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// Copyright 2018 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "cc/mcts_node.h"
#include <array>
#include <set>
#include "cc/position.h"
#include "cc/random.h"
#include "cc/test_utils.h"
#include "gtest/gtes
10000
t.h"
namespace minigo {
namespace {
static constexpr char kAlmostDoneBoard[] = R"(
.XO.XO.OO
X.XXOOOO.
XXXXXOOOO
XXXXXOOOO
.XXXXOOO.
XXXXXOOOO
.XXXXOOO.
XXXXXOOOO
XXXXOOOOO)";
// Test puct and child action score calculation
TEST(MctsNodeTest, UpperConfidenceBound) {
float epsilon = 1e-7;
std::array<float, kNumMoves> probs;
for (float& prob : probs) {
prob = 0.02;
}
MctsNode::EdgeStats root_stats;
MctsNode root(&root_stats, TestablePosition("", Color::kBlack));
auto* leaf = root.SelectLeaf();
EXPECT_EQ(&root, leaf);
leaf->IncorporateResults(probs, 0.5, &root);
// 0.02 are normalized to 1/82
EXPECT_NEAR(1.0 / 82, root.child_P(0), epsilon);
EXPECT_NEAR(1.0 / 82, root.child_P(1), epsilon);
double puct_policy = kPuct * 1.0 / 82;
ASSERT_EQ(1, root.N());
EXPECT_NEAR(puct_policy * std::sqrt(1) / (1 + 0), root.child_U(0), epsilon);
leaf = root.SelectLeaf();
leaf->IncorporateResults(probs, 0.5, &root);
EXPECT_NE(&root, leaf);
EXPECT_EQ(&root, leaf->parent);
EXPECT_EQ(Coord(0), leaf->move);
// With the first child expanded.
ASSERT_EQ(2, root.N());
EXPECT_NEAR(puct_policy * std::sqrt(1) / (1 + 1), root.child_U(0), epsilon);
EXPECT_NEAR(puct_policy * std::sqrt(1) / (1 + 0), root.child_U(1), epsilon);
auto* leaf2 = root.SelectLeaf();
EXPECT_NE(&root, leaf2);
EXPECT_EQ(&root, leaf2->parent);
EXPECT_EQ(Coord(1), leaf2->move);
leaf2->IncorporateResults(probs, 0.5, &root);
// With the 2nd child expanded.
ASSERT_EQ(3, root.N());
EXPECT_NEAR(puct_policy * std::sqrt(2) / (1 + 1), root.child_U(0), epsilon);
EXPECT_NEAR(puct_policy * std::sqrt(2) / (1 + 1), root.child_U(1), epsilon);
EXPECT_NEAR(puct_policy * std::sqrt(2) / (1 + 0), root.child_U(2), epsilon);
}
// Verifies that no matter who is to play, when we know nothing else, the priors
// should be respected, and the same move should be picked.
TEST(MctsNodeTest, ActionFlipping) {
Random rnd(1);
std::array<float, kNumMoves> probs;
std::uniform_real_distribution<float> dist(0.02, 0.021);
for (float& prob : probs) {
prob = rnd();
}
MctsNode::EdgeStats black_stats, white_stats;
MctsNode black_root(&black_stats, TestablePosition("", Color::kBlack));
MctsNode white_root(&white_stats, TestablePosition("", Color::kWhite));
black_root.SelectLeaf()->IncorporateResults(probs, 0, &black_root);
white_root.SelectLeaf()->IncorporateResults(probs, 0, &white_root);
auto* black_leaf = black_root.SelectLeaf();
auto* white_leaf = white_root.SelectLeaf();
EXPECT_EQ(black_leaf->move, white_leaf->move);
EXPECT_EQ(black_root.CalculateChildActionScore(),
white_root.CalculateChildActionScore());
}
// Verfies that SelectLeaf chooses the child with the highest action score.
