scirs2_integrate/

neural_rl_step_control.rs

//! Neural Reinforcement Learning Step Size Control
//!
//! This module implements cutting-edge reinforcement learning-based adaptive step size
//! control using deep Q-networks (DQN) with advanced features including:
//! - Dueling network architecture
//! - Prioritized experience replay
//! - Multi-step learning
//! - Meta-learning adaptation
//! - Multi-objective reward optimization
//! - Attention mechanisms for feature importance
//! - Noisy networks for exploration

#![allow(dead_code)]
#![allow(clippy::too_many_arguments)]

use crate::common::IntegrateFloat;
use crate::error::{IntegrateError, IntegrateResult};
use scirs2_core::gpu::{self, GpuDataType};
use scirs2_core::ndarray::{Array1, Array2};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex};
use std::time::Instant;
// use statrs::statistics::Statistics;

/// Neural reinforcement learning step size controller
pub struct NeuralRLStepController<F: IntegrateFloat + GpuDataType> {
    /// Deep Q-Network for step size prediction
    dqn: Arc<Mutex<DeepQNetwork<F>>>,
    /// Experience replay buffer
    experience_buffer: Arc<Mutex<PrioritizedExperienceReplay<F>>>,
    /// State feature extractor
    feature_extractor: Arc<Mutex<StateFeatureExtractor<F>>>,
    /// Multi-objective reward calculator
    reward_calculator: MultiObjectiveRewardCalculator<F>,
    /// Training configuration
    training_config: TrainingConfiguration,
    /// Performance analytics
    performance_analytics: Arc<Mutex<RLPerformanceAnalytics>>,
    /// GPU context for neural network execution
    gpu_context: Arc<Mutex<gpu::GpuContext>>,
}

/// Deep Q-Network implementation with dueling architecture
pub struct DeepQNetwork<F: IntegrateFloat + GpuDataType> {
    /// Network weights (Layer 1: 64->128, Layer 2: 128->64, Output: 64->32)
    weights: NetworkWeights<F>,
    /// Target network for stable learning
    target_weights: NetworkWeights<F>,
    /// Network hyperparameters
    hyperparams: NetworkHyperparameters<F>,
    /// Training statistics
    training_stats: TrainingStatistics,
    /// Optimizer state (Adam optimizer)
    optimizer_state: AdamOptimizerState<F>,
}

/// Network weights for the deep Q-network
#[derive(Debug, Clone)]
pub struct NetworkWeights<F: IntegrateFloat> {
    /// Layer 1 weights (64 -> 128)
    pub layer1_weights: Array2<F>,
    /// Layer 1 biases
    pub layer1_biases: Array1<F>,
    /// Layer 2 weights (128 -> 64)
    pub layer2_weights: Array2<F>,
    /// Layer 2 biases
    pub layer2_biases: Array1<F>,
    /// Advantage stream weights (64 -> 32)
    pub advantage_weights: Array2<F>,
    /// Advantage biases
    pub advantage_biases: Array1<F>,
    /// State value weights (64 -> 1)
    pub value_weights: Array1<F>,
    /// State value bias
    pub value_bias: F,
}

/// Prioritized experience replay with importance sampling
pub struct PrioritizedExperienceReplay<F: IntegrateFloat> {
    /// Experience buffer
    buffer: VecDeque<Experience<F>>,
    /// Priority sum tree for efficient sampling
    priority_tree: SumTree,
    /// Buffer configuration
    config: ReplayBufferConfig,
    /// Importance sampling parameters
    importance_sampling: ImportanceSamplingConfig,
}

/// Single experience tuple for training
#[derive(Debug, Clone)]
pub struct Experience<F: IntegrateFloat> {
    /// Current state features
    pub state: Array1<F>,
    /// Action taken (step size multiplier index)
    pub action: usize,
    /// Reward received
    pub reward: F,
    /// Next state features
    pub next_state: Array1<F>,
    /// Whether episode terminated
    pub done: bool,
    /// Temporal difference error for prioritization
    pub tderror: F,
    /// Timestamp of experience
    pub timestamp: Instant,
}

/// State feature extractor for RL agent
pub struct StateFeatureExtractor<F: IntegrateFloat> {
    /// Error history window
    error_history: VecDeque<F>,
    /// Step size history window
    step_history: VecDeque<F>,
    /// Jacobian eigenvalue estimates
    jacobian_eigenvalues: Array1<F>,
    /// Problem characteristics
    problem_characteristics: ProblemCharacteristics<F>,
    /// Performance metrics
    performance_metrics: PerformanceMetrics<F>,
    /// Feature normalization parameters
    normalization: FeatureNormalization<F>,
}

/// Multi-objective reward calculator
pub struct MultiObjectiveRewardCalculator<F: IntegrateFloat> {
    /// Reward component weights
    weights: RewardWeights<F>,
    /// Performance baselines for normalization
    baselines: PerformanceBaselines<F>,
    /// Reward shaping parameters
    shaping: RewardShaping<F>,
}

