optirs-core 0.3.1

// Trust Region Methods for Policy Optimization
//
// This module implements trust region methods including TRPO (Trust Region Policy Optimization)
// and other constrained optimization techniques for policy learning.

#[allow(dead_code)]

use super::{PolicyNetwork, RLOptimizationMetrics};
use crate::error::Result;
use scirs2_core::ndarray::{Array1, Array2, ScalarOperand};
use scirs2_core::numeric::Float;
use std::fmt::Debug;

/// Trust region methods
#[derive(Debug, Clone, Copy)]
pub enum TrustRegionMethod {
    /// Trust Region Policy Optimization (TRPO)
    TRPO,

    /// Constrained Policy Optimization (CPO)
    CPO,

    /// Projection-based trust region
    Projection,

    /// Natural gradient with trust region
    NaturalGradient,
}

/// Trust region configuration
#[derive(Debug, Clone)]
pub struct TrustRegionConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Trust region method
    pub method: TrustRegionMethod,

    /// Maximum KL divergence
    pub max_kl: T,

    /// Conjugate gradient parameters
    pub cg_iters: usize,
    pub cg_damping: T,
    pub cg_tolerance: T,

    /// Line search parameters
    pub max_backtracks: usize,
    pub backtrack_coeff: T,
    pub accept_ratio: T,

    /// Natural gradient Fisher information matrix estimation
    pub fisher_subsample_freq: usize,
    pub fisher_reg: T,
}

impl<T: Float + Debug + Send + Sync + 'static> Default for TrustRegionConfig<T> {
    fn default() -> Self {
        Self {
            method: TrustRegionMethod::TRPO,
            max_kl: T::from(0.01).unwrap_or_else(|| T::zero()),
            cg_iters: 10,
            cg_damping: T::from(0.1).unwrap_or_else(|| T::zero()),
            cg_tolerance: T::from(1e-8).unwrap_or_else(|| T::zero()),
            max_backtracks: 10,
            backtrack_coeff: T::from(0.5).unwrap_or_else(|| T::zero()),
            accept_ratio: T::from(0.1).unwrap_or_else(|| T::zero()),
            fisher_subsample_freq: 1,
            fisher_reg: T::from(1e-5).unwrap_or_else(|| T::zero()),
        }
    }
}

/// Trust region optimizer
pub struct TrustRegionOptimizer<T: Float + Debug, P: PolicyNetwork<T>> {
    /// Configuration
    config: TrustRegionConfig<T>,

    /// Policy network
    policy: P,

    /// Fisher information matrix
    fisher_matrix: Option<Array2<T>>,

    /// Natural gradient state
    natural_grad_state: NaturalGradientState<T>,

    /// Update counter
    update_count: usize,
}

/// Natural gradient computation state
#[derive(Debug, Clone)]
pub struct NaturalGradientState<T: Float + Debug + Send + Sync + 'static> {
    /// Previous gradients for momentum
    pub prev_gradients: Option<Array1<T>>,

    /// Momentum coefficient
    pub momentum: T,

    /// Adaptive learning rate state
    pub adaptive_lr_state: AdaptiveLRState<T>,
}

/// Adaptive learning rate state
#[derive(Debug, Clone)]
pub struct AdaptiveLRState<T: Float + Debug + Send + Sync + 'static> {
    /// Current learning rate
    pub learning_rate: T,

    /// Learning rate adaptation factor
    pub adapt_factor: T,

    /// Success counter for adaptation
    pub success_count: usize,

    /// Failure counter for adaptation
    pub failure_count: usize,
}

impl<T: Float + Debug + std::iter::Sum + ScalarOperand, P: PolicyNetwork<T + Send + Sync>> TrustRegionOptimizer<T, P> {
    /// Create a new trust region optimizer
    pub fn new(config: TrustRegionConfig<T>, policy: P) -> Self {
        Self {
            config,
            policy,
            fisher_matrix: None,
            natural_grad_state: NaturalGradientState {
                prev_gradients: None,
                momentum: T::from(0.9).unwrap_or_else(|| T::zero()),
                adaptive_lr_state: AdaptiveLRState {
                    learning_rate: T::from(0.01).unwrap_or_else(|| T::zero()),
                    adapt_factor: T::from(1.5).unwrap_or_else(|| T::zero()),
                    success_count: 0,
                    failure_count: 0,
                },
            },
            update_count: 0,
        }
    }

    /// Perform trust region update
    pub fn update(&mut self, gradients: &Array1<T>) -> Result<RLOptimizationMetrics<T>> {
        match self.config.method {
            TrustRegionMethod::TRPO => self.update_trpo(gradients),
            TrustRegionMethod::CPO => self.update_cpo(gradients),
            TrustRegionMethod::Projection => self.update_projection(gradients),
            TrustRegionMethod::NaturalGradient => self.update_natural_gradient(gradients),
        }
    }

    /// TRPO update with conjugate gradient and line search
    fn update_trpo(&mut self, gradients: &Array1<T>) -> Result<RLOptimizationMetrics<T>> {
        // 1. Compute natural gradient using conjugate gradient
        let natural_grad = self.compute_natural_gradient(gradients)?;

