eucdist/
eucdist.rs

1/* This Source Code Form is subject to the terms of the Mozilla Public
2 * License, v. 2.0. If a copy of the MPL was not distributed with this
3 * file, You can obtain one at http://mozilla.org/MPL/2.0/. */
4
5use std::collections::HashMap;
6
7use rurel::mdp::{Agent, State};
8use rurel::strategy::explore::RandomExploration;
9use rurel::strategy::learn::QLearning;
10use rurel::strategy::terminate::FixedIterations;
11use rurel::AgentTrainer;
12
13#[derive(PartialEq, Eq, Hash, Clone)]
14struct MyState {
15    x: i32,
16    y: i32,
17    maxx: i32,
18    maxy: i32,
19}
20
21#[derive(PartialEq, Eq, Hash, Clone)]
22enum MyAction {
23    Move { dx: i32, dy: i32 },
24}
25
26impl State for MyState {
27    type A = MyAction;
28
29    fn reward(&self) -> f64 {
30        let (tx, ty) = (10, 10);
31        let d = (((tx - self.x).pow(2) + (ty - self.y).pow(2)) as f64).sqrt();
32        -d
33    }
34
35    fn actions(&self) -> Vec<MyAction> {
36        vec![
37            MyAction::Move { dx: -1, dy: 0 },
38            MyAction::Move { dx: 1, dy: 0 },
39            MyAction::Move { dx: 0, dy: -1 },
40            MyAction::Move { dx: 0, dy: 1 },
41        ]
42    }
43}
44
45struct MyAgent {
46    state: MyState,
47}
48
49impl Agent<MyState> for MyAgent {
50    fn current_state(&self) -> &MyState {
51        &self.state
52    }
53
54    fn take_action(&mut self, action: &MyAction) {
55        match action {
56            &MyAction::Move { dx, dy } => {
57                self.state = MyState {
58                    x: (((self.state.x + dx) % self.state.maxx) + self.state.maxx)
59                        % self.state.maxx,
60                    y: (((self.state.y + dy) % self.state.maxy) + self.state.maxy)
61                        % self.state.maxy,
62                    ..self.state.clone()
63                };
64            }
65        }
66    }
67}
68
69fn main() {
70    let initial_state = MyState {
71        x: 0,
72        y: 0,
73        maxx: 21,
74        maxy: 21,
75    };
76    let mut trainer = AgentTrainer::new();
77    let mut agent = MyAgent {
78        state: initial_state.clone(),
79    };
80    trainer.train(
81        &mut agent,
82        &QLearning::new(0.2, 0.01, 2.),
83        &mut FixedIterations::new(100000),
84        &RandomExploration::new(),
85    );
86    for j in 0..21 {
87        for i in 0..21 {
88            let entry: &HashMap<MyAction, f64> = trainer
89                .expected_values(&MyState {
90                    x: i,
91                    y: j,
92                    ..initial_state
93                })
94                .unwrap();
95            let best_action = entry
96                .iter()
97                .max_by(|(_, v1), (_, v2)| v1.partial_cmp(v2).unwrap())
98                .map(|(v, _)| v)
99                .unwrap();
100            match best_action {
101                MyAction::Move { dx: -1, dy: 0 } => print!("<"),
102                MyAction::Move { dx: 1, dy: 0 } => print!(">"),
103                MyAction::Move { dx: 0, dy: -1 } => print!("^"),
104                MyAction::Move { dx: 0, dy: 1 } => print!("v"),
105                _ => unreachable!(),
106            };
107        }
108        println!();
109    }
110}
eucdist/eucdist.rs

eucdist/
eucdist.rs