TEST(MctsNodeTest, SelectLeaf) {
std::array<float, kNumMoves> probs;
for (float& prob : probs) {
prob = 0.02;
}
Coord c = Coord::FromKgs("D9");
probs[c] = 0.4;
MctsNode::EdgeStats root_stats;
auto board = TestablePosition(kAlmostDoneBoard, Color::kWhite);
MctsNode root(&root_stats, board);
root.SelectLeaf()->IncorporateResults(probs, 0, &root);
EXPECT_EQ(Color::kWhite, root.position.to_play());
auto* leaf = root.SelectLeaf();
EXPECT_EQ(root.children[c].get(), leaf);
}
// Verifies IncorporateResults and BackupValue.
TEST(MctsNodeTest, BackupIncorporateResults) {
std::array<float, kNumMoves> probs;
for (float& prob : probs) {
prob = 0.02;
}
MctsNode::EdgeStats root_stats;
auto board = TestablePosition(kAlmostDoneBoard, Color::kWhite);
MctsNode root(&root_stats, board);
root.SelectLeaf()->IncorporateResults(probs, 0, &root);
auto* leaf = root.SelectLeaf();
leaf->IncorporateResults(probs, -1, &root); // white wins!
// Root was visited twice: first at the root, then at this child.
EXPECT_EQ(2, root.N());
// Root has 0 as a prior and two visits with value 0, -1.
EXPECT_FLOAT_EQ(-1.0 / 3, root.Q()); // average of 0, 0, -1
// Leaf should have one visit
EXPECT_EQ(1, root.child_N(leaf->move));
EXPECT_EQ(1, leaf->N());
// And that leaf's value had its parent's Q (0) as a prior, so the Q
// should now be the average of 0, -1
EXPECT_FLOAT_EQ(-0.5, root.child_Q(leaf->move));
EXPECT_FLOAT_EQ(-0.5, leaf->Q());
// We're assuming that SelectLeaf() returns a leaf like:
// root
// |
// leaf
// |
// leaf2
// which happens in this test because root is W to play and leaf was a W win.
EXPECT_EQ(Color::kWhite, root.position.to_play());
auto* leaf2 = root.SelectLeaf();
ASSERT_EQ(leaf, leaf2->parent);
leaf2->IncorporateResults(probs, -0.2, &root); // another white semi-win
EXPECT_EQ(3, root.N());
// average of 0, 0, -1, -0.2
EXPECT_FLOAT_EQ(-0.3, root.Q());
EXPECT_EQ(2, leaf->N());
EXPECT_EQ(1, leaf2->N());
// average of 0, -1, -0.2
EXPECT_FLOAT_EQ(root.child_Q(leaf->move), leaf->Q());
EXPECT_FLOAT_EQ(-0.4, leaf->Q());
// average of -1, -0.2
EXPECT_FLOAT_EQ(-0.6, leaf->child_Q(leaf2->move));
EXPECT_FLOAT_EQ(-0.6, leaf2->Q());
}
TEST(MctsNodeTest, DoNotExplorePastFinish) {
std::array<float, kNumMoves> probs;
for (float& prob : probs) {
prob = 0.02;
}
MctsNode::EdgeStats root_stats;
auto board = TestablePosition(kAlmostDoneBoard, Color::kWhite);
MctsNode root(&root_stats, board);
root.SelectLeaf()->IncorporateResults(probs, 0, &root);
auto* first_pass = root.MaybeAddChild(Coord::kPass);
first_pass->IncorporateResults(probs, 0, &root);
auto* second_pass = first_pass->MaybeAddChild(Coord::kPass);
EXPECT_DEATH(second_pass->IncorporateResults(probs, 0, &root),
"is_game_over");
float value = second_pass->position.CalculateScore(0) > 0 ? 1 : -1;
second_pass->IncorporateEndGameResult(value, &root);
auto* node_to_explore = second_pass->SelectLeaf();
// should just stop exploring at the end position.
