much refactor

.gitignore

@@ -1,2 +1,4 @@
 /target
 **/*.rs.bk
+flamegraph.svg
+perf.*

src/actors/genetic.rs

@@ -2,12 +2,14 @@
 
 use super::{Actor, State};
 use crate::{
-    game::Action,
+    cli::Train,
+    game::{Action, Controllable, Game},
     playfield::{PLAYFIELD_HEIGHT, PLAYFIELD_WIDTH},
 };
 use rand::rngs::SmallRng;
-use rand::Rng;
+use rand::{seq::SliceRandom, Rng, SeedableRng};
 
+#[derive(Copy, Clone, Debug)]
 pub struct Parameters {
     total_height: f64,
     bumpiness: f64,
@@ -57,6 +59,7 @@ impl Parameters {
     }
 }
 
+#[derive(Clone, Copy)]
 pub struct GeneticHeuristicAgent {
     params: Parameters,
 }
@@ -115,40 +118,101 @@ impl GeneticHeuristicAgent {
         }
     }
 
-    fn get_heuristic(&self, state: &State, action: &Action) -> f64 {
+    fn get_heuristic(&self, game: &Game, action: &Action) -> f64 {
         todo!();
     }
+
+    pub fn breed(&self, self_fitness: u32, other: &Self, other_fitness: u32) -> Self {
+        let weight = (self_fitness + other_fitness) as f64;
+        let self_weight = self_fitness as f64 / weight;
+        let other_weight = other_fitness as f64 / weight;
+        Self {
+            params: Parameters {
+                total_height: self.params.total_height * self_weight
+                    + other.params.total_height * other_weight,
+                bumpiness: self.params.total_height * self_weight
+                    + other.params.total_height * other_weight,
+                holes: self.params.total_height * self_weight
+                    + other.params.total_height * other_weight,
+                complete_lines: self.params.total_height * self_weight
+                    + other.params.total_height * other_weight,
+            },
+        }
+    }
+
+    fn mutate(&mut self, rng: &mut SmallRng) {
+        self.params.mutate(rng);
+    }
 }
 
 impl Actor for GeneticHeuristicAgent {
-    fn get_action(&self, rng: &mut SmallRng, state: &State, legal_actions: &[Action]) -> Action {
+    fn get_action(&self, _: &mut SmallRng, game: &Game, legal_actions: &[Action]) -> Action {
         *legal_actions
             .iter()
-            .map(|action| (action, self.get_heuristic(state, action)))
-            .max_by_key(|(action, heuristic)| (heuristic * 1_000_00.0) as usize)
+            .map(|action| (action, self.get_heuristic(game, action)))
+            .max_by_key(|(_, heuristic)| (heuristic * 1_000_00.0) as usize)
             .unwrap()
             .0
     }
-    fn update(
-        &mut self,
-        state: State,
-        action: Action,
-        next_state: State,
-        next_legal_actions: &[Action],
-        reward: f64,
-    ) {
-        unimplemented!()
-    }
-    fn set_learning_rate(&mut self, learning_rate: f64) {
-        unimplemented!()
-    }
-    fn set_exploration_prob(&mut self, exploration_prob: f64) {
-        unimplemented!()
-    }
-    fn set_discount_rate(&mut self, discount_rate: f64) {
-        unimplemented!()
-    }
+
     fn dbg(&self) {
-        unimplemented!()
+        // unimplemented!()
     }
 }
+
+pub fn train_actor(opts: &Train) -> Box<dyn Actor> {
+    let mut rng = SmallRng::from_entropy();
+    let mut population = vec![(GeneticHeuristicAgent::default(), 0); 1000];
+
+    for _ in 0..opts.episodes {
+        let mut new_population: Vec<(GeneticHeuristicAgent, u32)> = population
+            .iter()
+            .map(|(agent, _)| {
+                let mut fitness = 0;
+                for _ in 0..100 {
+                    let mut game = Game::default();
+                    game.set_piece_limit(500);
+                    while (&game).is_game_over().is_none() {
+                        super::apply_action_to_game(
+                            agent.get_action(
+                                &mut rng,
+                                &game.clone().into(),
+                                &game.get_legal_actions(),
+                            ),
+                            &mut game,
+                        )
+                    }
+                    fitness += game.line_clears;
+                }
+                (*agent, fitness)
+            })
+            .collect::<Vec<_>>();
+
+        let mut breeded_population: Vec<(GeneticHeuristicAgent, u32)> = Vec::with_capacity(300);
+        for _ in 0..3 {
+            let mut random_selection = new_population
+                .choose_multiple(&mut rng, 100)
+                .collect::<Vec<_>>();
+            random_selection.sort_unstable_by(|e1, e2| e1.1.cmp(&e2.1));
+            let best_two = random_selection.iter().rev().take(2).collect::<Vec<_>>();
+            let parent1 = best_two[0];
+            let parent2 = best_two[1];
+            for _ in 0..100 {
+                let breeded = parent1.0.breed(parent1.1, &parent2.0, parent2.1);
+                let mut cloned = breeded.clone();
+                if rng.gen::<f64>() < 0.05 {
+                    cloned.mutate(&mut rng);
+                }
+                breeded_population.push((cloned, 0));
+            }
+        }
+
+        new_population.sort_unstable_by_key(|e| e.1);
+
+        new_population.splice(..breeded_population.len(), breeded_population);
+
+        population = new_population;
+    }
+    population.sort_unstable_by_key(|e| e.1);
+    Box::new(population.iter().rev().next().unwrap().0)
+}

