my code:
```
// This project simulates a simplified version of a poker game environment including
// player actions, card dealings, and hand evaluation. The simulation is set up to support
// reinforcement learning agents by providing a detailed game state observation.

// Imports for randomness and formatting
use rand::prelude::*;
use std::fmt;

// Constants for the game
const NUM_PLAYERS: usize = 9;       // Number of players in the game
const NUM_CARDS: usize = 52;        // Number of cards in a standard deck
const NUM_RANKS: usize = 13;        // Number of ranks in a standard deck
const NUM_SUITS: usize = 4;         // Number of suits in a standard deck
const MAX_STACK: u32 = 10000;       // Initial stack size for each player
const SMALL_BLIND: u32 = 50;        // Small blind bet
const BIG_BLIND: u32 = 100;         // Big blind bet

// Enumeration for player actions
#[derive(Clone, Copy, Debug)]
enum Action {
    Fold,   // Player gives up their hand
    Call,   // Player matches the current bet
    Raise,  // Player increases the current bet
}

// Structure to represent a playing card with rank and suit
#[derive(Clone, Copy, Debug)]
struct Card {
    rank: usize,
    suit: usize,
}

impl Card {
    // Creates a new Card based on an integer ID
    fn new(id: usize) -> Card {
        Card {
            suit: id / NUM_RANKS,
            rank: id % NUM_RANKS,
        }
    }
}

// Display formatting for Card, allowing cards to be printed as strings
impl fmt::Display for Card {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        let rank_strings = ["2", "3", "4", "5", "6", "7", "8", "9", "T", "J", "Q", "K", "A"];
        let suit_strings = ["h", "d", "c", "s"];
        write!(f, "{}{}", rank_strings[self.rank], suit_strings[self.suit])
    }
}

// Deck of cards management (shuffling and dealing)
struct Deck {
    cards: Vec<Card>,
    rng: ThreadRng,
}

impl Deck {
    // Creates a new shuffled deck
    fn new() -> Deck {
        let mut deck = Deck {
            cards: Vec::with_capacity(NUM_CARDS),
            rng: thread_rng(),
        };
        deck.generate_deck();
        deck.shuffle();
        deck
    }

    // Generates a standard deck of 52 cards
    fn generate_deck(&mut self) {
        self.cards.clear();
        for i in 0..NUM_CARDS {
            self.cards.push(Card::new(i));
        }
    }

    // Shuffles the deck
    fn shuffle(&mut self) {
        self.cards.shuffle(&mut self.rng);
    }

    // Deals a single card from the deck
    fn deal(&mut self) -> Card {
        self.cards.pop().expect("No more cards in the deck")
    }

    // Resets the deck to a shuffled full deck
    fn reset(&mut self) {
        self.generate_deck();
        self.shuffle();
    }
}

// Hand evaluation using quinary method
struct HandEvaluator {
    combinations: [[u32; 8]; 14], // Combination table using u32 for compatibility
}

impl HandEvaluator {
    // Initializes a hand evaluator with precomputed combinations
    fn new() -> Self {
        let combinations = Self::precompute_combinations();
        HandEvaluator { combinations }
    }

    // Precomputes combination table for hand evaluation
    fn precompute_combinations() -> [[u32; 8]; 14] {
        let mut combinations = [[0; 8]; 14];
        for i in 0..14 {
            for j in 0..=i.min(7) {
                if j == 0 || j == i {
                    combinations[i][j] = 1;
                } else {
                    combinations[i][j] = combinations[i - 1][j] + combinations[i - 1][j - 1];
                }
            }
        }
        combinations
    }

    // Converts a hand into a quinary representation
    fn quinary_from_hand(cards: &[Card]) -> [u8; NUM_RANKS] {
        let mut quinary = [0u8; NUM_RANKS];
        for card in cards {
            quinary[card.rank] += 1;
        }
        quinary
    }

    // Evaluates a hand using its quinary representation
    fn evaluate_hand(&self, hand: &[Card], community_cards: &[Card]) -> u32 {
        let mut all_cards = hand.to_vec();
        all_cards.extend_from_slice(community_cards);
        let quinary = Self::quinary_from_hand(&all_cards);
        self.hash_quinary(&quinary)
    }

    // Computes the hash for a quinary representation
    fn hash_quinary(&self, quinary: &[u8; NUM_RANKS]) -> u32 {
    let mut hash = 0;
    let mut remaining_k = 7; // Use usize for the number of cards in the hand

    for (i, &count) in quinary.iter().enumerate() {
        if remaining_k >= i {
            hash += self.combinations[NUM_RANKS - 1 - i][remaining_k];
        }
        remaining_k = remaining_k.saturating_sub(count as usize); // Use saturating_sub
    }
    hash
}
}

// Representation of a Player in the game
struct Player {
    stack: u32,        // Player's current stack
    bet: u32,          // Player's current bet in the round
    active: bool,      // Is the player still active in the round
    all_in: bool,      // Is the player all-in
    hand: Vec<Card>,   // Player's hand (2 cards)
}

impl Player {
    // Creates a new player with an initial stack
    fn new(stack: u32) -> Player {
        Player {
            stack,
            bet: 0,
            active: true,
            all_in: false,
            hand: Vec::with_capacity(2),
        }
    }

    // Resets the player's state for a new round
    fn reset(&mut self) {
        self.bet = 0;
        self.active = true;
        self.all_in = false;
        self.hand.clear();
    }
}

// Comprehensive game state for RL agents
#[derive(Debug, Clone)]
struct GameState {
    player_hands: Vec<Vec<Card>>, // Each player's hole cards
    community_cards: Vec<Card>,   // The shared community cards
    player_stacks: Vec<u32>,      // Each player's current stack
    player_bets: Vec<u32>,        // Each player's current bet