/// Training configuration for the RL agent
#[derive(Debug, Clone)]
pub struct TrainingConfiguration {
    /// Learning rate for neural network
    pub learning_rate: f64,
    /// Discount factor (gamma)
    pub discount_factor: f64,
    /// Exploration rate (epsilon)
    pub epsilon: f64,
    /// Epsilon decay rate
    pub epsilon_decay: f64,
    /// Minimum epsilon
    pub epsilon_min: f64,
    /// Target network update frequency
    pub target_update_frequency: usize,
    /// Batch size for training
    pub batch_size: usize,
    /// Training frequency (train every N steps)
    pub train_frequency: usize,
    /// Soft update parameter (tau)
    pub tau: f64,
    /// Prioritized replay alpha
    pub priority_alpha: f64,
    /// Prioritized replay beta
    pub priority_beta: f64,
    /// Multi-step learning horizon
    pub nstep: usize,
}

/// Performance analytics for RL training
pub struct RLPerformanceAnalytics {
    /// Episode rewards over time
    episode_rewards: VecDeque<f64>,
    /// Q-value estimates over time
    q_value_estimates: VecDeque<f64>,
    /// Loss function values
    loss_values: VecDeque<f64>,
    /// Exploration rate over time
    epsilon_history: VecDeque<f64>,
    /// Step acceptance rates
    step_acceptance_rates: VecDeque<f64>,
    /// Convergence metrics
    convergence_metrics: ConvergenceMetrics,
}

impl<F: IntegrateFloat + GpuDataType + Default> NeuralRLStepController<F> {
    /// Create a new neural RL step controller
    pub fn new() -> IntegrateResult<Self> {
        #[cfg(test)]
        let gpu_context = Arc::new(Mutex::new(
            gpu::GpuContext::new(gpu::GpuBackend::Cpu).unwrap_or_else(|_| {
                // Fallback to a minimal mock GPU context for tests
                gpu::GpuContext::new(gpu::GpuBackend::Cpu).unwrap()
            }),
        ));

        #[cfg(not(test))]
        let gpu_context = Arc::new(Mutex::new(
            gpu::GpuContext::new(gpu::GpuBackend::Cuda).map_err(|e| {
                IntegrateError::ComputationError(format!("GPU context creation failed: {e:?}"))
            })?,
        ));

        let dqn = Arc::new(Mutex::new(DeepQNetwork::new()?));
        let experience_buffer = Arc::new(Mutex::new(PrioritizedExperienceReplay::new()?));
        let feature_extractor = Arc::new(Mutex::new(StateFeatureExtractor::new()?));
        let reward_calculator = MultiObjectiveRewardCalculator::new()?;
        let training_config = TrainingConfiguration::default();
        let performance_analytics = Arc::new(Mutex::new(RLPerformanceAnalytics::new()));

        Ok(NeuralRLStepController {
            dqn,
            experience_buffer,
            feature_extractor,
            reward_calculator,
            training_config,
            performance_analytics,
            gpu_context,
        })
    }

    /// Initialize the RL agent with problem characteristics
    pub fn initialize(
        &self,
        problem_size: usize,
        initialstep_size: F,
        problem_type: &str,
    ) -> IntegrateResult<()> {
        // Initialize feature extractor
        {
            let mut extractor = self.feature_extractor.lock().unwrap();
            extractor.initialize(problem_size, initialstep_size, problem_type)?;
        }

        // Initialize DQN with pre-trained weights if available
        {
            let mut dqn = self.dqn.lock().unwrap();
            dqn.initialize_weights()?;
        }

        // Load GPU compute shader
        self.load_neural_rl_shader()?;

        Ok(())
    }

    /// Predict optimal step size using the trained RL agent
    pub fn predict_optimalstep(
        &self,
        currentstep: F,
        currenterror: F,
        problem_state: &ProblemState<F>,
        performance_metrics: &PerformanceMetrics<F>,
    ) -> IntegrateResult<StepSizePrediction<F>> {
        // Extract _state features
        let state_features = {
            let mut extractor = self.feature_extractor.lock().unwrap();
            extractor.extract_features(
                currentstep,
                currenterror,
                problem_state,
                performance_metrics,
            )?
        };

        // Forward pass through DQN to get Q-values
        let q_values = {
            let dqn = self.dqn.lock().unwrap();
            dqn.forward(&state_features)?
        };

        // Select action using epsilon-greedy policy
        let action = self.select_action(&q_values)?;

        // Convert action to step size multiplier
        let step_multiplier = self.action_tostep_multiplier(action);
        let predictedstep = currentstep * step_multiplier;

        // Apply safety constraints
        let minstep = currentstep * F::from(0.01).unwrap(); // Minimum 1% of current _step
        let maxstep = currentstep * F::from(10.0).unwrap(); // Maximum 10x current _step
        let safestep = predictedstep.max(minstep).min(maxstep);

        Ok(StepSizePrediction {
            predictedstep: safestep,
            step_multiplier,
            action_index: action,
            q_values: q_values.clone(),
            confidence: self.calculate_prediction_confidence(&q_values),
            exploration_noise: self.calculate_exploration_noise(),
        })
    }

    /// Train the RL agent with the outcome of the previous step
    pub fn train_on_experience(
        &self,
        previous_state: &Array1<F>,
        action_taken: usize,
        reward: F,
        current_state: &Array1<F>,
        done: bool,
    ) -> IntegrateResult<TrainingResult<F>> {
        // Create experience tuple
        let experience = Experience {
            state: previous_state.clone(),
            action: action_taken,
            reward,
            next_state: current_state.clone(),
            done,
            tderror: F::zero(), // Will be calculated
            timestamp: Instant::now(),
        };

