quantrs2_ml/

continuous_rl.rs

//! Quantum Reinforcement Learning with Continuous Actions
//!
//! This module extends quantum reinforcement learning to support continuous action spaces,
//! implementing algorithms like DDPG, TD3, and SAC adapted for quantum circuits.

use crate::autodiff::optimizers::Optimizer;
use crate::error::{MLError, Result};
use crate::optimization::OptimizationMethod;
use crate::qnn::{QNNLayerType, QuantumNeuralNetwork};
use ndarray::{Array1, Array2, ArrayView1};
use quantrs2_circuit::builder::{Circuit, Simulator};
use quantrs2_core::gate::{
    single::{RotationX, RotationY, RotationZ},
    GateOp,
};
use quantrs2_sim::statevector::StateVectorSimulator;
use std::collections::{HashMap, VecDeque};
use std::f64::consts::PI;

/// Continuous action environment trait
pub trait ContinuousEnvironment {
    /// Gets the current state
    fn state(&self) -> Array1<f64>;

    /// Gets the action space bounds (min, max) for each dimension
    fn action_bounds(&self) -> Vec<(f64, f64)>;

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

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

    /// Get state dimension
    fn state_dim(&self) -> usize;

    /// Get action dimension
    fn action_dim(&self) -> usize;
}

/// Experience replay buffer for continuous RL
#[derive(Debug, Clone)]
pub struct ReplayBuffer {
    /// Maximum buffer size
    capacity: usize,

    /// Buffer storage
    buffer: VecDeque<Experience>,
}

/// Single experience tuple
#[derive(Debug, Clone)]
pub struct Experience {
    pub state: Array1<f64>,
    pub action: Array1<f64>,
    pub reward: f64,
    pub next_state: Array1<f64>,
    pub done: bool,
}

impl ReplayBuffer {
    /// Create new replay buffer
    pub fn new(capacity: usize) -> Self {
        Self {
            capacity,
            buffer: VecDeque::with_capacity(capacity),
        }
    }

    /// Add experience to buffer
    pub fn push(&mut self, exp: Experience) {
        if self.buffer.len() >= self.capacity {
            self.buffer.pop_front();
        }
        self.buffer.push_back(exp);
    }

    /// Sample batch from buffer
    pub fn sample(&self, batch_size: usize) -> Result<Vec<Experience>> {
        if self.buffer.len() < batch_size {
            return Err(MLError::ModelCreationError(
                "Not enough experiences in buffer".to_string(),
            ));
        }

        let mut batch = Vec::new();
        let mut rng = fastrand::Rng::new();

        for _ in 0..batch_size {
            let idx = rng.usize(0..self.buffer.len());
            batch.push(self.buffer[idx].clone());
        }

        Ok(batch)
    }

    /// Get buffer size
    pub fn len(&self) -> usize {
        self.buffer.len()
    }
}

/// Quantum actor network for continuous actions
pub struct QuantumActor {
    /// Quantum neural network
    qnn: QuantumNeuralNetwork,

    /// Action bounds
    action_bounds: Vec<(f64, f64)>,

    /// State dimension
    state_dim: usize,

    /// Action dimension
    action_dim: usize,
}

impl QuantumActor {
    /// Create new quantum actor
    pub fn new(
        state_dim: usize,
        action_dim: usize,
        action_bounds: Vec<(f64, f64)>,
        num_qubits: usize,
    ) -> Result<Self> {
        let layers = vec![
            QNNLayerType::EncodingLayer {
                num_features: state_dim,
            },
            QNNLayerType::VariationalLayer {
                num_params: num_qubits * 3,
            },
            QNNLayerType::EntanglementLayer {
                connectivity: "circular".to_string(),
            },
            QNNLayerType::VariationalLayer {
                num_params: num_qubits * 3,
            },
            QNNLayerType::MeasurementLayer {
                measurement_basis: "Pauli-Z".to_string(),
            },
        ];

        let qnn = QuantumNeuralNetwork::new(layers, num_qubits, state_dim, action_dim)?;

        Ok(Self {
            qnn,
            action_bounds,
            state_dim,
            action_dim,
        })
    }

    /// Get action from state
    pub fn get_action(&self, state: &Array1<f64>, add_noise: bool) -> Result<Array1<f64>> {
        // Placeholder - would use quantum circuit to generate actions
        let raw_actions = self.extract_continuous_actions_placeholder()?;

        // Apply bounds and noise
        let mut actions = Array1::zeros(self.action_dim);
        for i in 0..self.action_dim {
            let (min_val, max_val) = self.action_bounds[i];

            // Map quantum output to action range
            actions[i] = min_val + (max_val - min_val) * (raw_actions[i] + 1.0) / 2.0;

