foxhunt/docs/RAINBOW_DQN_COMPONENT_MATRIX.md

Rainbow DQN Component Analysis - Foxhunt ML Codebase

Analysis Date: 2025-11-27 Codebase Path: /home/jgrusewski/Work/foxhunt/ml/src/dqn/ Architecture: System Architecture Designer Analysis

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Executive Summary

The foxhunt DQN implementation contains ALL 6 Rainbow DQN components with comprehensive integration:

Component	Status	Default Enabled	Hyperopt Tunable
Double DQN	✅ Complete	✅ Yes (always)	❌ No (hardcoded true)
Dueling Networks	✅ Complete	✅ Yes	✅ Yes (boolean disabled)
Prioritized Replay (PER)	✅ Complete	✅ Yes	✅ Yes (alpha, beta)
Multi-Step Returns	✅ Complete	✅ Yes (n=3)	✅ Yes (n=1-5)
Distributional RL (C51)	✅ Complete	❌ DISABLED	✅ Yes (v_min, v_max, atoms)
Noisy Networks	✅ Complete	✅ Yes	✅ Yes (sigma)

Critical Note: C51 Distributional RL is DISABLED by default due to BUG #36 (Candle library scatter_add gradient flow issue causing 40% training failure rate).

---

1. Double DQN

Implementation Status: ✅ COMPLETE

Core Files:

• ml/src/dqn/dqn.rs (lines 591-612, 1176-1210)
• ml/src/trainers/dqn/trainer.rs (line 540)

Key Components:

• Target Network: Separate target Q-network for stability
• Double Q-Learning: Action selection from main network, evaluation from target network
• Update Mechanism: Polyak averaging (soft updates) or periodic hard updates

Config Integration:

// ml/src/dqn/dqn.rs
pub struct DQNConfig {
    pub use_double_dqn: bool,  // Line 55
    pub tau: f64,              // Line 67 - Polyak coefficient
    pub use_soft_updates: bool, // Line 69
}

Trainer Integration:

// ml/src/trainers/dqn/trainer.rs (line 540)
use_double_dqn: true,  // ALWAYS ENABLED

Hyperopt Integration:

• ❌ NOT tunable - hardcoded to true in all configs
• ✅ tau parameter IS tunable (0.0001-0.01, log-scale)

Default Enabled: ✅ YES (production standard, prevents Q-value overestimation)

---

2. Dueling Networks

Implementation Status: ✅ COMPLETE

Core Files:

• ml/src/dqn/dueling.rs (complete implementation)
• ml/src/dqn/distributional_dueling.rs (hybrid with C51)
• ml/src/dqn/rainbow_network.rs (lines 73-85, dueling architecture)

Key Components:

// ml/src/dqn/dueling.rs
pub struct DuelingConfig {
    pub state_dim: usize,
    pub num_actions: usize,
    pub hidden_dim: usize,
    // ... activation, dropout config
}

pub struct DuelingQNetwork {
    // Separate value and advantage streams
    value_stream: Vec<Box<dyn Module>>,
    advantage_stream: Vec<Box<dyn Module>>,
}

Architecture:

• Value Stream: Estimates state value V(s)
• Advantage Stream: Estimates action advantages A(s,a)
• Aggregation: Q(s,a) = V(s) + (A(s,a) - mean(A))

Config Integration:

// ml/src/trainers/dqn/config.rs (line 404)
pub use_dueling: bool,
pub dueling_hidden_dim: usize,  // 128-512

Trainer Integration:

// ml/src/trainers/dqn/trainer.rs
if self.hyperparams.use_dueling {  // Line 1377
    // Use DuelingQNetwork or DistributionalDuelingQNetwork
}

Hyperopt Integration:

// ml/src/hyperopt/adapters/dqn.rs
pub use_dueling: bool,          // Line 203 - Boolean flag
pub dueling_hidden_dim: usize,  // Line 207 - Capacity (128-512)

// Search space (line 426):
(128.0, 512.0), // dueling_hidden_dim (linear, step=128)

// Default (line 336):
use_dueling: true,  // ENABLED by default
dueling_hidden_dim: 128,

Default Enabled: ✅ YES (Wave 8: full Rainbow DQN with 6/6 components)

---

3. Prioritized Experience Replay (PER)

Implementation Status: ✅ COMPLETE

Core Files:

• ml/src/dqn/prioritized_replay.rs (complete segment tree implementation)
• ml/src/dqn/replay_buffer_type.rs (enum wrapper for uniform/prioritized)

Key Components:

// ml/src/dqn/prioritized_replay.rs
pub struct PrioritizedReplayBuffer {
    experiences: Vec<Experience>,
    priorities: SegmentTree,  // Sum tree for O(log n) sampling
    alpha: f32,               // Prioritization exponent
    beta: f32,                // Importance sampling correction
    max_priority: f32,
}

pub struct SegmentTree {
    // Binary tree for efficient priority-based sampling
    pub fn update(&mut self, idx: usize, priority: f32)
    pub fn sample(&self, value: f32) -> Result<usize, MLError>
}

Algorithm:

• Priority: P(i) = |TD_error(i)|^alpha + epsilon
• Sampling: Probability ∝ priority
• IS Weights: w(i) = (1 / (N * P(i)))^beta
• Beta Annealing: beta_start → 1.0 over training

Config Integration:

// ml/src/trainers/dqn/config.rs (lines 398-401)
pub use_per: bool,
pub per_alpha: f64,        // 0.4-0.8
pub per_beta_start: f64,   // 0.2-0.6

Trainer Integration:

// ml/src/trainers/dqn/trainer.rs
if self.hyperparams.use_per {  // Line 1383
    // Use PrioritizedReplayBuffer instead of ReplayBuffer
}

Hyperopt Integration:

// ml/src/hyperopt/adapters/dqn.rs
pub use_per: bool,          // Line 188
pub per_alpha: f64,         // Line 193 (0.4-0.8)
pub per_beta_start: f64,    // Line 198 (0.2-0.6)

// Search space (lines 416-417):
(0.4, 0.8),   // per_alpha
(0.2, 0.6),   // per_beta_start

// Default (line 333):
use_per: true,        // ENABLED (25-40% speedup)
per_alpha: 0.6,       // Rainbow standard
per_beta_start: 0.4,  // Rainbow standard

Default Enabled: ✅ YES (25-40% convergence speed improvement)

---

4. Multi-Step Returns (N-Step TD)

Implementation Status: ✅ COMPLETE

Core Files:

• ml/src/dqn/multi_step.rs (complete n-step calculator)
• ml/src/dqn/nstep_buffer.rs (n-step experience buffer)

Key Components:

// ml/src/dqn/multi_step.rs
pub struct MultiStepConfig {
    pub enabled: bool,
    pub n_steps: usize,  // 1-10 steps
    pub gamma: f64,
}

pub struct MultiStepCalculator {
    pub fn compute_n_step_return(&self,
        rewards: &[f32],
        next_q: f32
    ) -> f32
}

// N-step return formula:
// R_t^n = r_t + γ*r_{t+1} + ... + γ^(n-1)*r_{t+n-1} + γ^n*Q(s_{t+n}, a*)

Config Integration:

// ml/src/dqn/dqn.rs (lines 77-81)
pub n_steps: usize,  // Rainbow standard: 3

// ml/src/trainers/dqn/config.rs (line 411)
pub n_steps: usize,  // 1-10

Trainer Integration:

• Multi-step returns computed during TD target calculation
• Integrated into main training loop via MultiStepCalculator

Hyperopt Integration:

// ml/src/hyperopt/adapters/dqn.rs
pub n_steps: usize,  // Line 212

// Search space (line 427):
(1.0, 5.0),  // n_steps (linear, cast to int)

// Default (line 338):
n_steps: 1,  // Standard 1-step TD

Default Enabled: ✅ YES (n=3 in Rainbow configs, n=1 in conservative)

---

5. Distributional RL (C51)

Implementation Status: ✅ COMPLETE BUT DISABLED

Core Files:

• ml/src/dqn/distributional.rs (categorical distribution)
• ml/src/dqn/distributional_dueling.rs (hybrid dueling + C51)
• ml/src/dqn/quantile_regression.rs (QR-DQN variant - Wave 26 P1.13)
• ml/src/dqn/rainbow_network.rs (lines 77-82, distribution outputs)

Key Components:

// ml/src/dqn/distributional.rs
pub struct DistributionalConfig {
    pub num_atoms: usize,  // 51 (Rainbow standard)
    pub v_min: f64,        // -2.0
    pub v_max: f64,        // +2.0
}

pub struct CategoricalDistribution {
    supports: Tensor,  // Value atoms
    delta: f64,        // Atom spacing
    pub fn project_distribution(&self, ...) -> Result<Tensor, MLError>
}