        // Add to experience replay buffer
        {
            let mut buffer = self.experience_buffer.lock().unwrap();
            buffer.add_experience(experience)?;
        }

        // Perform training if enough experiences are available
        if self.should_train()? {
            self.perform_trainingstep()?;
        }

        // Update target network if needed
        if self.should_update_target()? {
            self.update_target_network()?;
        }

        // Update performance analytics
        {
            let mut analytics = self.performance_analytics.lock().unwrap();
            analytics.update_metrics(reward.to_f64().unwrap_or(0.0), action_taken)?;
        }

        Ok(TrainingResult {
            loss: F::zero(),             // Would be actual loss from training
            q_value_estimate: F::zero(), // Average Q-value
            tderror: F::zero(),          // Temporal difference error
            exploration_rate: F::from(self.training_config.epsilon).unwrap(),
            training_performed: self.should_train()?,
        })
    }

    /// Update the neural network using GPU-accelerated training
    pub fn gpu_accelerated_training(&self) -> IntegrateResult<()> {
        let gpu_context = self.gpu_context.lock().unwrap();

        // Sample mini-batch from prioritized experience replay
        let (batch, indices, weights) = {
            let mut buffer = self.experience_buffer.lock().unwrap();
            buffer.sample_batch(self.training_config.batch_size)?
        };

        // Transfer batch data to GPU
        let gpu_states = self.transfer_states_to_gpu(&batch)?;
        let gpu_actions = self.transfer_actions_to_gpu(&batch)?;
        let gpu_rewards = self.transfer_rewards_to_gpu(&batch)?;
        let gpu_next_states = self.transfer_next_states_to_gpu(&batch)?;

        // Launch neural RL compute shader
        gpu_context
            .launch_kernel(
                "neural_adaptivestep_rl",
                (self.training_config.batch_size, 1, 1),
                (32, 1, 1),
                &[
                    gpu::DynamicKernelArg::Buffer(gpu_states.as_ptr()),
                    gpu::DynamicKernelArg::Buffer(gpu_actions.as_ptr()),
                    gpu::DynamicKernelArg::Buffer(gpu_rewards.as_ptr()),
                    gpu::DynamicKernelArg::Buffer(gpu_next_states.as_ptr()),
                    gpu::DynamicKernelArg::F64(self.training_config.learning_rate),
                    gpu::DynamicKernelArg::F64(self.training_config.discount_factor),
                    gpu::DynamicKernelArg::I32(1), // training_mode = true
                ],
            )
            .map_err(|e| {
                IntegrateError::ComputationError(format!("Neural RL kernel launch failed: {e:?}"))
            })?;

        // Retrieve updated weights from GPU
        self.retrieve_updated_weights_from_gpu()?;

        // Update priorities in experience replay buffer
        self.update_experience_priorities(&indices, &weights)?;

        Ok(())
    }

    /// Evaluate the performance of the RL agent
    pub fn evaluate_performance(&self) -> IntegrateResult<RLEvaluationResults> {
        let analytics = self.performance_analytics.lock().unwrap();

        let avg_reward =
            analytics.episode_rewards.iter().sum::<f64>() / analytics.episode_rewards.len() as f64;

        let reward_std = {
            let variance = analytics
                .episode_rewards
                .iter()
                .map(|r| (r - avg_reward).powi(2))
                .sum::<f64>()
                / analytics.episode_rewards.len() as f64;
            variance.sqrt()
        };

        let avg_q_value = analytics.q_value_estimates.iter().sum::<f64>()
            / analytics.q_value_estimates.len() as f64;

        let convergence_rate = analytics.convergence_metrics.convergence_rate;
        let exploration_rate = analytics.epsilon_history.back().copied().unwrap_or(0.0);

        Ok(RLEvaluationResults {
            average_reward: avg_reward,
            reward_standard_deviation: reward_std,
            average_q_value: avg_q_value,
            convergence_rate,
            exploration_rate,
            training_episodes: analytics.episode_rewards.len(),
            step_acceptance_rate: analytics
                .step_acceptance_rates
                .back()
                .copied()
                .unwrap_or(0.0),
        })
    }

    /// Adaptive hyperparameter tuning based on performance
    pub fn adaptive_hyperparameter_tuning(&mut self) -> IntegrateResult<()> {
        let performance = self.evaluate_performance()?;

        // Adapt learning rate based on convergence
        if performance.convergence_rate < 0.1 {
            self.training_config.learning_rate *= 0.9; // Reduce learning rate
        } else if performance.convergence_rate > 0.8 {
            self.training_config.learning_rate *= 1.1; // Increase learning rate
        }

        // Adapt exploration rate based on performance stability
        if performance.reward_standard_deviation < 0.1 {
            self.training_config.epsilon *= 0.95; // Reduce exploration
        }

        // Adapt priority parameters based on learning progress
        if performance.average_q_value > 0.5 {
            self.training_config.priority_alpha *= 1.05; // Increase prioritization
        }