            // Add exploration noise if requested
            if add_noise {
                let noise = 0.1 * (max_val - min_val) * (2.0 * rand::random::<f64>() - 1.0);
                actions[i] = (actions[i] + noise).clamp(min_val, max_val);
            }
        }

        Ok(actions)
    }

    /// Extract continuous actions from quantum state (placeholder)
    fn extract_continuous_actions_placeholder(&self) -> Result<Array1<f64>> {
        // Placeholder - would measure expectation values
        let mut actions = Array1::zeros(self.action_dim);

        for i in 0..self.action_dim {
            // Simulate measurement of Pauli-Z on different qubits
            actions[i] = 2.0 * rand::random::<f64>() - 1.0; // [-1, 1]
        }

        Ok(actions)
    }
}

/// Quantum critic network for value estimation
pub struct QuantumCritic {
    /// Quantum neural network
    qnn: QuantumNeuralNetwork,

    /// Input dimension (state + action)
    input_dim: usize,
}

impl QuantumCritic {
    /// Create new quantum critic
    pub fn new(state_dim: usize, action_dim: usize, num_qubits: usize) -> Result<Self> {
        let input_dim = state_dim + action_dim;

        let layers = vec![
            QNNLayerType::EncodingLayer {
                num_features: input_dim,
            },
            QNNLayerType::VariationalLayer {
                num_params: num_qubits * 3,
            },
            QNNLayerType::EntanglementLayer {
                connectivity: "full".to_string(),
            },
            QNNLayerType::VariationalLayer {
                num_params: num_qubits * 3,
            },
            QNNLayerType::MeasurementLayer {
                measurement_basis: "computational".to_string(),
            },
        ];

        let qnn = QuantumNeuralNetwork::new(
            layers, num_qubits, input_dim, 1, // Q-value output
        )?;

        Ok(Self { qnn, input_dim })
    }

    /// Estimate Q-value for state-action pair
    pub fn get_q_value(&self, state: &Array1<f64>, action: &Array1<f64>) -> Result<f64> {
        // Concatenate state and action
        let mut input = Array1::zeros(self.input_dim);
        for i in 0..state.len() {
            input[i] = state[i];
        }
        for i in 0..action.len() {
            input[state.len() + i] = action[i];
        }

        // Placeholder - would use quantum circuit to estimate Q-value
        Ok(0.5 + 0.5 * (2.0 * rand::random::<f64>() - 1.0))
    }
}

/// Quantum Deep Deterministic Policy Gradient (QDDPG)
pub struct QuantumDDPG {
    /// Actor network
    actor: QuantumActor,

    /// Critic network
    critic: QuantumCritic,

    /// Target actor network
    target_actor: QuantumActor,

    /// Target critic network
    target_critic: QuantumCritic,

    /// Replay buffer
    replay_buffer: ReplayBuffer,

    /// Discount factor
    gamma: f64,

    /// Soft update coefficient
    tau: f64,

    /// Batch size
    batch_size: usize,
}

impl QuantumDDPG {
    /// Create new QDDPG agent
    pub fn new(
        state_dim: usize,
        action_dim: usize,
        action_bounds: Vec<(f64, f64)>,
        num_qubits: usize,
        buffer_capacity: usize,
    ) -> Result<Self> {
        let actor = QuantumActor::new(state_dim, action_dim, action_bounds.clone(), num_qubits)?;
        let critic = QuantumCritic::new(state_dim, action_dim, num_qubits)?;

        // Clone for target networks
        let target_actor = QuantumActor::new(state_dim, action_dim, action_bounds, num_qubits)?;
        let target_critic = QuantumCritic::new(state_dim, action_dim, num_qubits)?;

        Ok(Self {
            actor,
            critic,
            target_actor,
            target_critic,
            replay_buffer: ReplayBuffer::new(buffer_capacity),
            gamma: 0.99,
            tau: 0.005,
            batch_size: 64,
        })
    }

    /// Get action for state
    pub fn get_action(&self, state: &Array1<f64>, training: bool) -> Result<Array1<f64>> {
        self.actor.get_action(state, training)
    }

    /// Store experience in replay buffer
    pub fn store_experience(&mut self, exp: Experience) {
        self.replay_buffer.push(exp);
    }

    /// Update networks
    pub fn update(
        &mut self,
        actor_optimizer: &mut dyn Optimizer,
        critic_optimizer: &mut dyn Optimizer,
    ) -> Result<()> {
        if self.replay_buffer.len() < self.batch_size {
            return Ok(());
        }

        // Sample batch
        let batch = self.replay_buffer.sample(self.batch_size)?;

