simple-mcts 0.1.4

A Rust library providing a straightforward and configurable implementation of the Monte Carlo Tree Search (MCTS) algorithm. It's designed for easy integration into various game AI projects, supporting both single-instance and batch-processing MCTS simulations.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133

//! Test utilities for MCTS implementation

use crate::{Game, GameEvaluator};

/// A simple test game implementation
pub struct GameTest {
    state: Vec<i32>,
}

impl Game<4> for GameTest {
    type State = [i32; 4];

    fn new() -> Self {
        GameTest {
            state: Vec::<i32>::new(),
        }
    }

    fn get_actions(&self) -> [bool; 4] {
        std::array::from_fn(|index| !self.state.contains(&(index as i32)))
    }

    fn is_finish(&self) -> bool{
        self.state.len() == 4
    }

    fn play(&mut self, action: usize) {
        self.state.push(action as i32);
    }

    fn get_state(&self) -> Self::State {
        std::array::from_fn(|index| {
            if index < self.state.len() {
                self.state[index]
            } else {
                -1
            }
        })
    }

    fn get_result(&self) -> Option<f64> {
        if self.state.len() != 4 {
            None
        } else {
            let rel = (self.state[0] + self.state[2]) - (self.state[1] + self.state[3]);

            if rel == 0 {
                Some(0.0)
            } else if rel > 0 {
                Some(1.0)
            } else {
                Some(-1.0)
            }
        }
    }

    fn clone(&self) -> Self {
        GameTest {
            state: self.state.clone(),
        }
    }
}

/// A simple test evaluator implementation
pub struct GameEvaluatorTest;

impl GameEvaluatorTest {
    #[allow(dead_code)]
    pub fn new() -> Self {
        GameEvaluatorTest {}
    }
}

impl GameEvaluator<GameTest, 4> for GameEvaluatorTest {
    fn evaluate(&self, state: <GameTest as Game<4>>::State) -> (f64, [f64; 4]) {
        let last = state.into_iter().filter(|i| *i != -1).last();

        if let Some(last) = last {
            match last {
                0 => (-0.2, [0.41, 0.39, 0.09, 0.11]),
                1 => (0.2, [0.09, 0.11, 0.41, 0.39]),
                2 => (-0.9, [0.1, 0.12, 0.13, 0.65]),
                3 => (0.9, [0.13, 0.65, 0.1, 0.12]),
                _ => (0.0, [0.25, 0.25, 0.25, 0.25]),
            }
        } else {
            (0.0, [0.2, 0.7, 0.06, 0.04])
        }
    }
}

/// Another test evaluator implementation with different behavior
pub struct GameEvaluatorTest2;

impl GameEvaluatorTest2 {
    #[allow(dead_code)]
    pub fn new() -> Self {
        GameEvaluatorTest2 {}
    }
}

impl GameEvaluator<GameTest, 4> for GameEvaluatorTest2 {
    fn evaluate(&self, state: <GameTest as Game<4>>::State) -> (f64, [f64; 4]) {
        let last = state.into_iter().filter(|i| *i != -1).last();

        if let Some(last) = last {
            match last {
                0 => (0.5, [0.1, 0.15, 0.25, 0.5]),
                1 => (0.25, [0.1, 0.15, 0.25, 0.5]),
                2 => (-0.25, [0.1, 0.15, 0.25, 0.5]),
                3 => (-0.5, [0.1, 0.15, 0.25, 0.5]),
                _ => (0.0, [0.25, 0.25, 0.25, 0.25]),
            }
        } else {
            (0.0, [0.1, 0.15, 0.25, 0.5])
        }
    }
}

/// Utility function to compare float arrays with tolerance
///
/// # Parameters
/// - `a`: First array
/// - `b`: Second array
///
/// # Returns
/// `true` if all elements are approximately equal
#[allow(dead_code)]
pub fn compare_array<const N: usize>(a: &[f64; N], b: &[f64; N]) -> bool{
    (0..N).all(|index|{
        (a[index]-b[index]).abs() < 1e-8
    })
}