Add a "threads" feature for mutli-threading

Cleanup debug print
Renamed the benchmark file
2025-06-30 19:49:45 -07:00 · 2025-06-29 22:51:17 -07:00 · 2025-06-29 22:50:54 -07:00 · 2025-06-29 18:43:52 -07:00 · 2025-06-27 22:25:56 -07:00
6 changed files with 491 additions and 43 deletions
@@ -2,6 +2,12 @@
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+
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+ "clap_builder",
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+
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+dependencies = [
+ "anstyle",
+ "clap_lex",
+ "terminal_size",
+]
+
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+dependencies = [
+ "cfg-if",
+ "clap",
+ "condtype",
+ "divan-macros",
+ "libc",
+ "regex-lite",
+]
+
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+ "quote",
+ "syn",
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+dependencies = [
+ "libc",
+ "windows-sys 0.60.2",
+]
+
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@@ -32,6 +105,12 @@ version = "0.2.174"
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+
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@@ -94,14 +173,34 @@ dependencies = [
 "getrandom",
 ]

+[[package]]
+name = "regex-lite"
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+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "53a49587ad06b26609c52e423de037e7f57f20d53535d66e08c695f347df952a"
+
 [[package]]
 name = "rustic_mcts"
 version = "0.1.0"
 dependencies = [
+ "divan",
 "rand",
 "thiserror",
 ]

+[[package]]
+name = "rustix"
+version = "1.0.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c71e83d6afe7ff64890ec6b71d6a69bb8a610ab78ce364b3352876bb4c801266"
+dependencies = [
+ "bitflags",
+ "errno",
+ "libc",
+ "linux-raw-sys",
+ "windows-sys 0.59.0",
+]
+
 [[package]]
 name = "syn"
 version = "2.0.103"
@@ -113,6 +212,16 @@ dependencies = [
 "unicode-ident",
 ]

+[[package]]
+name = "terminal_size"
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+dependencies = [
+ "rustix",
+ "windows-sys 0.59.0",
+]
+
 [[package]]
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@@ -148,6 +257,152 @@ dependencies = [
 "wit-bindgen-rt",
 ]

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+]
+
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+dependencies = [
+ "windows-targets 0.53.2",
+]
+
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+ "windows_aarch64_msvc 0.52.6",
+ "windows_i686_gnu 0.52.6",
+ "windows_i686_gnullvm 0.52.6",
+ "windows_i686_msvc 0.52.6",
+ "windows_x86_64_gnu 0.52.6",
+ "windows_x86_64_gnullvm 0.52.6",
+ "windows_x86_64_msvc 0.52.6",
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+
 [[package]]
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@@ -10,7 +10,16 @@ readme = "README.md"
 keywords = ["mcts", "rust", "monte_carlo", "tree", "ai", "ml"]
 categories = ["algorithms", "data-structures"]

+[features]
+threads = []