    pot: u32,                     // Current size of the pot
    current_player: usize,        // Index of the current player
    phase: Phase,                 // Current phase of the game
}

// RL-compatible observation structure
struct RLObservation {
    state: GameState, // Detailed game state
    min_call: u32,    // Minimum amount needed to call
}

// The core environment managing the game state and progression
struct PokerEnvironment<F>
where
    F: Fn(usize, &RLObservation) -> Action,
{
    num_players: usize,   // Number of players in the game
    players: Vec<Player>, // Vector of players
    deck: Deck,           // Deck of cards
    pot: u32,             // Total pot size
    min_call: u32,        // Minimum amount to call in the current bet
    last_raise: u32,      // Last raised amount
    current_player: usize,// The index of the current player in action
    dealer_position: usize, // The index of the dealer
    phase: Phase,         // Current phase of the game
    betting_round_over: bool, // Is the current betting round over?
    community_cards: Vec<Card>, // Community cards visible to all players
    last_aggressor: Option<usize>, // Last player to make an aggressive action
    agent_function: F,    // Function determining player actions
    evaluator: HandEvaluator, // Hand evaluator with precomputed combinations
}

// Enumeration of possible game phases
#[derive(Clone, Copy, Debug, PartialEq)]
enum Phase {
    PreFlop,
    Flop,
    Turn,
    River,
    Showdown,
}

impl<F> PokerEnvironment<F>
where
    F: Fn(usize, &RLObservation) -> Action,
{
    // Initializes a new poker game environment
    fn new(num_players: usize, agent_function: F) -> PokerEnvironment<F> {
        let evaluator = HandEvaluator::new(); // Create evaluator with precomputed values
        let mut env = PokerEnvironment {
            num_players,
            players: Vec::with_capacity(num_players),
            deck: Deck::new(),
            pot: 0,
            min_call: 0,
            last_raise: 0,
            current_player: 0,
            dealer_position: 0,
            phase: Phase::PreFlop,
            betting_round_over: false,
            community_cards: Vec::new(),
            last_aggressor: None,
            agent_function,
            evaluator,
        };
        env.initialize_game();
        env
    }

    // Sets up a new game with initial states and conditions
    fn initialize_game(&mut self) {
        self.players.clear();
        for _ in 0..self.num_players {
            self.players.push(Player::new(MAX_STACK));
        }

        self.deck.reset();

        for player in &mut self.players {
            player.reset();
            player.hand.push(self.deck.deal());
            player.hand.push(self.deck.deal());
        }

        self.pot = 0;
        self.min_call = BIG_BLIND;
        self.last_raise = BIG_BLIND;
        self.current_player = (self.dealer_position + 3) % self.num_players;
        self.phase = Phase::PreFlop;
        self.betting_round_over = false;
        self.community_cards.clear();
        self.last_aggressor = None;

        let small_blind_position = (self.dealer_position + 1) % self.num_players;
        let big_blind_position = (self.dealer_position + 2) % self.num_players;
        self.post_blind(small_blind_position, SMALL_BLIND);
        self.post_blind(big_blind_position, BIG_BLIND);
    }

    // Runs a single hand of poker, progressing through rounds and phases
    fn play_single_hand(&mut self) {
        while !self.is_terminal() {
            let player_id = self.current_player;
            let player = &self.players[player_id];
            if player.active && !player.all_in {
                let observation = self.get_rl_observation();
                let action = (self.agent_function)(player_id, &observation);
                self.step(action);
            } else {
                self.next_player();
            }
        }
        self.resolve_hand();
    }

    // Generates a detailed observation for RL agents
    fn get_rl_observation(&self) -> RLObservation {
        RLObservation {
            state: GameState {
                player_hands: self.players.iter().map(|p| p.hand.clone()).collect(),
                community_cards: self.community_cards.clone(),
                player_stacks: self.players.iter().map(|p| p.stack).collect(),
                player_bets: self.players.iter().map(|p| p.bet).collect(),
                pot: self.pot,
                current_player: self.current_player,
                phase: self.phase,
            },
            min_call: self.min_call,
        }
    }

    // Processes a player's action and updates the game state
    fn step(&mut self, action: Action) {
        let player_id = self.current_player;
        let player = &mut self.players[player_id];
        if !player.active || player.all_in {
            self.next_player();
            return;
        }

        match action {
            Action::Fold => self.handle_fold(player_id),
            Action::Call => self.handle_call(player_id),
            Action::Raise => self.handle_raise(player_id),
        }

        if self.betting_round_over {
            self.advance_phase();
        }

        if !self.is_terminal() {
            self.next_player();
        }
    }

    // Checks if the hand is at a terminal state (finished)
    fn is_terminal(&self) -> bool {
        let active_count = self.players.iter().filter(|p| p.active || p.all_in).count();
        active_count <= 1 || self.phase == Phase::Showdown
    }

    // Handles the fold action by a player
    fn handle_fold(&mut self, player_id: usize) {
        let player = &mut self.players[player_id];
        player.active = false;