        // Update critic
        self.update_critic(&batch, critic_optimizer)?;

        // Update actor
        self.update_actor(&batch, actor_optimizer)?;

        // Soft update target networks
        self.soft_update()?;

        Ok(())
    }

    /// Update critic network
    fn update_critic(&mut self, batch: &[Experience], optimizer: &mut dyn Optimizer) -> Result<()> {
        // Compute target Q-values
        let mut target_q_values = Vec::new();

        for exp in batch {
            let target_action = self.target_actor.get_action(&exp.next_state, false)?;
            let target_q = self
                .target_critic
                .get_q_value(&exp.next_state, &target_action)?;
            let y = exp.reward + if exp.done { 0.0 } else { self.gamma * target_q };
            target_q_values.push(y);
        }

        // Placeholder - would compute loss and update parameters

        Ok(())
    }

    /// Update actor network
    fn update_actor(&mut self, batch: &[Experience], optimizer: &mut dyn Optimizer) -> Result<()> {
        // Compute policy gradient
        let mut policy_loss = 0.0;

        for exp in batch {
            let action = self.actor.get_action(&exp.state, false)?;
            let q_value = self.critic.get_q_value(&exp.state, &action)?;
            policy_loss -= q_value; // Maximize Q-value
        }

        policy_loss /= batch.len() as f64;

        // Placeholder - would compute gradients and update

        Ok(())
    }

    /// Soft update target networks
    fn soft_update(&mut self) -> Result<()> {
        // Update target actor parameters
        for i in 0..self.actor.qnn.parameters.len() {
            self.target_actor.qnn.parameters[i] = self.tau * self.actor.qnn.parameters[i]
                + (1.0 - self.tau) * self.target_actor.qnn.parameters[i];
        }

        // Update target critic parameters
        for i in 0..self.critic.qnn.parameters.len() {
            self.target_critic.qnn.parameters[i] = self.tau * self.critic.qnn.parameters[i]
                + (1.0 - self.tau) * self.target_critic.qnn.parameters[i];
        }

        Ok(())
    }

    /// Train on environment
    pub fn train(
        &mut self,
        env: &mut dyn ContinuousEnvironment,
        episodes: usize,
        actor_optimizer: &mut dyn Optimizer,
        critic_optimizer: &mut dyn Optimizer,
    ) -> Result<Vec<f64>> {
        let mut episode_rewards = Vec::new();

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

            while !done {
                // Get action
                let action = self.get_action(&state, true)?;

                // Step environment
                let (next_state, reward, is_done) = env.step(action.clone())?;

                // Store experience
                self.store_experience(Experience {
                    state: state.clone(),
                    action,
                    reward,
                    next_state: next_state.clone(),
                    done: is_done,
                });

                // Update networks
                self.update(actor_optimizer, critic_optimizer)?;

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

            episode_rewards.push(episode_reward);

            if episode % 10 == 0 {
                println!("Episode {}: Reward = {:.2}", episode, episode_reward);
            }
        }

        Ok(episode_rewards)
    }
}

/// Quantum Soft Actor-Critic (QSAC)
pub struct QuantumSAC {
    /// Actor network
    actor: QuantumActor,

    /// Two Q-networks for stability
    q1: QuantumCritic,
    q2: QuantumCritic,

    /// Target Q-networks
    target_q1: QuantumCritic,
    target_q2: QuantumCritic,

    /// Temperature parameter for entropy
    alpha: f64,

    /// Replay buffer
    replay_buffer: ReplayBuffer,

    /// Hyperparameters
    gamma: f64,
    tau: f64,
    batch_size: usize,
}

impl QuantumSAC {
    /// Create new QSAC agent
    pub fn new(
        state_dim: usize,
        action_dim: usize,
        action_bounds: Vec<(f64, f64)>,
        num_qubits: usize,
        buffer_capacity: usize,
    ) -> Result<Self> {
        let actor = QuantumActor::new(state_dim, action_dim, action_bounds, num_qubits)?;

        let q1 = QuantumCritic::new(state_dim, action_dim, num_qubits)?;
        let q2 = QuantumCritic::new(state_dim, action_dim, num_qubits)?;

        let target_q1 = QuantumCritic::new(state_dim, action_dim, num_qubits)?;
        let target_q2 = QuantumCritic::new(state_dim, action_dim, num_qubits)?;

        Ok(Self {
            actor,
            q1,
            q2,
            target_q1,
            target_q2,
            alpha: 0.2,
            replay_buffer: ReplayBuffer::new(buffer_capacity),
            gamma: 0.99,
            tau: 0.005,
            batch_size: 64,
        })
    }