 [dependencies]
 rand = "~0.9"
 thiserror = "~2.0"
+
+[dev-dependencies]
+divan = "0.1.21"
+
+[[bench]]
+name = "e2e"
+harness = false
@@ -0,0 +1,191 @@
+use std::collections::HashMap;
+
+use rustic_mcts::policy::backprop::BackpropagationPolicy;
+use rustic_mcts::policy::decision::DecisionPolicy;
+use rustic_mcts::policy::selection::SelectionPolicy;
+use rustic_mcts::policy::simulation::SimulationPolicy;
+use rustic_mcts::{Action, GameState, MCTSConfig, RewardVal, MCTS};
+
+fn main() {
+    // Run registered benchmarks.
+    divan::main();
+}
+
+#[divan::bench]
+fn tic_tac_toe() {
+    // Set up a new game
+    let mut game = TicTacToe::new();
+
+    // Create MCTS configuration
+    let config = MCTSConfig {
+        max_iterations: 10_000,
+        max_time: None,
+        tree_size_allocation: 10_000,
+        selection_policy: SelectionPolicy::UCB1Tuned(1.414),
+        simulation_policy: SimulationPolicy::Random,
+        backprop_policy: BackpropagationPolicy::Standard,
+        decision_policy: DecisionPolicy::MostVisits,
+    };
+
+    // Main game loop
+    while !game.is_terminal() {
+        // Create a new MCTS search
+        let mut mcts = MCTS::new(game.clone(), &config);
+
+        // Find the best move
+        match mcts.search() {
+            Ok(action) => {
+                // Apply the AI's move
+                game = game.state_after_action(&action);
+            }
+            Err(_) => {
+                break;
+            }
+        }
+    }
+}
+
+/// Players in Tic-Tac-Toe
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
+enum Player {
+    X,
+    O,
+}
+
+impl rustic_mcts::Player for Player {}
+
+/// Tic-Tac-Toe move
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+struct Move {
+    /// Board position index (0-8)
+    index: usize,
+}
+
+impl Action for Move {
+    fn id(&self) -> usize {
+        self.index
+    }
+}
+
+/// Tic-Tac-Toe game state
+#[derive(Debug, Clone)]
+struct TicTacToe {
+    /// Board representation (None = empty, Some(Player) = occupied)
+    board: [Option<Player>; 9],
+
+    /// Current player's turn
+    current_player: Player,
+
+    /// Number of moves played so far
+    moves_played: usize,
+}
+
+impl TicTacToe {
+    /// Creates a new empty Tic-Tac-Toe board
+    fn new() -> Self {
+        TicTacToe {
+            board: [None; 9],
+            current_player: Player::X,
+            moves_played: 0,
+        }
+    }
+
+    /// Returns the winner of the game, if any
+    fn get_winner(&self) -> Option<Player> {
+        // Check rows
+        for row in 0..3 {
+            let i = row * 3;
+            if self.board[i].is_some()
+                && self.board[i] == self.board[i + 1]
+                && self.board[i] == self.board[i + 2]
+            {
+                return self.board[i];
+            }
+        }
+
+        // Check columns
+        for col in 0..3 {
+            if self.board[col].is_some()
+                && self.board[col] == self.board[col + 3]
+                && self.board[col] == self.board[col + 6]
+            {
+                return self.board[col];
+            }
+        }
+
+        // Check diagonals
+        if self.board[0].is_some()
+            && self.board[0] == self.board[4]
+            && self.board[0] == self.board[8]
+        {
+            return self.board[0];
+        }
+        if self.board[2].is_some()
+            && self.board[2] == self.board[4]
+            && self.board[2] == self.board[6]
+        {
+            return self.board[2];
+        }
+
+        None
+    }
+}
+
+impl GameState for TicTacToe {
+    type Action = Move;
+    type Player = Player;
+
+    fn get_legal_actions(&self) -> Vec<Self::Action> {
+        let mut actions = Vec::new();
+        for i in 0..9 {
+            if self.board[i].is_none() {
+                actions.push(Move { index: i });
+            }
+        }
+        actions
+    }
+
+    fn state_after_action(&self, action: &Self::Action) -> Self {
+        let mut new_state = self.clone();
+
+        // Make the move
+        new_state.board[action.index] = Some(self.current_player);
+        new_state.moves_played = self.moves_played + 1;
+
+        // Switch player
+        new_state.current_player = match self.current_player {
+            Player::X => Player::O,
+            Player::O => Player::X,
+        };
+
+        new_state
+    }
+
+    fn is_terminal(&self) -> bool {
+        self.get_winner().is_some() || self.moves_played == 9
+    }
+
+    fn reward_for_player(&self, player: &Self::Player) -> RewardVal {
+        if let Some(winner) = self.get_winner() {
+            if winner == *player {
+                return 1.0; // Win
+            } else {
+                return 0.0; // Loss
+            }
+        }
+
+        // Draw
+        0.5
+    }
+
+    fn rewards_for_players(&self) -> HashMap<Self::Player, RewardVal> {
+        HashMap::from_iter(vec![
+            (Player::X, self.reward_for_player(&Player::X)),
+            (Player::O, self.reward_for_player(&Player::O)),
+        ])
+    }
+
+    fn get_current_player(&self) -> &Self::Player {
+        &self.current_player
+    }
+}
@@ -40,7 +40,6 @@ fn main() {
        // Find the best move
        match mcts.search() {
            Ok(action) => {
-                mcts.print_tree();
                println!(
                    "AI chooses: {} (row {}, col {})",
                    action.index,
@@ -36,15 +36,15 @@ pub fn select_best_child<S: GameState>(
                exploration_constant,
                parent_visits,
                node_a.visits,
-                node_a.rewards(player),
                node_a.reward_average(player),
+                node_a.reward_variance(player),
            );
            let ucb_b = ucb1_tuned_value(
                exploration_constant,
                parent_visits,
                node_b.visits,
-                node_b.rewards(player),
                node_b.reward_average(player),
+                node_b.reward_variance(player),
            );
            ucb_a.partial_cmp(&ucb_b).unwrap()
        })
@@ -57,41 +57,31 @@ pub fn ucb1_tuned_value(
    exploration_constant: f64,
    parent_visits: u64,
    child_visits: u64,
-    child_rewards: Option<&Vec<RewardVal>>,
    reward_avg: RewardVal,
+    reward_variance: f64,
 ) -> RewardVal {
-    match child_rewards {
-        None => {
-            RewardVal::INFINITY // Always explore nodes that have never been visited
-        }
-        Some(child_rewards) => {
-            if child_visits == 0 {
-                RewardVal::INFINITY // Always explore nodes that have never been visited
-            } else {
-                let parent_visits: RewardVal = parent_visits as RewardVal;
-                let child_visits: RewardVal = child_visits as RewardVal;
+    if child_visits == 0 {
+        RewardVal::INFINITY // Always explore nodes that have never been visited
+    } else {
+        let parent_visits: RewardVal = parent_visits as RewardVal;
+        let child_visits: RewardVal = child_visits as RewardVal;