src/actors/mod.rs

@@ -40,20 +40,7 @@ impl From<PlayField> for State {
 }
 
 pub trait Actor {
-    fn get_action(&self, rng: &mut SmallRng, state: &State, legal_actions: &[Action]) -> Action;
-
-    fn update(
-        &mut self,
-        state: State,
-        action: Action,
-        next_state: State,
-        next_legal_actions: &[Action],
-        reward: f64,
-    );
-
-    fn set_learning_rate(&mut self, learning_rate: f64);
-    fn set_exploration_prob(&mut self, exploration_prob: f64);
-    fn set_discount_rate(&mut self, discount_rate: f64);
+    fn get_action(&self, rng: &mut SmallRng, game: &Game, legal_actions: &[Action]) -> Action;
 
     fn dbg(&self);
 }
@@ -80,3 +67,16 @@ impl Predictable for Game {
         game
     }
 }
+
+pub fn apply_action_to_game(action: Action, game: &mut Game) {
+    match action {
+        Action::Nothing => (),
+        Action::MoveLeft => game.move_left(),
+        Action::MoveRight => game.move_right(),
+        Action::SoftDrop => game.move_down(),
+        Action::HardDrop => game.hard_drop(),
+        Action::Hold => game.hold(),
+        Action::RotateLeft => game.rotate_left(),
+        Action::RotateRight => game.rotate_right(),
+    }
+}

src/actors/qlearning.rs

@@ -12,6 +12,21 @@ use rand::Rng;
 use rand::SeedableRng;
 use std::collections::HashMap;
 
+pub trait QLearningActor: Actor {
+    fn update(
+        &mut self,
+        state: State,
+        action: Action,
+        next_state: State,
+        next_legal_actions: &[Action],
+        reward: f64,
+    );
+
+    fn set_learning_rate(&mut self, learning_rate: f64);
+    fn set_exploration_prob(&mut self, exploration_prob: f64);
+    fn set_discount_rate(&mut self, discount_rate: f64);
+}
+
 pub struct QLearningAgent {
     pub learning_rate: f64,
     pub exploration_prob: f64,
@@ -64,14 +79,20 @@ impl QLearningAgent {
 impl Actor for QLearningAgent {
     // Because doing (Nothing) is in the set of legal actions, this will never
     // be empty
-    fn get_action(&self, rng: &mut SmallRng, state: &State, legal_actions: &[Action]) -> Action {
+    fn get_action(&self, rng: &mut SmallRng, game: &Game, legal_actions: &[Action]) -> Action {
         if rng.gen::<f64>() < self.exploration_prob {
             *legal_actions.choose(rng).unwrap()
         } else {
-            self.get_action_from_q_values(state, legal_actions)
+            self.get_action_from_q_values(&game.into(), legal_actions)
         }
     }
 