        self.pot += player.bet;
        player.bet = 0;
    }

    // Handles the call action, matching a player's bet to the minimum call
    fn handle_call(&mut self, player_id: usize) {
        let player = &mut self.players[player_id];
        let call_amount = self.min_call - player.bet;
        let amount = std::cmp::min(call_amount, player.stack);
        player.bet += amount;
        player.stack -= amount;
        if player.stack == 0 {
            player.all_in = true;
        }
    }

    // Handles the raise action, increasing a player's bet
    fn handle_raise(&mut self, player_id: usize) {
        let player = &mut self.players[player_id];
        let raise_amount = std::cmp::max(self.last_raise * 2, self.min_call * 2);
        let amount = std::cmp::min(raise_amount, player.stack);
        player.bet += amount;
        player.stack -= amount;
        self.min_call = player.bet;
        self.last_raise = amount;
        self.last_aggressor = Some(player_id);
        if player.stack == 0 {
            player.all_in = true;
        }
    }

    // Advances the current player to the next one in line
    fn next_player(&mut self) {
        let start_player = self.current_player;
        loop {
            self.current_player = (self.current_player + 1) % self.num_players;
            if self.current_player == start_player {
                self.betting_round_over = true;
                break;
            }
            let player = &self.players[self.current_player];
            if player.active && !player.all_in {
                break;
            }
        }

        if let Some(last_aggressor) = self.last_aggressor {
            if self.current_player == last_aggressor {
                self.betting_round_over = true;
            }
        }
    }

    // Progresses the game to the next phase
    fn advance_phase(&mut self) {
        for player in &mut self.players {
            self.pot += player.bet;
            player.bet = 0;
        }
        self.min_call = 0;
        self.last_raise = 0;
        self.betting_round_over = false;
        self.last_aggressor = None;

        self.phase = match self.phase {
            Phase::PreFlop => {
                self.community_cards.push(self.deck.deal());
                self.community_cards.push(self.deck.deal());
                self.community_cards.push(self.deck.deal());
                Phase::Flop
            }
            Phase::Flop => {
                self.community_cards.push(self.deck.deal());
                Phase::Turn
            }
            Phase::Turn => {
                self.community_cards.push(self.deck.deal());
                Phase::River
            }
            Phase::River => Phase::Showdown,
            Phase::Showdown => Phase::Showdown,
        };

        self.current_player = (self.dealer_position + 1) % self.num_players;
        while {
            let player = &self.players[self.current_player];
            (!player.active || player.all_in) && self.current_player != self.dealer_position
        } {
            self.current_player = (self.current_player + 1) % self.num_players;
        }
    }

    // Resolves the end of a hand, determining winners and distributing the pot
    fn resolve_hand(&mut self) {
        let active_players: Vec<_> = self
            .players
            .iter()
            .enumerate()
            .filter(|(_, p)| p.active || p.all_in)
            .collect();

        if active_players.len() == 1 {
            let (winner_id, _) = active_players[0];
            let winner = &mut self.players[winner_id];
            winner.stack += self.pot;
            println!(
                "Player {} wins the pot of {} by default.",
                winner_id, self.pot
            );
            return;
        }

        let mut best_rank: Option<u32> = None;
        let mut winners = vec![];
        for (player_id, player) in active_players {
            let rank = self.evaluator.evaluate_hand(&player.hand, &self.community_cards);
            if best_rank.is_none() || rank < best_rank.unwrap() {
                best_rank = Some(rank);
                winners.clear();
                winners.push(player_id);
            } else if rank == best_rank.unwrap() {
                winners.push(player_id);
            }
        }

        let split_pot = self.pot / winners.len() as u32;
        for &winner_id in &winners {
            let winner = &mut self.players[winner_id];
            winner.stack += split_pot;
            println!(
                "Player {} wins {} with hand rank {}.",
                winner_id,
                split_pot,
                best_rank.unwrap()
            );
        }
    }

    // Posts a blind bet for a specified player
    fn post_blind(&mut self, player_id: usize, amount: u32) {
        let player = &mut self.players[player_id];
        let blind_amount = std::cmp::min(amount, player.stack);
        player.bet += blind_amount;
        player.stack -= blind_amount;
        self.pot += blind_amount;
        self.min_call = std::cmp::max(self.min_call, player.bet);
        if player.stack == 0 {
            player.all_in = true;
        }
    }
}

// Function defining a random player action
fn random_agent_function(_player_id: usize, _observation: &RLObservation) -> Action {
    let mut rng = thread_rng();
    match rng.gen_range(0..3) {
        0 => Action::Fold,
        1 => Action::Call,
        _ => Action::Raise,
    }
}

// Main function initializes the game and triggers the simulation
fn main() {
    // Use the random agent function
    let agent_function = random_agent_function;
    let mut env = PokerEnvironment::new(NUM_PLAYERS, agent_function);
    env.play_single_hand();
    println!("Hand simulation completed.");
}
```