EXPECT_EQ(second_pass, node_to_explore);
}
TEST(MctsNodeTest, AddChild) {
MctsNode::EdgeStats root_stats;
TestablePosition board("");
MctsNode root(&root_stats, board);
Coord c = Coord::FromKgs("B9");
auto* child = root.MaybeAddChild(c);
EXPECT_EQ(1, root.children.count(c));
EXPECT_EQ(&root, child->parent);
EXPECT_EQ(child->move, c);
}
TEST(MctsNodeTest, AddChildIdempotency) {
MctsNode::EdgeStats root_stats;
TestablePosition board("");
MctsNode root(&root_stats, board);
Coord c = Coord::FromKgs("B9");
auto* child = root.MaybeAddChild(c);
EXPECT_EQ(1, root.children.count(c));
EXPECT_EQ(1, root.children.size());
auto* child2 = root.MaybeAddChild(c);
EXPECT_EQ(child, child2);
EXPECT_EQ(1, root.children.count(c));
EXPECT_EQ(1, root.children.size());
}
TEST(MctsNodeTest, NeverSelectIllegalMoves) {
std::array<float, kNumMoves> probs;
for (float& prob : probs) {
prob = 0.02;
}
// let's say the NN were to accidentally put a high weight on an illegal move
probs[1] = 0.99;
MctsNode::EdgeStats root_stats;
auto board = TestablePosition(kAlmostDoneBoard, Color::kWhite);
MctsNode root(&root_stats, board);
root.SelectLeaf()->IncorporateResults(probs, 0, &root);
// and let's say the root were visited a lot of times, which pumps up the
// action score for unvisited moves...
root.stats->N = 100000;
for (int i = 0; i < kNumMoves; ++i) {
if (root.position.IsMoveLegal(i)) {
root.edges[i].N = 10000;
}
}
// this should not throw an error...
auto* leaf = root.SelectLeaf();
// the returned leaf should not be the illegal move
EXPECT_NE(1, leaf->move);
// and even after injecting noise, we should still not select an illegal move
Random rnd(1);
for (int i = 0; i < 10; ++i) {
std::array<float, kNumMoves> noise;
rnd.Uniform(0, 1, &noise);
root.InjectNoise(noise);
leaf = root.SelectLeaf();
EXPECT_NE(1, leaf->move);
}
}
TEST(MctsNodeTest, DontTraverseUnexpandedChild) {
std::array<float, kNumMoves> probs;
for (float& prob : probs) {
prob = 0.001;
}
// Make one move really likely so that tree search goes down that path twice
// even with a virtual loss.
probs[17] = 0.99;
MctsNode::EdgeStats root_stats;
auto board = TestablePosition(kAlmostDoneBoard, Color::kWhite);
MctsNode root(&root_stats, board);
root_stats.N = 5;
root.SelectLeaf()->IncorporateResults(probs, 0, &root);
auto* leaf1 = root.SelectLeaf();
EXPECT_EQ(17, leaf1->move);
leaf1->AddVirtualLoss(&root);
auto* leaf2 = root.SelectLeaf();
EXPECT_EQ(leaf1, leaf2); // assert we didn't go below the first leaf.
}
// Verifies that action score is used as a tie-breaker to choose between moves
// with the same visit count when selecting the best one.
// This test uses raw indices here instead of KGS coords to make it clear that
// without using action score as a tie-breaker, the move with the lower index
// would be selected by GetMostVisitedMove.
TEST(MctsNodeTest, GetMostVisitedPath) {
// Give two moves a higher probability.
std::array<float, kNumMoves> probs;
for (float& prob : probs) {
prob = 0.001;
}
probs[15] = 0.5;
probs[16] = 0.6;
MctsNode::EdgeStats root_stats;
auto board = TestablePosition("", Color::kBlack);
MctsNode root(&root_stats, board);
root.SelectLeaf()->IncorporateResults(probs, 0, &root);
// We should select the highest probabilty first.
auto* leaf1 = root.SelectLeaf();
EXPECT_EQ(Coord(16), leaf1->move);
leaf1->AddVirtualLoss(&root);
// Then the second highest probability.
auto* leaf2 = root.SelectLeaf();
EXPECT_EQ(Coord(15), leaf2->move);
leaf1->RevertVirtualLoss(&root);
// Both Coord(15) and Coord(16) have visit counts of 1.