Algorithm:

• Models return distribution Z(s,a) instead of expected value Q(s,a)
• Discretizes distribution into num_atoms support points
• Loss: KL divergence between predicted and target distributions

Config Integration:

// ml/src/trainers/dqn/config.rs (lines 415-421)
pub use_distributional: bool,
pub num_atoms: usize,    // 51
pub v_min: f64,          // -2.0
pub v_max: f64,          // +2.0

Trainer Integration:

// ml/src/trainers/dqn/trainer.rs
if self.hyperparams.use_distributional {  // Line 1396
    // Use distributional loss (C51)
}

Hyperopt Integration:

// ml/src/hyperopt/adapters/dqn.rs
pub use_distributional: bool,  // Line 222
pub num_atoms: usize,          // Line 227
pub v_min: f64,                // Line 231
pub v_max: f64,                // Line 235

// Search space (lines 423-424, 428):
(-3.0, -1.0),   // v_min (center: -2.0)
(1.0, 3.0),     // v_max (center: +2.0)
(51.0, 201.0),  // num_atoms (step=50)

// Default (lines 355-358):
use_distributional: false,  // ❌ DISABLED (BUG #36)
num_atoms: 51,
v_min: -2.0,
v_max: 2.0,

Default Enabled: ❌ NO

Critical Issue - BUG #36:

=============================================================================
WAVE 23 P1 FIX: C51 DISTRIBUTIONAL RL DISABLED (BUG #36)
=============================================================================
BUG #36: Candle's scatter_add has broken gradient flow in backward pass
Symptom: 40% of trials experience complete gradient collapse at Epoch 2
Root Cause: CPU scatter loop breaks autograd graph in project_distribution()
Status: BLOCKED by external library bug
Re-enable: After Candle library fixes scatter_add or we implement workaround

Evidence: /tmp/WAVE23_CAMPAIGN_FINAL_ANALYSIS.md
- 60% success rate WITH C51 enabled
- Expected 95%+ success rate WITH C51 disabled (standard DQN proven stable)

Performance: Standard DQN achieves Sharpe 0.77-2.0 WITHOUT distributional RL
=============================================================================

Alternative: QR-DQN (Quantile Regression) implemented in quantile_regression.rs as more robust alternative for risk modeling.

---

6. Noisy Networks

Implementation Status: ✅ COMPLETE

Core Files:

• ml/src/dqn/noisy_layers.rs (factorized Gaussian noise implementation)
• ml/src/dqn/noisy_sigma_scheduler.rs (noise annealing)
• ml/src/dqn/rainbow_network.rs (lines 98-100, noisy layer integration)

Key Components:

// ml/src/dqn/noisy_layers.rs
pub struct NoisyLinear {
    weight_mu: Tensor,      // Learnable mean
    weight_sigma: Tensor,   // Learnable std dev
    bias_mu: Tensor,
    bias_sigma: Tensor,

    pub fn forward(&self, x: &Tensor) -> Result<Tensor, MLError> {
        // y = (μ_w + σ_w ⊙ ε_w) x + μ_b + σ_b ⊙ ε_b
    }
}

Algorithm:

• Factorized Gaussian: ε_i,j = f(ε_i) * f(ε_j) where f(x) = sgn(x)√|x|
• Learned Exploration: σ parameters learned via backprop
• Replaces ε-greedy: No manual exploration schedule needed

Config Integration:

// ml/src/trainers/dqn/config.rs (lines 425-429)
pub use_noisy_nets: bool,
pub noisy_sigma_init: f64,     // 0.1-1.0
pub enable_noisy_sigma_scheduler: bool,
pub noisy_sigma_initial: f64,  // 0.6
pub noisy_sigma_final: f64,    // 0.4

Trainer Integration:

// ml/src/trainers/dqn/trainer.rs
if self.hyperparams.use_noisy_nets {  // Line 1403
    // Use NoisyLinear layers in Q-network
}

Hyperopt Integration:

// ml/src/hyperopt/adapters/dqn.rs
pub use_noisy_nets: bool,      // Line 240
pub noisy_sigma_init: f64,     // Line 245

// Search space (line 425):
(0.1_f64.ln(), 1.0_f64.ln()),  // noisy_sigma_init (log-scale)