Now I should add neuroevolution:
Streamlined Design Plan for 9-Max NLTH Poker AI Using Neuroevolution
1. Define Objectives and Requirements

    Objective: Develop a neuroevolution-based AI capable of playing 9-max NLTH poker, adapting strategies effectively across tables with 2 to 9 players.
    Performance Metrics: Evaluate success using Big Blinds per Hand (BB/hand) to measure strategic effectiveness in self-play scenarios.

2. Environment Setup

    Game Simulation Engine: Use the existing Rust-based poker environment to simulate 9-max NLTH games.
    State Representation: Ensure that the state includes critical game elements like player positions, community cards, pot size, actions, and stack sizes, suitable for various player counts.

3. Neural Network Design

    Initial Architecture: Begin with small, randomly configured neural networks with variability in layers and neurons.
    Architecture Evolution: Allow the architecture to evolve throughout the process, adapting to strategic complexity based on player count and interactions.

4. Neuroevolution Components

    Fitness Evaluation:
        Conduct games where networks compete against each other in configurations of 2 to 9 players.
        Measure performance using the average Big Blinds per Hand (BB/hand) metric over a set number of hands (N).

    Selection:
        Rank networks based on BB/hand scores.
        Use truncation selection to retain the top N networks for creating the next generation.

    Genetic Operators:
        Crossover: Combine top networks to produce offspring, integrating both structural and weight parameters.
        Mutation: Randomly alter components of the network architecture, such as adding/removing neurons or layers.

5. Training Loop

    Initial Population: Generate a diverse initial set of networks, each with unique small architectures.
    Generational Cycle:
        Evaluate each network's BB/hand performance across tables with different player counts.
        Select top-performing networks using truncation selection.
        Apply crossover and mutation to create new networks from the selected top performers.
        Replace the old population with these newly generated networks.
        Continue the process iteratively over many generations, refining strategies and network configurations.

Help me