quantrs2_ml/

reinforcement.rs

use crate::error::{MLError, Result};
use crate::qnn::QuantumNeuralNetwork;
use quantrs2_circuit::prelude::Circuit;
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::random::prelude::*;
use std::collections::HashMap;

/// Environment for reinforcement learning
pub trait Environment {
    /// Gets the current state
    fn state(&self) -> Array1<f64>;

    /// Gets the number of available actions
    fn num_actions(&self) -> usize;

    /// Takes an action and returns the reward and next state
    fn step(&mut self, action: usize) -> Result<(Array1<f64>, f64, bool)>;

    /// Resets the environment
    fn reset(&mut self) -> Array1<f64>;
}

/// Agent for reinforcement learning
pub trait QuantumAgent {
    /// Gets an action for a given state
    fn get_action(&self, state: &Array1<f64>) -> Result<usize>;

    /// Updates the agent based on a reward
    fn update(
        &mut self,
        state: &Array1<f64>,
        action: usize,
        reward: f64,
        next_state: &Array1<f64>,
        done: bool,
    ) -> Result<()>;

    /// Trains the agent on an environment
    fn train(&mut self, env: &mut dyn Environment, episodes: usize) -> Result<f64>;

    /// Evaluates the agent on an environment
    fn evaluate(&self, env: &mut dyn Environment, episodes: usize) -> Result<f64>;
}

/// Reinforcement learning algorithm type
#[derive(Debug, Clone, Copy)]
pub enum ReinforcementLearningType {
    /// Q-learning
    QLearning,

    /// SARSA
    SARSA,

    /// Deep Q-Network
    DQN,

    /// Policy Gradient
    PolicyGradient,

    /// Quantum Approximate Optimization Algorithm
    QAOA,
}

/// Reinforcement learning with quantum circuit
#[derive(Debug, Clone)]
pub struct ReinforcementLearning {
    /// Type of reinforcement learning algorithm
    rl_type: ReinforcementLearningType,

    /// Quantum neural network
    qnn: QuantumNeuralNetwork,

    /// Learning rate
    learning_rate: f64,

    /// Discount factor
    discount_factor: f64,

    /// Exploration rate
    exploration_rate: f64,

    /// Number of state dimensions
    state_dim: usize,

    /// Number of actions
    action_dim: usize,
}

impl ReinforcementLearning {
    /// Creates a new quantum reinforcement learning agent
    pub fn new() -> Self {
        // This is a placeholder implementation
        // In a real system, this would create a proper QNN

        let layers = vec![
            crate::qnn::QNNLayerType::EncodingLayer { num_features: 4 },
            crate::qnn::QNNLayerType::VariationalLayer { num_params: 16 },
            crate::qnn::QNNLayerType::EntanglementLayer {
                connectivity: "full".to_string(),
            },
            crate::qnn::QNNLayerType::VariationalLayer { num_params: 16 },
            crate::qnn::QNNLayerType::MeasurementLayer {
                measurement_basis: "computational".to_string(),
            },
        ];

        let qnn = QuantumNeuralNetwork::new(
            layers, 8, // 8 qubits
            4, // 4 input features
            2, // 2 output actions
        )
        .unwrap();

        ReinforcementLearning {
            rl_type: ReinforcementLearningType::QLearning,
            qnn,
            learning_rate: 0.01,
            discount_factor: 0.95,
            exploration_rate: 0.1,
            state_dim: 4,
            action_dim: 2,
        }
    }

    /// Sets the reinforcement learning algorithm type
    pub fn with_algorithm(mut self, rl_type: ReinforcementLearningType) -> Self {
        self.rl_type = rl_type;
        self
    }

    /// Sets the state dimension
    pub fn with_state_dimension(mut self, state_dim: usize) -> Self {
        self.state_dim = state_dim;
        self
    }

    /// Sets the action dimension
    pub fn with_action_dimension(mut self, action_dim: usize) -> Self {
        self.action_dim = action_dim;
        self
    }

    /// Sets the learning rate
    pub fn with_learning_rate(mut self, learning_rate: f64) -> Self {
        self.learning_rate = learning_rate;
        self
    }

    /// Sets the discount factor
    pub fn with_discount_factor(mut self, discount_factor: f64) -> Self {
        self.discount_factor = discount_factor;
        self
    }

    /// Sets the exploration rate
    pub fn with_exploration_rate(mut self, exploration_rate: f64) -> Self {
        self.exploration_rate = exploration_rate;
        self
    }

    /// Encodes a state into a quantum circuit
    fn encode_state(&self, state: &Array1<f64>) -> Result<Circuit<8>> {
        // This is a dummy implementation
        // In a real system, this would encode the state into a quantum circuit

        let mut circuit = Circuit::<8>::new();

        for i in 0..state.len().min(8) {
            circuit.ry(i, state[i] * std::f64::consts::PI)?;
        }

        Ok(circuit)
    }

    /// Gets the Q-values for a state
    fn get_q_values(&self, state: &Array1<f64>) -> Result<Array1<f64>> {
        // This is a dummy implementation
        // In a real system, this would compute Q-values using the QNN

        let mut q_values = Array1::zeros(self.action_dim);

        for i in 0..self.action_dim {
            q_values[i] = 0.5 + 0.5 * thread_rng().gen::<f64>();
        }