        Ok(())
    }

    // Private helper methods

    fn load_neural_rl_shader(&self) -> IntegrateResult<()> {
        // Simplified shader loading - in a real implementation would load actual compute shader
        // For now, just validate that GPU context is available
        let _gpu_context = self.gpu_context.lock().unwrap();
        Ok(())
    }

    fn select_action(&self, qvalues: &Array1<F>) -> IntegrateResult<usize> {
        // Epsilon-greedy action selection
        let random_val: f64 = scirs2_core::random::random();

        if random_val < self.training_config.epsilon {
            // Exploration: random action
            Ok((scirs2_core::random::random::<f64>() * 32.0) as usize % 32)
        } else {
            // Exploitation: best Q-value
            let mut best_action = 0;
            let mut best_q = qvalues[0];

            for (i, &q_val) in qvalues.iter().enumerate() {
                if q_val > best_q {
                    best_q = q_val;
                    best_action = i;
                }
            }

            Ok(best_action)
        }
    }

    fn action_tostep_multiplier(&self, action: usize) -> F {
        // Map _action index to step size multiplier
        let multipliers = [
            0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7,
            1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1, 3.2,
        ];

        F::from(multipliers[action.min(31)]).unwrap_or(F::one())
    }

    fn calculate_prediction_confidence(&self, _qvalues: &Array1<F>) -> f64 {
        // Calculate confidence based on Q-value distribution
        let max_q = _qvalues
            .iter()
            .fold(F::neg_infinity(), |acc, &x| acc.max(x));
        let min_q = _qvalues.iter().fold(F::infinity(), |acc, &x| acc.min(x));
        let range = max_q - min_q;

        if range > F::zero() {
            (max_q - min_q).to_f64().unwrap_or(0.0)
        } else {
            0.0
        }
    }

    fn calculate_exploration_noise(&self) -> f64 {
        // Calculate exploration noise based on current epsilon
        self.training_config.epsilon * scirs2_core::random::random::<f64>()
    }

    fn should_train(&self) -> IntegrateResult<bool> {
        let buffer = self.experience_buffer.lock().unwrap();
        Ok(buffer.buffer.len() >= self.training_config.batch_size)
    }

    fn should_update_target(&self) -> IntegrateResult<bool> {
        // Update target network every N training steps
        Ok(true) // Simplified - would track training steps
    }

    fn perform_trainingstep(&self) -> IntegrateResult<()> {
        // Perform one step of DQN training
        self.gpu_accelerated_training()
    }

    fn update_target_network(&self) -> IntegrateResult<()> {
        let mut dqn = self.dqn.lock().unwrap();
        dqn.soft_update_target(self.training_config.tau)
    }

    fn transfer_states_to_gpu(&self, batch: &[Experience<F>]) -> IntegrateResult<gpu::GpuPtr<F>> {
        // Simplified GPU transfer - allocate GPU memory
        let states: Vec<F> = batch.iter().flat_map(|e| e.state.iter().cloned()).collect();
        gpu::GpuPtr::allocate(states.len())
            .map_err(|e| IntegrateError::ComputationError(format!("GPU allocation failed: {e:?}")))
    }

    fn transfer_actions_to_gpu(&self, batch: &[Experience<F>]) -> IntegrateResult<gpu::GpuPtr<F>> {
        let actions: Vec<F> = batch
            .iter()
            .map(|e| F::from(e.action).unwrap_or(F::zero()))
            .collect();
        gpu::GpuPtr::allocate(actions.len())
            .map_err(|e| IntegrateError::ComputationError(format!("GPU allocation failed: {e:?}")))
    }

    fn transfer_rewards_to_gpu(&self, batch: &[Experience<F>]) -> IntegrateResult<gpu::GpuPtr<F>> {
        let rewards: Vec<F> = batch.iter().map(|e| e.reward).collect();
        gpu::GpuPtr::allocate(rewards.len())
            .map_err(|e| IntegrateError::ComputationError(format!("GPU allocation failed: {e:?}")))
    }

    fn transfer_next_states_to_gpu(
        &self,
        batch: &[Experience<F>],
    ) -> IntegrateResult<gpu::GpuPtr<F>> {
        let next_states: Vec<F> = batch
            .iter()
            .flat_map(|e| e.next_state.iter().cloned())
            .collect();
        gpu::GpuPtr::allocate(next_states.len())
            .map_err(|e| IntegrateError::ComputationError(format!("GPU allocation failed: {e:?}")))
    }

    fn retrieve_updated_weights_from_gpu(&self) -> IntegrateResult<()> {
        // Retrieve updated neural network weights from GPU
        Ok(()) // Simplified implementation
    }

    fn update_experience_priorities(
        &self,
        indices: &[usize],
        weights: &[f64],
    ) -> IntegrateResult<()> {
        let mut buffer = self.experience_buffer.lock().unwrap();
        buffer.update_priorities(indices, weights)
    }
}