+    fn dbg(&self) {
+        debug!("Total states: {}", self.q_values.len());
+    }
+}
+
+impl QLearningActor for QLearningAgent {
     fn update(
         &mut self,
         state: State,
@@ -94,10 +115,6 @@ impl Actor for QLearningAgent {
             .insert(action, new_q_val);
     }
 
-    fn dbg(&self) {
-        debug!("Total states: {}", self.q_values.len());
-    }
-
     fn set_learning_rate(&mut self, learning_rate: f64) {
         self.learning_rate = learning_rate;
     }
@@ -208,14 +225,20 @@ impl ApproximateQLearning {
 }
 
 impl Actor for ApproximateQLearning {
-    fn get_action(&self, rng: &mut SmallRng, state: &State, legal_actions: &[Action]) -> Action {
+    fn get_action(&self, rng: &mut SmallRng, game: &Game, legal_actions: &[Action]) -> Action {
         if rng.gen::<f64>() < self.exploration_prob {
             *legal_actions.choose(rng).unwrap()
         } else {
-            self.get_action_from_q_values(state, legal_actions)
+            self.get_action_from_q_values(&game.into(), legal_actions)
         }
     }
 
+    fn dbg(&self) {
+        dbg!(&self.weights);
+    }
+}
+
+impl QLearningActor for ApproximateQLearning {
     fn update(
         &mut self,
         state: State,
@@ -248,15 +271,15 @@ impl Actor for ApproximateQLearning {
     fn set_discount_rate(&mut self, discount_rate: f64) {
         self.discount_rate = discount_rate;
     }
-
-    fn dbg(&self) {
-        dbg!(&self.weights);
-    }
 }
 
-pub fn train_actor(episodes: usize, mut actor: Box<dyn Actor>, opts: &Train) -> Box<dyn Actor> {
+pub fn train_actor<T: 'static + QLearningActor + Actor>(
+    mut actor: T,
+    opts: &Train,
+) -> Box<dyn Actor> {
     let mut rng = SmallRng::from_entropy();
     let mut avg = 0.0;
+    let episodes = opts.episodes;
 
     actor.set_learning_rate(opts.learning_rate);
     actor.set_discount_rate(opts.discount_rate);
@@ -274,20 +297,11 @@ pub fn train_actor(episodes: usize, mut actor: Box<dyn Actor>, opts: &Train) ->
         }
         let mut game = Game::default();
         while (&game).is_game_over().is_none() {
-            let cur_state = (&game).into();
+            let cur_state = game.clone();
             let cur_score = game.score();
             let action = actor.get_action(&mut rng, &cur_state, &((&game).get_legal_actions()));
 
-            match action {
-                Action::Nothing => (),
-                Action::MoveLeft => game.move_left(),
-                Action::MoveRight => game.move_right(),
-                Action::SoftDrop => game.move_down(),
-                Action::HardDrop => game.hard_drop(),
-                Action::Hold => game.hold(),
-                Action::RotateLeft => game.rotate_left(),
-                Action::RotateRight => game.rotate_right(),
-            }
+            super::apply_action_to_game(action, &mut game);
 
             let new_state = (&game).into();
             let mut reward = game.score() as f64 - cur_score as f64;
@@ -301,7 +315,13 @@ pub fn train_actor(episodes: usize, mut actor: Box<dyn Actor>, opts: &Train) ->
 
             let new_legal_actions = game.get_legal_actions();
 
-            actor.update(cur_state, action, new_state, &new_legal_actions, reward);
+            actor.update(
+                cur_state.into(),
+                action,
+                new_state,
+                &new_legal_actions,
+                reward,
+            );
 
             game.tick();
         }
@@ -316,6 +336,5 @@ pub fn train_actor(episodes: usize, mut actor: Box<dyn Actor>, opts: &Train) ->
     if opts.no_learn_during_evaluation {
         actor.set_learning_rate(0.0);
     }
-
-    actor
+    Box::new(actor)
 }

src/cli.rs

@@ -74,6 +74,7 @@ arg_enum! {
     pub enum Agent {
         QLearning,
         ApproximateQLearning,
+        HeuristicGenetic
     }
 }
 

src/game.rs

@@ -16,6 +16,7 @@ const LINE_CLEAR_DELAY: u64 = TICKS_PER_SECOND as u64 * 41 / 60;
 pub enum LossReason {
     TopOut,
     LockOut,
+    PieceLimitReached,
     BlockOut(Position),
 }
 