// Coord(16) should be selected because of it's higher action score.
EXPECT_EQ(Coord(16), root.GetMostVisitedMove());
}
// Verifies that even when one move is hugely more likely than all the others,
// SelectLeaf will eventually start exploring other moves given enough
// iterations.
TEST(MctsNodeTest, TestSelectLeaf) {
std::array<float, kNumMoves> probs;
for (float& prob : probs) {
prob = 0.001;
}
probs[17] = 0.99;
MctsNode::EdgeStats root_stats;
auto board = TestablePosition(kAlmostDoneBoard, Color::kWhite);
MctsNode root(&root_stats, board);
root.SelectLeaf()->IncorporateResults(probs, 0, &root);
std::set<MctsNode*> leaves;
auto* leaf = root.SelectLeaf();
EXPECT_EQ(17, leaf->move);
leaf->AddVirtualLoss(&root);
leaves.insert(leaf);
for (int i = 0; i < 1000; ++i) {
leaf = root.SelectLeaf();
leaf->AddVirtualLoss(&root);
leaves.insert(leaf);
}
// We should have selected at least 2 leaves.
EXPECT_LE(2, leaves.size());
}
TEST(MctsNodeTest, NormalizeTest) {
// Generate probability with sum of policy less than 1
std::array<float, kNumMoves> probs;
for (float& prob : probs) {
prob = 0.001;
}
// Five times larger to test normalization
probs[17] = 0.005;
probs[18] = 0;
MctsNode::EdgeStats root_stats;
auto board = TestablePosition("");
MctsNode root(&root_stats, board);
root.IncorporateResults(probs, 0, &root);
// Adjust for the one value that is five times larger and one missing value.
float normalized = 1.0 / (kNumMoves - 1 + 4);
for (int i = 0; i < kNumMoves; ++i) {
if (i == 17) {
EXPECT_FLOAT_EQ(5 * normalized, root.child_P(i));
} else if (i == 18) {
EXPECT_FLOAT_EQ(0, root.child_P(i));
} else {
EXPECT_FLOAT_EQ(normalized, root.child_P(i));
}
}
}
TEST(MctsNodeTest, InjectNoiseOnlyLegalMoves) {
// Give moves a uniform policy value.
std::array<float, kNumMoves> probs;
for (float& prob : probs) {
prob = 0.02;
}
MctsNode::EdgeStats root_stats;
auto board = TestablePosition(kAlmostDoneBoard, Color::kWhite);
MctsNode root(&root_stats, board);
root.IncorporateResults(probs, 0, &root);
// kAlmostDoneBoard has 6 legal moves including pass.
float uniform_policy = 1.0 / 6;
for (int i = 0; i < kNumMoves; ++i) {
if (root.illegal_moves[i]) {
EXPECT_FLOAT_EQ(0, root.edges[i].P);
} else {
EXPECT_FLOAT_EQ(uniform_policy, root.edges[i].P);
}
}
// and even after injecting noise, we should still not select an illegal move
Random rnd(1);
std::array<float, kNumMoves> noise;
rnd.Uniform(0, 1, &noise);
root.InjectNoise(noise);
for (int i = 0; i < kNumMoves; ++i) {
if (root.illegal_moves[i]) {
EXPECT_FLOAT_EQ(0, root.edges[i].P);
} else {
EXPECT_LT(0.75 * uniform_policy, root.edges[i].P);
EXPECT_GT(0.75 * uniform_policy + 0.25, root.edges[i].P);
}
}
}
} // namespace
} // namespace minigo