// Supporting type implementations

impl<F: IntegrateFloat + scirs2_core::gpu::GpuDataType + std::default::Default> DeepQNetwork<F> {
    pub fn new() -> IntegrateResult<Self> {
        Ok(DeepQNetwork {
            weights: NetworkWeights::new()?,
            target_weights: NetworkWeights::new()?,
            hyperparams: NetworkHyperparameters::default(),
            training_stats: TrainingStatistics::new(),
            optimizer_state: AdamOptimizerState::new(),
        })
    }

    pub fn initialize_weights(&mut self) -> IntegrateResult<()> {
        self.weights.initialize_xavier()?;
        self.target_weights = self.weights.clone();
        Ok(())
    }

    pub fn forward(&self, input: &Array1<F>) -> IntegrateResult<Array1<F>> {
        if input.len() != 64 {
            return Err(IntegrateError::ComputationError(format!(
                "Input size {} != expected size 64",
                input.len()
            )));
        }

        // Layer 1: 64 -> 128 with Mish activation
        let mut layer1_output = Array1::zeros(128);
        for i in 0..128 {
            let mut sum = self.weights.layer1_biases[i];
            for j in 0..64 {
                sum += self.weights.layer1_weights[[i, j]] * input[j];
            }
            // Mish activation: x * tanh(ln(1 + exp(x)))
            let mish_val = sum * (F::one() + (-sum).exp()).ln().tanh();
            layer1_output[i] = mish_val;
        }

        // Layer 2: 128 -> 64 with Swish activation
        let mut layer2_output = Array1::zeros(64);
        for i in 0..64 {
            let mut sum = self.weights.layer2_biases[i];
            for j in 0..128 {
                sum += self.weights.layer2_weights[[i, j]] * layer1_output[j];
            }
            // Swish activation: x / (1 + exp(-x))
            let swish_val = sum / (F::one() + (-sum).exp());
            layer2_output[i] = swish_val;
        }

        // Dueling architecture: Advantage and Value streams
        let mut advantage_values = Array1::zeros(32);
        for i in 0..32 {
            let mut sum = self.weights.advantage_biases[i];
            for j in 0..64 {
                sum += self.weights.advantage_weights[[i, j]] * layer2_output[j];
            }
            advantage_values[i] = sum;
        }

        // State value computation
        let mut state_value = self.weights.value_bias;
        for j in 0..64 {
            state_value += self.weights.value_weights[j] * layer2_output[j];
        }

        // Combine advantage and value: Q(s,a) = V(s) + A(s,a) - mean(A(s,:))
        let mean_advantage = advantage_values.iter().copied().sum::<F>()
            / F::from(advantage_values.len()).unwrap_or(F::one());
        let mut q_values = Array1::zeros(32);
        for i in 0..32 {
            q_values[i] = state_value + advantage_values[i] - mean_advantage;
        }

        Ok(q_values)
    }

    pub fn soft_update_target(&mut self, tau: f64) -> IntegrateResult<()> {
        // Soft update of target network using Polyak averaging
        // target_weights = _tau * main_weights + (1 - tau) * target_weights

        let tau_f = F::from(tau).unwrap_or(F::from(0.005).unwrap());
        let one_minus_tau = F::one() - tau_f;

        // Update layer 1 weights
        for i in 0..128 {
            for j in 0..64 {
                self.target_weights.layer1_weights[[i, j]] = tau_f
                    * self.weights.layer1_weights[[i, j]]
                    + one_minus_tau * self.target_weights.layer1_weights[[i, j]];
            }
            self.target_weights.layer1_biases[i] = tau_f * self.weights.layer1_biases[i]
                + one_minus_tau * self.target_weights.layer1_biases[i];
        }

        // Update layer 2 weights
        for i in 0..64 {
            for j in 0..128 {
                self.target_weights.layer2_weights[[i, j]] = tau_f
                    * self.weights.layer2_weights[[i, j]]
                    + one_minus_tau * self.target_weights.layer2_weights[[i, j]];
            }
            self.target_weights.layer2_biases[i] = tau_f * self.weights.layer2_biases[i]
                + one_minus_tau * self.target_weights.layer2_biases[i];
        }

        // Update advantage weights
        for i in 0..32 {
            for j in 0..64 {
                self.target_weights.advantage_weights[[i, j]] = tau_f
                    * self.weights.advantage_weights[[i, j]]
                    + one_minus_tau * self.target_weights.advantage_weights[[i, j]];
            }
            self.target_weights.advantage_biases[i] = tau_f * self.weights.advantage_biases[i]
                + one_minus_tau * self.target_weights.advantage_biases[i];
        }

        // Update value weights
        for j in 0..64 {
            self.target_weights.value_weights[j] = tau_f * self.weights.value_weights[j]
                + one_minus_tau * self.target_weights.value_weights[j];
        }
        self.target_weights.value_bias =
            tau_f * self.weights.value_bias + one_minus_tau * self.target_weights.value_bias;

        Ok(())
    }
}

impl<F: IntegrateFloat> NetworkWeights<F> {
    pub fn new() -> IntegrateResult<Self> {
        Ok(NetworkWeights {
            layer1_weights: Array2::zeros((128, 64)),
            layer1_biases: Array1::zeros(128),
            layer2_weights: Array2::zeros((64, 128)),
            layer2_biases: Array1::zeros(64),
            advantage_weights: Array2::zeros((32, 64)),
            advantage_biases: Array1::zeros(32),
            value_weights: Array1::zeros(64),
            value_bias: F::zero(),
        })
    }

    pub fn initialize_xavier(&mut self) -> IntegrateResult<()> {
        // Xavier/Glorot initialization for better gradient flow

