, MCTSTree* parent)
: m_parent(parent)
, m_exploration_parameter(exploration_parameter)
, m_board(board)
{
if (m_parent)
m_eval_method = m_parent->eval_method();
}
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_board.turn());
if (uct >= max_uct) {
max_uct = uct;
node = &child;
}
}
ASSERT(node);
return node->select_leaf();
}
MCTSTree& MCTSTree::expand()
{
ASSERT(!expanded() || m_children.size() == 0);
if (!m_moves_generated) {
m_board.generate_moves([&](Chess::Move move) {
Chess::Board clone = m_board;
clone.apply_move(move);
m_children.append(make<MCTSTree>(clone, m_exploration_parameter, this));
return IterationDecision::Continue;
});
m_moves_generated = true;
}
if (m_children.size() == 0) {
return *this;
}
for (auto& child : m_children) {
if (child.m_simulations == 0) {
return child;
}
}
ASSERT_NOT_REACHED();
}
int MCTSTree::simulate_game() const
{
ASSERT_NOT_REACHED();
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()
{
auto& node = select_leaf().expand();
int result;
if (m_eval_method == EvalMethod::Simulation) {
result = node.simulate_game();
} else {
result = node.heuristic();
}
node.apply_result(result);
}
Chess::Move MCTSTree::best_move() const
{
int score_multiplier = (m_board.turn() == Chess::Colour::White) ? 1 : -1;
Chess::Move best_move = { { 0, 0 }, { 0, 0 } };
double best_score = -double(INFINITY);
ASSERT(m_children.size());
for (auto& node : m_children) {
double node_score = node.expected_value() * score_multiplier;
if (node_score >= best_score) {
// The best move is the last move made in the child.
best_move = node.m_board.moves()[node.m_board.moves().size() - 1];
best_score = node_score;
}
}
return best_move;
}
double MCTSTree::expected_value() const
{
if (m_simulations == 0)
return 0;
return double(m_white_points) / m_simulations;
}
double MCTSTree::uct(Chess::Colour colour) 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() * ((colour == Chess::Colour::White) ? 1 : -1);
return expected + m_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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