    /// Get action with entropy regularization
    pub fn get_action(&self, state: &Array1<f64>, training: bool) -> Result<Array1<f64>> {
        // SAC uses stochastic policy even during evaluation
        self.actor.get_action(state, true)
    }

    /// Compute log probability of action (for entropy)
    fn log_prob(&self, state: &Array1<f64>, action: &Array1<f64>) -> Result<f64> {
        // Placeholder - would compute actual log probability
        Ok(-0.5 * action.mapv(|a| a * a).sum())
    }
}

/// Pendulum environment for continuous control
pub struct PendulumEnvironment {
    /// Angle (radians)
    theta: f64,

    /// Angular velocity
    theta_dot: f64,

    /// Time step
    dt: f64,

    /// Maximum steps per episode
    max_steps: usize,

    /// Current step
    current_step: usize,
}

impl PendulumEnvironment {
    /// Create new pendulum environment
    pub fn new() -> Self {
        Self {
            theta: 0.0,
            theta_dot: 0.0,
            dt: 0.05,
            max_steps: 200,
            current_step: 0,
        }
    }
}

impl ContinuousEnvironment for PendulumEnvironment {
    fn state(&self) -> Array1<f64> {
        Array1::from_vec(vec![self.theta.cos(), self.theta.sin(), self.theta_dot])
    }

    fn action_bounds(&self) -> Vec<(f64, f64)> {
        vec![(-2.0, 2.0)] // Torque bounds
    }

    fn step(&mut self, action: Array1<f64>) -> Result<(Array1<f64>, f64, bool)> {
        let torque = action[0].clamp(-2.0, 2.0);

        // Physics simulation
        let g = 10.0;
        let m = 1.0;
        let l = 1.0;

        // Update dynamics
        let theta_acc = -3.0 * g / (2.0 * l) * self.theta.sin() + 3.0 * torque / (m * l * l);
        self.theta_dot += theta_acc * self.dt;
        self.theta_dot = self.theta_dot.clamp(-8.0, 8.0);
        self.theta += self.theta_dot * self.dt;

        // Normalize angle to [-pi, pi]
        self.theta = ((self.theta + PI) % (2.0 * PI)) - PI;

        // Compute reward (penalize angle and velocity)
        let reward = -(self.theta.powi(2) + 0.1 * self.theta_dot.powi(2) + 0.001 * torque.powi(2));

        self.current_step += 1;
        let done = self.current_step >= self.max_steps;

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

    fn reset(&mut self) -> Array1<f64> {
        self.theta = PI * (2.0 * rand::random::<f64>() - 1.0);
        self.theta_dot = 2.0 * rand::random::<f64>() - 1.0;
        self.current_step = 0;
        self.state()
    }

    fn state_dim(&self) -> usize {
        3
    }

    fn action_dim(&self) -> usize {
        1
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::autodiff::optimizers::Adam;

    #[test]
    fn test_replay_buffer() {
        let mut buffer = ReplayBuffer::new(100);

        for i in 0..150 {
            let exp = Experience {
                state: Array1::zeros(4),
                action: Array1::zeros(2),
                reward: i as f64,
                next_state: Array1::zeros(4),
                done: false,
            };
            buffer.push(exp);
        }

        assert_eq!(buffer.len(), 100);

        let batch = buffer.sample(10).unwrap();
        assert_eq!(batch.len(), 10);
    }

    #[test]
    fn test_pendulum_environment() {
        let mut env = PendulumEnvironment::new();
        let state = env.reset();
        assert_eq!(state.len(), 3);

        let action = Array1::from_vec(vec![1.0]);
        let (next_state, reward, done) = env.step(action).unwrap();

        assert_eq!(next_state.len(), 3);
        assert!(reward <= 0.0); // Reward should be negative
        assert!(!done); // Not done after one step
    }

    #[test]
    fn test_quantum_actor() {
        let actor = QuantumActor::new(
            3, // state_dim
            1, // action_dim
            vec![(-2.0, 2.0)],
            4, // num_qubits
        )
        .unwrap();

        let state = Array1::from_vec(vec![1.0, 0.0, 0.5]);
        let action = actor.get_action(&state, false).unwrap();

        assert_eq!(action.len(), 1);
        assert!(action[0] >= -2.0 && action[0] <= 2.0);
    }

    #[test]
    fn test_quantum_critic() {
        let critic = QuantumCritic::new(3, 1, 4).unwrap();

        let state = Array1::from_vec(vec![1.0, 0.0, 0.5]);
        let action = Array1::from_vec(vec![1.5]);

        let q_value = critic.get_q_value(&state, &action).unwrap();
        assert!(q_value.is_finite());
    }
}