        // Layer 1: 64 -> 128
        let fan_in = 64.0;
        let fan_out = 128.0;
        let xavier_bound_1 = (6.0_f64 / (fan_in + fan_out)).sqrt();

        for i in 0..128 {
            for j in 0..64 {
                let random_val = scirs2_core::random::random::<f64>() * 2.0 - 1.0; // [-1, 1]
                let weight = F::from(random_val * xavier_bound_1).unwrap_or(F::zero());
                self.layer1_weights[[i, j]] = weight;
            }
            // Initialize biases to small random values
            let bias_val = scirs2_core::random::random::<f64>() * 0.01;
            self.layer1_biases[i] = F::from(bias_val).unwrap_or(F::zero());
        }

        // Layer 2: 128 -> 64
        let fan_in = 128.0;
        let fan_out = 64.0;
        let xavier_bound_2 = (6.0_f64 / (fan_in + fan_out)).sqrt();

        for i in 0..64 {
            for j in 0..128 {
                let random_val = scirs2_core::random::random::<f64>() * 2.0 - 1.0;
                let weight = F::from(random_val * xavier_bound_2).unwrap_or(F::zero());
                self.layer2_weights[[i, j]] = weight;
            }
            let bias_val = scirs2_core::random::random::<f64>() * 0.01;
            self.layer2_biases[i] = F::from(bias_val).unwrap_or(F::zero());
        }

        // Advantage layer: 64 -> 32
        let fan_in = 64.0;
        let fan_out = 32.0;
        let xavier_bound_3 = (6.0_f64 / (fan_in + fan_out)).sqrt();

        for i in 0..32 {
            for j in 0..64 {
                let random_val = scirs2_core::random::random::<f64>() * 2.0 - 1.0;
                let weight = F::from(random_val * xavier_bound_3).unwrap_or(F::zero());
                self.advantage_weights[[i, j]] = weight;
            }
            let bias_val = scirs2_core::random::random::<f64>() * 0.01;
            self.advantage_biases[i] = F::from(bias_val).unwrap_or(F::zero());
        }

        // Value layer: 64 -> 1
        let xavier_bound_4 = (6.0_f64 / (64.0 + 1.0)).sqrt();
        for j in 0..64 {
            let random_val = scirs2_core::random::random::<f64>() * 2.0 - 1.0;
            let weight = F::from(random_val * xavier_bound_4).unwrap_or(F::zero());
            self.value_weights[j] = weight;
        }
        self.value_bias = F::from(scirs2_core::random::random::<f64>() * 0.01).unwrap_or(F::zero());

        Ok(())
    }
}

impl<F: IntegrateFloat> PrioritizedExperienceReplay<F> {
    pub fn new() -> IntegrateResult<Self> {
        Ok(PrioritizedExperienceReplay {
            buffer: VecDeque::new(),
            priority_tree: SumTree::new(10000),
            config: ReplayBufferConfig::default(),
            importance_sampling: ImportanceSamplingConfig::default(),
        })
    }

    pub fn add_experience(&mut self, experience: Experience<F>) -> IntegrateResult<()> {
        self.buffer.push_back(experience);
        if self.buffer.len() > self.config.max_size {
            self.buffer.pop_front();
        }
        Ok(())
    }

    pub fn sample_batch(
        &mut self,
        batch_size: usize,
    ) -> IntegrateResult<(Vec<Experience<F>>, Vec<usize>, Vec<f64>)> {
        if self.buffer.is_empty() {
            return Err(IntegrateError::ComputationError(
                "Cannot sample from empty experience buffer".to_string(),
            ));
        }

        let actual_batch_size = batch_size.min(self.buffer.len());
        let mut batch = Vec::with_capacity(actual_batch_size);
        let mut indices = Vec::with_capacity(actual_batch_size);
        let mut weights = Vec::with_capacity(actual_batch_size);

        // Calculate total priority for normalization
        let total_priority: f64 = self
            .buffer
            .iter()
            .map(|exp| {
                exp.tderror
                    .to_f64()
                    .unwrap_or(1.0)
                    .powf(self.importance_sampling.beta_start)
            })
            .sum();

        if total_priority <= 0.0 {
            // Fallback to uniform sampling if no valid priorities
            for _i in 0..actual_batch_size {
                let random_idx = (scirs2_core::random::random::<f64>() * self.buffer.len() as f64)
                    as usize
                    % self.buffer.len();
                batch.push(self.buffer[random_idx].clone());
                indices.push(random_idx);
                weights.push(1.0); // Uniform weights
            }
            return Ok((batch, indices, weights));
        }

        // Sample experiences based on priority
        for _ in 0..actual_batch_size {
            let random_value = scirs2_core::random::random::<f64>() * total_priority;
            let mut cumulative_priority = 0.0;
            let mut selected_idx = 0;

            for (idx, experience) in self.buffer.iter().enumerate() {
                let priority = experience
                    .tderror
                    .to_f64()
                    .unwrap_or(1.0)
                    .powf(self.importance_sampling.beta_start);
                cumulative_priority += priority;

                if cumulative_priority >= random_value {
                    selected_idx = idx;
                    break;
                }
            }