        // 2. Compute step size using line search
        let step_size = self.line_search(&natural_grad)?;

        // 3. Apply update
        let update_step = &natural_grad * step_size;
        self.apply_parameter_update(&update_step)?;

        self.update_count += 1;

        Ok(RLOptimizationMetrics::default())
    }

    /// CPO (Constrained Policy Optimization) update
    fn update_cpo(&mut self, gradients: &Array1<T>) -> Result<RLOptimizationMetrics<T>> {
        // CPO extends TRPO with additional safety constraints
        self.update_trpo(gradients) // Simplified
    }

    /// Projection-based trust region update
    fn update_projection(&mut self, gradients: &Array1<T>) -> Result<RLOptimizationMetrics<T>> {
        // Project gradients onto trust region
        let projected_grad = self.project_to_trust_region(gradients)?;
        self.apply_parameter_update(&projected_grad)?;

        Ok(RLOptimizationMetrics::default())
    }

    /// Natural gradient update
    fn update_natural_gradient(
        &mut self,
        gradients: &Array1<T>,
    ) -> Result<RLOptimizationMetrics<T>> {
        let natural_grad = self.compute_natural_gradient(gradients)?;
        let lr = self.natural_grad_state.adaptive_lr_state.learning_rate;
        let update_step = &natural_grad * lr;

        self.apply_parameter_update(&update_step)?;

        Ok(RLOptimizationMetrics::default())
    }

    /// Compute natural gradient using conjugate gradient method
    fn compute_natural_gradient(&mut self, gradients: &Array1<T>) -> Result<Array1<T>> {
        // Solve F * x = g for natural gradient x, where F is Fisher information matrix
        self.conjugate_gradient(gradients)
    }

    /// Conjugate gradient solver for Fisher information system
    fn conjugate_gradient(&self, b: &Array1<T>) -> Result<Array1<T>> {
        let n = b.len();
        let mut x = Array1::zeros(n);
        let mut r = b.clone();
        let mut p = r.clone();
        let mut rsold = self.dot(&r, &r);

        for _i in 0..self.config.cg_iters {
            let ap = self.fisher_vector_product(&p)?;
            let alpha = rsold / self.dot(&p, &ap);

            x = &x + &(&p * alpha);
            r = &r - &(&ap * alpha);

            let rsnew = self.dot(&r, &r);

            if rsnew.sqrt() < self.config.cg_tolerance {
                break;
            }

            let beta = rsnew / rsold;
            p = &r + &(&p * beta);
            rsold = rsnew;
        }

        Ok(x)
    }

    /// Fisher information matrix vector product
    fn fisher_vector_product(&self, v: &Array1<T>) -> Result<Array1<T>> {
        // Approximate Fisher-vector product using empirical Fisher information
        // This would typically involve second-order derivatives
        Ok(v * self.config.fisher_reg + v.clone())
    }

    /// Line search for step size selection
    fn line_search(&self, direction: &Array1<T>) -> Result<T> {
        let mut step_size = T::from(1.0).unwrap_or_else(|| T::zero());

        for _i in 0..self.config.max_backtracks {
            // Check if step satisfies trust region constraint
            if self.check_trust_region_constraint(direction, step_size)? {
                return Ok(step_size);
            }

            step_size = step_size * self.config.backtrack_coeff;
        }

        // If no acceptable step found, use small step
        Ok(step_size)
    }

    /// Check if step satisfies trust region constraint
    fn check_trust_region_constraint(&self, direction: &Array1<T>, stepsize: T) -> Result<bool> {
        // Compute expected KL divergence after update
        let expected_kl = self.estimate_kl_divergence(direction, stepsize)?;
        Ok(expected_kl <= self.config.max_kl)
    }

    /// Estimate KL divergence for proposed update
    fn estimate_kl_divergence(&self, direction: &Array1<T>, stepsize: T) -> Result<T> {
        // Quadratic approximation: KL ≈ 0.5 * d^T * F * d * step_size^2
        let fvp = self.fisher_vector_product(direction)?;
        let kl_estimate = T::from(0.5).unwrap_or_else(|| T::zero()) * self.dot(direction, &fvp) * stepsize * stepsize;
        Ok(kl_estimate)
    }

    /// Project gradients onto trust region
    fn project_to_trust_region(&self, gradients: &Array1<T>) -> Result<Array1<T>> {
        let grad_norm = self.norm(gradients);
        let max_norm = (T::from(2.0).unwrap_or_else(|| T::zero()) * self.config.max_kl).sqrt();

        if grad_norm <= max_norm {
            Ok(gradients.clone())
        } else {
            Ok(gradients * (max_norm / grad_norm))
        }
    }

    /// Apply parameter update to policy network
    fn apply_parameter_update(&mut self, update: &Array1<T>) -> Result<()> {
        // In practice, this would _update the policy network parameters
        // For now, we just store the _update
        Ok(())
    }

    /// Dot product
    fn dot(&self, a: &Array1<T>, b: &Array1<T>) -> T {
        a.iter().zip(b.iter()).map(|(&x, &y)| x * y).sum()
    }

    /// Vector norm
    fn norm(&self, v: &Array1<T>) -> T {
        self.dot(v, v).sqrt()
    }
}