-                // N: number of visits to the parent node
-                // n: number of visits to the child node
-                // x_i: reward of the ith visit to the child node
-                // X: average reward of the child
-                // C: exploration constant
-                //
-                // UCB1-Tuned = X + C * sqrt(Ln(parent_visits) / child_visits * min(1/4, V_n)
-                //   V(n) = sum(x_i^2)/n - X^2 + sqrt(2*ln(N)/n)
-                let exploitation = reward_avg;
-                let mut variance = (child_rewards.iter().map(|&x| x * x).sum::<RewardVal>()
-                    / child_visits)
-                    - (reward_avg * reward_avg)
-                    + (2.0 * parent_visits.ln() / child_visits).sqrt();
-                if variance > 0.25 {
-                    variance = 0.25;
-                }
-                let exploration =
-                    exploration_constant * (parent_visits.ln() / child_visits * variance).sqrt();
-
-                exploitation + exploration
-            }
+        // N: number of visits to the parent node
+        // n: number of visits to the child node
+        // x_i: reward of the ith visit to the child node
+        // X: average reward of the child
+        // C: exploration constant
+        //
+        // UCB1-Tuned = X + C * sqrt(Ln(parent_visits) / child_visits * min(1/4, V_n)
+        //   V(n) = sum(x_i^2)/n - X^2 + sqrt(2*ln(N)/n)
+        let exploitation = reward_avg;
+        let mut variance = reward_variance + (2.0 * parent_visits.ln() / child_visits).sqrt();
+        if variance > 0.25 {
+            variance = 0.25;
        }
+        let exploration =
+            exploration_constant * (parent_visits.ln() / child_visits * variance).sqrt();
+
+        exploitation + exploration
    }
 }
@@ -69,10 +69,10 @@ impl<S: GameState> Node<S> {
        }
    }

-    pub fn rewards(&self, player: &S::Player) -> Option<&Vec<RewardVal>> {
+    pub fn reward_variance(&self, player: &S::Player) -> f64 {
        match self.player_view.get(player) {
-            Some(pv) => Some(&pv.rewards),
-            None => None,
+            Some(pv) => pv.weighted_variance / self.visits as f64,
+            None => 0.0,
        }
    }

@@ -82,9 +82,10 @@ impl<S: GameState> Node<S> {

    pub fn record_player_reward(&mut self, player: S::Player, reward: RewardVal) {
        let pv = self.player_view.entry(player).or_default();
-        pv.rewards.push(reward);
+        let prev_reward_average = pv.reward_average;
        pv.reward_sum += reward;
-        pv.reward_average = pv.reward_sum / pv.rewards.len() as f64;
+        pv.reward_average = pv.reward_sum / self.visits as f64;
+        pv.weighted_variance += (reward - prev_reward_average) * (reward - pv.reward_average);
    }
 }

@@ -98,8 +99,11 @@ pub struct PlayerRewardView {
    /// The average reward from simulations through this node, often called the node value
    pub reward_average: RewardVal,

-    /// The rewards we have gotten so far for simulations through this node
-    pub rewards: Vec<RewardVal>,
+    /// The weighted variance from simulations through this node
+    ///
+    /// This is used to calculate online sample variance.
+    /// See Donald E. Knuth. Seminumerical Algorithms, volume 2 of The Art of Computer Programming, chapter 4.2.2, page 232
+    pub weighted_variance: f64,
 }

 impl Default for PlayerRewardView {
@@ -107,7 +111,7 @@ impl Default for PlayerRewardView {
        PlayerRewardView {
            reward_sum: 0.0,
            reward_average: 0.0,
-            rewards: Vec::new(),
+            weighted_variance: 0.0,
        }
    }
 }
Author	SHA1	Message	Date
krugd	04a55f0a56	Add a "threads" feature for mutli-threading	2025-06-30 19:49:45 -07:00
krugd	a2711f341f	Cleanup debug print	2025-06-29 22:51:17 -07:00
krugd	48c93fa197	Renamed the benchmark file	2025-06-29 22:50:54 -07:00
krugd	854772a63d	Improve UCB1-Tuned performance We calculate the sample variance of the rewards online storing the value in the node. This greatly reduces the amount of summations that need to be done to calculate the variance during the selection phase. While this burdens other selection algorithms, the cost is not substantial.	2025-06-29 18:43:52 -07:00
krugd	44ef9ebdd8	Adding really stupid benchmark	2025-06-27 22:25:56 -07:00