        Ok(q_values)
    }
}

impl QuantumAgent for ReinforcementLearning {
    fn get_action(&self, state: &Array1<f64>) -> Result<usize> {
        // Epsilon-greedy action selection
        if thread_rng().gen::<f64>() < self.exploration_rate {
            // Explore: random action
            Ok(fastrand::usize(0..self.action_dim))
        } else {
            // Exploit: best action
            let q_values = self.get_q_values(state)?;
            let mut best_action = 0;
            let mut best_value = q_values[0];

            for i in 1..self.action_dim {
                if q_values[i] > best_value {
                    best_value = q_values[i];
                    best_action = i;
                }
            }

            Ok(best_action)
        }
    }

    fn update(
        &mut self,
        _state: &Array1<f64>,
        _action: usize,
        _reward: f64,
        _next_state: &Array1<f64>,
        _done: bool,
    ) -> Result<()> {
        // This is a dummy implementation
        // In a real system, this would update the QNN

        Ok(())
    }

    fn train(&mut self, env: &mut dyn Environment, episodes: usize) -> Result<f64> {
        let mut total_reward = 0.0;

        for _ in 0..episodes {
            let mut state = env.reset();
            let mut episode_reward = 0.0;
            let mut done = false;

            while !done {
                let action = self.get_action(&state)?;
                let (next_state, reward, is_done) = env.step(action)?;

                self.update(&state, action, reward, &next_state, is_done)?;

                state = next_state;
                episode_reward += reward;
                done = is_done;
            }

            total_reward += episode_reward;
        }

        Ok(total_reward / episodes as f64)
    }

    fn evaluate(&self, env: &mut dyn Environment, episodes: usize) -> Result<f64> {
        let mut total_reward = 0.0;

        for _ in 0..episodes {
            let mut state = env.reset();
            let mut episode_reward = 0.0;
            let mut done = false;

            while !done {
                let action = self.get_action(&state)?;
                let (next_state, reward, is_done) = env.step(action)?;

                state = next_state;
                episode_reward += reward;
                done = is_done;
            }

            total_reward += episode_reward;
        }

        Ok(total_reward / episodes as f64)
    }
}

/// GridWorld environment for testing reinforcement learning
pub struct GridWorldEnvironment {
    /// Width of the grid
    width: usize,

    /// Height of the grid
    height: usize,

    /// Current position (x, y)
    position: (usize, usize),

    /// Goal position (x, y)
    goal: (usize, usize),

    /// Obstacle positions (x, y)
    obstacles: Vec<(usize, usize)>,
}

impl GridWorldEnvironment {
    /// Creates a new GridWorld environment
    pub fn new(width: usize, height: usize) -> Self {
        GridWorldEnvironment {
            width,
            height,
            position: (0, 0),
            goal: (width - 1, height - 1),
            obstacles: Vec::new(),
        }
    }

    /// Sets the goal position
    pub fn with_goal(mut self, x: usize, y: usize) -> Self {
        self.goal = (x.min(self.width - 1), y.min(self.height - 1));
        self
    }

    /// Sets the obstacles
    pub fn with_obstacles(mut self, obstacles: Vec<(usize, usize)>) -> Self {
        self.obstacles = obstacles;
        self
    }

    /// Checks if a position is an obstacle
    pub fn is_obstacle(&self, x: usize, y: usize) -> bool {
        self.obstacles.contains(&(x, y))
    }

    /// Checks if a position is the goal
    pub fn is_goal(&self, x: usize, y: usize) -> bool {
        (x, y) == self.goal
    }
}

impl Environment for GridWorldEnvironment {
    fn state(&self) -> Array1<f64> {
        let mut state = Array1::zeros(4);

        // Normalize position
        state[0] = self.position.0 as f64 / self.width as f64;
        state[1] = self.position.1 as f64 / self.height as f64;

        // Normalize goal
        state[2] = self.goal.0 as f64 / self.width as f64;
        state[3] = self.goal.1 as f64 / self.height as f64;

        state
    }

    fn num_actions(&self) -> usize {
        4 // Up, Right, Down, Left
    }

    fn step(&mut self, action: usize) -> Result<(Array1<f64>, f64, bool)> {
        // Calculate new position
        let (x, y) = self.position;
        let (new_x, new_y) = match action {
            0 => (x, y.saturating_sub(1)), // Up
            1 => (x + 1, y),               // Right
            2 => (x, y + 1),               // Down
            3 => (x.saturating_sub(1), y), // Left
            _ => {
                return Err(MLError::InvalidParameter(format!(
                    "Invalid action: {}",
                    action
                )))
            }
        };

        // Check if new position is valid
        let new_x = new_x.min(self.width - 1);
        let new_y = new_y.min(self.height - 1);

        // Check if new position is an obstacle
        if self.obstacles.contains(&(new_x, new_y)) {
            // Stay in the same position
            let reward = -1.0;
            let done = false;
            return Ok((self.state(), reward, done));
        }

        // Update position
        self.position = (new_x, new_y);

        // Calculate reward
        let reward = if (new_x, new_y) == self.goal {
            10.0 // Goal reached
        } else {
            -0.1 // Step penalty
        };

        // Check if done
        let done = (new_x, new_y) == self.goal;

        Ok((self.state(), reward, done))
    }

    fn reset(&mut self) -> Array1<f64> {
        self.position = (0, 0);
        self.state()
    }
}