ChessEngine: Add ChessEngine

This engine is pretty bad, but doesn't let itself get checkmated

Applications/ChessEngine/MCTSTree.cpp

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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;
+}