@@ -35,6 +36,9 @@ pub struct Game {
     /// bonus is needed.
     last_clear_action: ClearAction,
     pub line_clears: u32,
+    // used if we set a limit on how long a game can last.
+    pieces_placed: usize,
+    piece_limit: usize,
 }
 
 impl fmt::Debug for Game {
@@ -59,6 +63,8 @@ impl Default for Game {
             is_game_over: None,
             last_clear_action: ClearAction::Single, // Doesn't matter what it's initialized to
             line_clears: 0,
+            pieces_placed: 0,
+            piece_limit: 0,
         }
     }
 }
@@ -168,10 +174,18 @@ impl Game {
         // It's possible that the player moved the piece in the meantime.
         if !self.playfield.can_active_piece_move_down() {
             let positions = self.playfield.lock_active_piece();
+            if self.pieces_placed < self.piece_limit {
+                self.pieces_placed += 1;
+                if self.pieces_placed >= self.piece_limit {
+                    trace!("Loss due to piece limit!");
+                    self.is_game_over = Some(LossReason::PieceLimitReached);
+                }
+            }
+
             self.is_game_over = self.is_game_over.or_else(|| {
                 if positions.iter().map(|p| p.y).all(|y| y < 20) {
-                    trace!("Loss due to topout! {:?}", positions);
-                    Some(LossReason::TopOut)
+                    trace!("Loss due to lockout! {:?}", positions);
+                    Some(LossReason::LockOut)
                 } else {
                     None
                 }
@@ -183,6 +197,7 @@ impl Game {
                 self.line_clears += cleared_lines as u32;
                 self.score += (cleared_lines * 100 * self.level as usize) as u32;
                 self.level = (self.line_clears / 10) as u8;
+
                 self.playfield.active_piece = None;
                 self.next_spawn_tick = self.tick + LINE_CLEAR_DELAY;
             } else {
@@ -195,6 +210,10 @@ impl Game {
         }
     }
 
+    pub fn set_piece_limit(&mut self, size: usize) {
+        self.piece_limit = size;
+    }
+
     pub fn playfield(&self) -> &PlayField {
         &self.playfield
     }

src/main.rs

@@ -30,18 +30,15 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
     init_verbosity(&opts)?;
 
     let agent = match opts.subcmd {
-        SubCommand::Play(sub_opts) => None,
+        SubCommand::Play(_) => None,
         SubCommand::Train(sub_opts) => Some(match sub_opts.agent {
-            Agent::QLearning => qlearning::train_actor(
-                sub_opts.episodes,
-                Box::new(qlearning::QLearningAgent::default()),
-                &sub_opts,
-            ),
-            Agent::ApproximateQLearning => qlearning::train_actor(
-                sub_opts.episodes,
-                Box::new(qlearning::ApproximateQLearning::default()),
-                &sub_opts,
-            ),
+            Agent::QLearning => {
+                qlearning::train_actor(qlearning::QLearningAgent::default(), &sub_opts)
+            }
+            Agent::ApproximateQLearning => {
+                qlearning::train_actor(qlearning::ApproximateQLearning::default(), &sub_opts)
+            }
+            Agent::HeuristicGenetic => genetic::train_actor(&sub_opts),
         }),
     };
 
@@ -70,7 +67,7 @@ async fn play_game(mut actor: Option<Box<dyn Actor>>) -> Result<(), Box<dyn std:
             None => (),
         }
 
-        let cur_state = (&game).into();
+        let cur_state = game.clone();
 
         // If there's an actor, the player action will get overridden. If not,
         // then then the player action falls through, if there is one. This is