// Default (line 359):
use_noisy_nets: true,          // ✅ ENABLED
noisy_sigma_init: 0.5,         // Rainbow standard

Default Enabled: ✅ YES (Rainbow DQN standard, replaces epsilon-greedy)

---

Advanced Rainbow Extensions

Additional Components Beyond Standard Rainbow DQN:

Component	File	Status	Integration
Quantile Regression (QR-DQN)	quantile_regression.rs	✅ Complete	Alternative to C51 (Wave 26 P1.13)
Ensemble Uncertainty	ensemble_network.rs	✅ Complete	Exploration via model disagreement (Wave 26 P2.3)
Hindsight Experience Replay	hindsight_replay.rs	✅ Complete	5-10x data efficiency (Wave 26 P1.7)
Curiosity-Driven Exploration	curiosity.rs	✅ Complete	Intrinsic rewards (Wave 26 P1.8)
GAE (Generalized Advantage)	gae.rs	✅ Complete	Lower variance returns (Wave 26 P1.9)
Attention Mechanisms	attention.rs	✅ Complete	Temporal patterns (Wave 26 P1.2)
Spectral Normalization	spectral_norm.rs	✅ Complete	Q-value stability
Residual Connections	residual.rs	✅ Complete	Gradient flow (Wave 26 P0.4)
RMSNorm	rmsnorm.rs	✅ Complete	15% faster than LayerNorm (Wave 26 P2.4)
Mixed Precision (AMP)	mixed_precision.rs	✅ Complete	2x speedup (Wave 26 P2.1)

---

Hyperopt Search Space Summary

Continuous Parameters (39D total):

Base Parameters (11D):

1. learning_rate (1e-5 to 3e-4, log-scale)
2. batch_size (64-160, linear)
3. gamma (0.95-0.99, linear)
4. buffer_size (50K-100K, log-scale)
5. hold_penalty_weight (1.0-2.0, linear)
6. max_position_absolute (4.0-8.0, linear)
7. huber_delta (10.0-40.0, log-scale)
8. entropy_coefficient (0.0-0.1, linear)
9. transaction_cost_multiplier (0.5-2.0, linear)
10. per_alpha (0.4-0.8, linear)
11. per_beta_start (0.2-0.6, linear)

Rainbow Components (6D): 12. v_min (-3.0 to -1.0, linear) - unused while C51 disabled 13. v_max (1.0 to 3.0, linear) - unused while C51 disabled 14. noisy_sigma_init (0.1-1.0, log-scale) 15. dueling_hidden_dim (128-512, linear, step=128) 16. n_steps (1-5, linear, int) 17. num_atoms (51-201, linear, step=50) - unused while C51 disabled

Wave 19: Kelly Risk (4D): 18-21. kelly_fractional, kelly_max_fraction, kelly_min_trades, volatility_window

Wave 26: Advanced Features (18D): 22-38. Ensemble uncertainty, warmup, curiosity, tau, TD error clamping, LR scheduling, GAE, noisy sigma scheduling, network architecture parameters

Boolean Flags (Hardcoded):

Always Enabled:

• use_double_dqn = true (prevents Q-value overestimation)
• use_per = true (25-40% speedup)
• use_dueling = true (Rainbow standard, Wave 8)
• use_noisy_nets = true (Rainbow standard, Wave 8)

Always Disabled:

• use_distributional = false (BUG #36 - Candle gradient flow issue)

---

Trainer Integration Architecture

DQNTrainer Component Selection:

// ml/src/trainers/dqn/trainer.rs (lines 1377-1403)

if self.hyperparams.use_dueling {
    // Path 1: Dueling + Distributional (if enabled)
    if self.hyperparams.use_distributional {
        // DistributionalDuelingQNetwork
    } else {
        // DuelingQNetwork
    }
}

if self.hyperparams.use_per {
    // PrioritizedReplayBuffer with segment tree
} else {
    // Standard ReplayBuffer
}

if self.hyperparams.use_distributional {
    // C51 categorical distribution loss
} else {
    // Standard Q-learning loss (MSE or Huber)
}

if self.hyperparams.use_noisy_nets {
    // NoisyLinear layers in Q-network
} else {
    // Standard Linear layers
}

Network Architecture Decision Tree:

Q-Network Architecture
├── use_distributional == true (DISABLED)
│   ├── use_dueling == true
│   │   └── DistributionalDuelingQNetwork (C51 + dueling)
│   └── use_dueling == false
│       └── DistributionalQNetwork (C51 only)
│
└── use_distributional == false (DEFAULT)
    ├── use_dueling == true (DEFAULT)
    │   └── DuelingQNetwork (standard dueling)
    └── use_dueling == false
        └── QNetwork (standard Q-network)

Current Production Path:

DuelingQNetwork + PrioritizedReplayBuffer + NoisyLinear + Multi-Step(n=3) + Double Q-Learning
= 5/6 Rainbow Components (C51 disabled due to BUG #36)

---

Configuration File Locations

Core Configs:

1. DQN Agent Config: ml/src/dqn/dqn.rs - Lines 33-120
2. Rainbow Config: ml/src/dqn/rainbow_config.rs - Lines 11-204
3. Trainer Hyperparameters: ml/src/trainers/dqn/config.rs - Lines 264-703
4. Hyperopt Parameters: ml/src/hyperopt/adapters/dqn.rs - Lines 160-391

Component Configs:

• Dueling: ml/src/dqn/dueling.rs - DuelingConfig
• Distributional: ml/src/dqn/distributional.rs - DistributionalConfig
• Multi-Step: ml/src/dqn/multi_step.rs - MultiStepConfig
• PER: ml/src/dqn/prioritized_replay.rs - PrioritizedReplayConfig
• Quantile: ml/src/dqn/quantile_regression.rs - QuantileConfig

---

Production Defaults (2025 Optimized)

Function: dqn_config_2025()

Location: ml/src/trainers/dqn/config.rs - Lines 750-808

DQNConfig {
    // Architecture
    state_dim: 51,           // 45 market + 6 portfolio features
    num_actions: 45,         // 5 exposure × 3 order × 3 urgency
    hidden_dims: vec![512, 256, 128],

    // Training
    learning_rate: 1e-4,
    warmup_steps: 5000,
    batch_size: 256,
    gamma: 0.99,

    // Loss & Stability
    use_huber_loss: true,
    huber_delta: 10.0,
    gradient_clip_norm: 100.0,

    // Replay
    replay_buffer_capacity: 500_000,
    use_per: true,           // ✅
    per_alpha: 0.6,
    per_beta_start: 0.4,

    // Exploration
    epsilon_start: 1.0,
    epsilon_end: 0.01,
    epsilon_decay: 0.9999,
    use_noisy_nets: true,    // ✅
    noisy_sigma_init: 0.5,

    // Target Updates
    use_soft_updates: true,
    tau: 0.001,
    target_update_freq: 1,

    // Rainbow Components
    use_double_dqn: true,    // ✅
    use_dueling: true,       // ✅
    dueling_hidden_dim: 256,
    use_distributional: true,  // ⚠️ Set to false in actual usage (BUG #36)
    num_atoms: 51,
    v_min: -2.0,
    v_max: 2.0,
    n_steps: 3,              // ✅
}

Variants:

• dqn_config_2025_hft() - HFT-optimized (faster updates, attention layers)
• dqn_config_2025_conservative() - Smaller network, lower LR
• dqn_config_2025_aggressive() - Larger network, higher LR

---

Testing & Validation

Component Tests:

• ml/src/dqn/tests/target_update_comprehensive_tests.rs - Double DQN
• ml/src/dqn/tests/factored_integration_tests.rs - Dueling integration
• ml/src/trainers/dqn/tests/p0_integration_tests.rs - PER integration
• ml/src/trainers/dqn/tests/p1_integration_tests.rs - Multi-step integration

Performance Validation:

• Benchmark: ml/src/benchmark/dqn_benchmark.rs
• Stress Testing: ml/src/dqn/stress_testing.rs
• Performance Tests: ml/src/dqn/performance_tests.rs

---

Recommendations

Immediate Actions:

1. ✅ Current State: 5/6 Rainbow components operational and production-ready
2. ⚠️ C51 Workaround: Consider switching to QR-DQN (already implemented) as alternative distributional method
3. 🔧 Hyperopt Integration: All components except C51 are tunable via 39D search space

Future Enhancements:

1. BUG #36 Resolution: Monitor Candle library updates for scatter_add gradient fix
2. QR-DQN Validation: Benchmark QR-DQN vs standard DQN for production use
3. Ensemble Uncertainty: Consider enabling for exploration (Wave 26 P1.4, currently disabled)
4. HER Integration: Evaluate Hindsight Experience Replay for data efficiency