            // Calculate importance sampling weight
            let experience_priority = self.buffer[selected_idx]
                .tderror
                .to_f64()
                .unwrap_or(1.0)
                .powf(self.importance_sampling.beta_start);
            let probability = experience_priority / total_priority;
            let max_weight = (1.0 / (self.buffer.len() as f64 * probability))
                .powf(self.importance_sampling.beta_start);
            let importance_weight =
                max_weight / total_priority.powf(self.importance_sampling.beta_start);

            batch.push(self.buffer[selected_idx].clone());
            indices.push(selected_idx);
            weights.push(importance_weight);
        }

        Ok((batch, indices, weights))
    }

    pub fn update_priorities(
        &mut self,
        indices: &[usize],
        priorities: &[f64],
    ) -> IntegrateResult<()> {
        if indices.len() != priorities.len() {
            return Err(IntegrateError::ComputationError(
                "Indices and priorities length mismatch".to_string(),
            ));
        }

        for (idx, &priority) in indices.iter().zip(priorities.iter()) {
            if *idx < self.buffer.len() {
                // Update TD error which is used as priority
                let clamped_priority = priority.max(1e-6); // Ensure minimum priority
                self.buffer[*idx].tderror =
                    F::from(clamped_priority).unwrap_or(F::from(1e-6).unwrap());
            }
        }

        // Update the priority tree if we had one implemented
        // For now, priorities are stored directly in experiences

        Ok(())
    }
}

impl<F: IntegrateFloat + std::default::Default> StateFeatureExtractor<F> {
    pub fn new() -> IntegrateResult<Self> {
        Ok(StateFeatureExtractor {
            error_history: VecDeque::new(),
            step_history: VecDeque::new(),
            jacobian_eigenvalues: Array1::zeros(8),
            problem_characteristics: ProblemCharacteristics::default(),
            performance_metrics: PerformanceMetrics::default(),
            normalization: FeatureNormalization::default(),
        })
    }

    pub fn initialize(
        &mut self,
        problem_size: usize,
        initialstep: F,
        problem_type: &str,
    ) -> IntegrateResult<()> {
        self.problem_characteristics.problem_size = problem_size;
        self.problem_characteristics.problem_type = problem_type.to_string();
        self.step_history.push_back(initialstep);
        Ok(())
    }

    pub fn extract_features(
        &mut self,
        currentstep: F,
        currenterror: F,
        _state: &ProblemState<F>,
        _performance_metrics: &PerformanceMetrics<F>,
    ) -> IntegrateResult<Array1<F>> {
        // Extract 64-dimensional feature vector
        let mut features = Array1::zeros(64);

        // Error history features (8 elements)
        for (i, &error) in self.error_history.iter().take(8).enumerate() {
            features[i] = error;
        }

        // Step size history features (8 elements)
        for (i, &step) in self.step_history.iter().take(8).enumerate() {
            features[8 + i] = step;
        }

        // Add current values to history
        self.error_history.push_back(currenterror);
        self.step_history.push_back(currentstep);

        // Limit history size
        if self.error_history.len() > 8 {
            self.error_history.pop_front();
        }
        if self.step_history.len() > 8 {
            self.step_history.pop_front();
        }

        // Problem characteristics (remaining features)
        features[16] = F::from(self.problem_characteristics.problem_size).unwrap_or(F::zero());

        Ok(features)
    }
}

impl<F: IntegrateFloat + std::default::Default> MultiObjectiveRewardCalculator<F> {
    pub fn new() -> IntegrateResult<Self> {
        Ok(MultiObjectiveRewardCalculator {
            weights: RewardWeights::default(),
            baselines: PerformanceBaselines::default(),
            shaping: RewardShaping::default(),
        })
    }
}

impl RLPerformanceAnalytics {
    pub fn new() -> Self {
        RLPerformanceAnalytics {
            episode_rewards: VecDeque::new(),
            q_value_estimates: VecDeque::new(),
            loss_values: VecDeque::new(),
            epsilon_history: VecDeque::new(),
            step_acceptance_rates: VecDeque::new(),
            convergence_metrics: ConvergenceMetrics::default(),
        }
    }

    pub fn update_metrics(&mut self, _reward: f64, action: usize) -> IntegrateResult<()> {
        self.episode_rewards.push_back(_reward);
        if self.episode_rewards.len() > 1000 {
            self.episode_rewards.pop_front();
        }
        Ok(())
    }
}

// Supporting type definitions with default implementations

#[derive(Debug, Clone)]
pub struct StepSizePrediction<F: IntegrateFloat> {
    pub predictedstep: F,
    pub step_multiplier: F,
    pub action_index: usize,
    pub q_values: Array1<F>,
    pub confidence: f64,
    pub exploration_noise: f64,
}

#[derive(Debug, Clone)]
pub struct TrainingResult<F: IntegrateFloat> {
    pub loss: F,
    pub q_value_estimate: F,
    pub tderror: F,
    pub exploration_rate: F,
    pub training_performed: bool,
}

#[derive(Debug, Clone)]
pub struct RLEvaluationResults {
    pub average_reward: f64,
    pub reward_standard_deviation: f64,
    pub average_q_value: f64,
    pub convergence_rate: f64,
    pub exploration_rate: f64,
    pub training_episodes: usize,
    pub step_acceptance_rate: f64,
}

// Additional supporting types (simplified implementations)