Performance Notes:

• Without C51: Sharpe 0.77-2.0 achieved (production validated)
• With 5/6 Rainbow: 25-40% faster convergence (PER contribution)
• Noisy Networks: Better sample efficiency than epsilon-greedy
• Dueling: +10-20% sample efficiency (literature validated)

---

Architecture Diagram

┌─────────────────────────────────────────────────────────────┐
│                     Rainbow DQN Agent                       │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  ┌──────────────┐      ┌──────────────┐                   │
│  │ Main Network │      │Target Network│                   │
│  │  (Q-Network) │      │  (Q-Target)  │                   │
│  └──────┬───────┘      └──────┬───────┘                   │
│         │                     │                            │
│         │ ┌───────────────────┴──────────┐                │
│         │ │  Dueling Architecture (✅)    │                │
│         │ │  ┌──────────┐ ┌─────────────┐│                │
│         │ │  │ Value    │ │ Advantage   ││                │
│         │ │  │ Stream   │ │ Stream      ││                │
│         │ │  └────┬─────┘ └─────┬───────┘│                │
│         │ │       └───────┬─────┘         │                │
│         │ │          Aggregation          │                │
│         │ └───────────────────────────────┘                │
│         │                                                  │
│         │ ┌───────────────────────────────┐                │
│         │ │  Noisy Networks (✅)          │                │
│         │ │  NoisyLinear(μ, σ)            │                │
│         │ │  Learned exploration          │                │
│         │ └───────────────────────────────┘                │
│         │                                                  │
│         │ ┌───────────────────────────────┐                │
│         │ │  C51 Distributional (❌)      │                │
│         │ │  DISABLED (BUG #36)           │                │
│         │ │  51 atoms: Z(s,a) → Q(s,a)    │                │
│         │ └───────────────────────────────┘                │
│         │                                                  │
│         ▼                                                  │
│  ┌──────────────────────────────────────────┐             │
│  │  Double Q-Learning (✅)                   │             │
│  │  Action: argmax Q(s,a; θ)                │             │
│  │  Eval: Q(s,a*; θ')                       │             │
│  └──────────────────────────────────────────┘             │
│         │                                                  │
│         ▼                                                  │
│  ┌──────────────────────────────────────────┐             │
│  │  Prioritized Replay Buffer (✅)           │             │
│  │  SegmentTree: O(log n) sampling          │             │
│  │  P(i) = |TD_error|^α                     │             │
│  └──────────────────────────────────────────┘             │
│         │                                                  │
│         ▼                                                  │
│  ┌──────────────────────────────────────────┐             │
│  │  Multi-Step Returns (✅)                  │             │
│  │  N-step TD (n=3)                         │             │
│  │  R_t^n = Σ γ^k * r_{t+k} + γ^n * Q(...)  │             │
│  └──────────────────────────────────────────┘             │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Legend:
  ✅ = Enabled by default
  ❌ = Disabled (BUG #36)

---

File Inventory (70+ DQN-related files)

Core Rainbow Components (6):

1. dqn.rs - Main agent (Double DQN + target network)
2. dueling.rs - Dueling architecture
3. prioritized_replay.rs - PER with segment tree
4. multi_step.rs - N-step returns
5. distributional.rs - C51 categorical distribution
6. noisy_layers.rs - Factorized Gaussian noise

Integration Files (4):

7. rainbow_agent.rs - Complete Rainbow agent
8. rainbow_config.rs - Rainbow configuration
9. rainbow_network.rs - Network with all components
10. rainbow_integration.rs - Integration helpers

Advanced Components (10):

11. quantile_regression.rs - QR-DQN (C51 alternative)
12. distributional_dueling.rs - Hybrid architecture
13. ensemble_network.rs - Ensemble uncertainty
14. hindsight_replay.rs - HER for data efficiency
15. curiosity.rs - Intrinsic motivation
16. gae.rs - Generalized Advantage Estimation
17. attention.rs - Temporal attention
18. spectral_norm.rs - Q-value stability
19. residual.rs - Skip connections
20. rmsnorm.rs - Efficient normalization

Support Files (50+):

21-70. Replay buffers, reward functions, portfolio tracking, risk integration, preprocessing, target updates, etc.

---

End of Analysis