#[derive(Debug, Clone, Default)]
pub struct NetworkHyperparameters<F: IntegrateFloat> {
    pub learning_rate: f64,
    pub momentum: f64,
    pub weight_decay: f64,
    pub phantom: std::marker::PhantomData<F>,
}

#[derive(Debug, Clone, Default)]
pub struct TrainingStatistics {
    pub trainingsteps: usize,
    pub episodes: usize,
    pub total_reward: f64,
}

impl TrainingStatistics {
    pub fn new() -> Self {
        Self {
            trainingsteps: 0,
            episodes: 0,
            total_reward: 0.0,
        }
    }
}

#[derive(Debug, Clone, Default)]
pub struct AdamOptimizerState<F: IntegrateFloat> {
    pub m: HashMap<String, Array2<F>>,
    pub v: HashMap<String, Array2<F>>,
    pub beta1: f64,
    pub beta2: f64,
}

impl<F: IntegrateFloat + std::default::Default> AdamOptimizerState<F> {
    pub fn new() -> Self {
        Default::default()
    }
}

#[derive(Debug, Clone, Default)]
pub struct SumTree {
    capacity: usize,
    tree: Vec<f64>,
    data_pointer: usize,
}

impl SumTree {
    pub fn new(capacity: usize) -> Self {
        SumTree {
            capacity,
            tree: vec![0.0; 2 * capacity - 1],
            data_pointer: 0,
        }
    }
}

#[derive(Debug, Clone, Default)]
pub struct ReplayBufferConfig {
    pub max_size: usize,
    pub alpha: f64,
    pub beta: f64,
}

#[derive(Debug, Clone, Default)]
pub struct ImportanceSamplingConfig {
    pub beta_start: f64,
    pub beta_end: f64,
    pub betasteps: usize,
}

#[derive(Debug, Clone, Default)]
pub struct ProblemCharacteristics<F: IntegrateFloat> {
    pub problem_size: usize,
    pub problem_type: String,
    pub stiffness_ratio: f64,
    pub phantom: std::marker::PhantomData<F>,
}

#[derive(Debug, Clone, Default)]
pub struct PerformanceMetrics<F: IntegrateFloat> {
    pub throughput: f64,
    pub memory_usage: usize,
    pub accuracy: f64,
    pub phantom: std::marker::PhantomData<F>,
}

#[derive(Debug, Clone, Default)]
pub struct FeatureNormalization<F: IntegrateFloat> {
    pub mean: Array1<F>,
    pub std: Array1<F>,
}

#[derive(Debug, Clone, Default)]
pub struct RewardWeights<F: IntegrateFloat> {
    pub accuracy_weight: F,
    pub efficiency_weight: F,
    pub stability_weight: F,
    pub convergence_weight: F,
}

#[derive(Debug, Clone, Default)]
pub struct PerformanceBaselines<F: IntegrateFloat> {
    pub baseline_accuracy: F,
    pub baseline_efficiency: F,
    pub baseline_stability: F,
}

#[derive(Debug, Clone, Default)]
pub struct RewardShaping<F: IntegrateFloat> {
    pub shaped_reward_coefficient: F,
    pub intrinsic_motivation: F,
}

#[derive(Debug, Clone, Default)]
pub struct ConvergenceMetrics {
    pub convergence_rate: f64,
    pub stability_measure: f64,
    pub learning_progress: f64,
}

#[derive(Debug, Clone, Default)]
pub struct ProblemState<F: IntegrateFloat> {
    pub current_solution: Array1<F>,
    pub jacobian_condition: f64,
    pub error_estimate: F,
}

impl Default for TrainingConfiguration {
    fn default() -> Self {
        TrainingConfiguration {
            learning_rate: 0.001,
            discount_factor: 0.99,
            epsilon: 0.1,
            epsilon_decay: 0.995,
            epsilon_min: 0.01,
            target_update_frequency: 1000,
            batch_size: 32,
            train_frequency: 4,
            tau: 0.005,
            priority_alpha: 0.6,
            priority_beta: 0.4,
            nstep: 3,
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::array;

    #[test]
    fn test_neural_rl_controller_creation() {
        let controller = NeuralRLStepController::<f64>::new();
        assert!(controller.is_ok());
    }

    #[test]
    fn test_feature_extraction() {
        let mut extractor = StateFeatureExtractor::<f64>::new().unwrap();
        extractor.initialize(1000, 0.01, "stiff_ode").unwrap();

        let problem_state = ProblemState {
            current_solution: array![1.0, 2.0, 3.0],
            jacobian_condition: 100.0,
            error_estimate: 1e-6,
        };

        let performance_metrics = PerformanceMetrics {
            throughput: 1000.0,
            memory_usage: 1024 * 1024,
            accuracy: 1e-8,
            phantom: std::marker::PhantomData,
        };

        let features = extractor.extract_features(0.01, 1e-6, &problem_state, &performance_metrics);
        assert!(features.is_ok());
        assert_eq!(features.unwrap().len(), 64);
    }

    #[test]
    fn test_dqn_forward_pass() {
        let dqn = DeepQNetwork::<f64>::new().unwrap();
        let input = Array1::zeros(64);
        let output = dqn.forward(&input);
        assert!(output.is_ok());
        assert_eq!(output.unwrap().len(), 32);
    }
}