foxhunt/docs/codebase-cleanup/wave26_p1.6_adaptive_dropout_report.md

Wave 26 P1.6: Adaptive Dropout Scheduler Implementation

Summary

Implemented adaptive dropout scheduling that linearly decreases dropout rate from an initial high value to a final low value over a specified number of training steps. High dropout early in training prevents overfitting, while low dropout late allows fine-tuning.

Implementation Details

1. DropoutScheduler Struct

Location: /home/jgrusewski/Work/foxhunt/ml/src/dqn/network.rs (lines 13-58)

pub struct DropoutScheduler {
    initial_rate: f64,
    final_rate: f64,
    decay_steps: usize,
    current_step: usize,
}

Key Methods:

• new(initial_rate, final_rate, decay_steps) - Creates scheduler
• get_rate() - Returns current dropout rate using linear interpolation
• step(steps) - Advances scheduler by N steps
• current_step() - Returns current step count

Formula:

progress = min(current_step / decay_steps, 1.0)
rate = initial_rate * (1 - progress) + final_rate * progress

2. QNetworkConfig Extension

Location: /home/jgrusewski/Work/foxhunt/ml/src/dqn/network.rs (lines 60-93)

New Field:

pub dropout_schedule: Option<(f64, f64, usize)>  // (initial, final, decay_steps)

• None - Uses static dropout_prob (existing behavior)
• Some((0.5, 0.1, 100000)) - Adaptive dropout from 0.5 → 0.1 over 100k steps

3. QNetwork Integration

Changes:

1. Added scheduler field (line 132):

   dropout_scheduler: std::sync::Mutex<Option<DropoutScheduler>>
   

2. Initialize scheduler in constructor (lines 229-235):

   let dropout_scheduler = if let Some((initial, final_rate, steps)) = config.dropout_schedule {
       Some(DropoutScheduler::new(initial, final_rate, steps))
   } else {
       None
   };
   

3. Updated forward pass (lines 258-259):

   let dropout_rate = self.get_dropout_rate();
   let layers = NetworkLayers::new_with_dropout_rate(&var_builder, &config, &device, dropout_rate)?;
   

4. Step scheduler after each forward (lines 292-296):

   if let Ok(mut scheduler_opt) = self.dropout_scheduler.lock() {
       if let Some(scheduler) = scheduler_opt.as_mut() {
           scheduler.step(1);
       }
   }
   

5. New helper method (lines 408-418):

   pub fn get_dropout_rate(&self) -> f64 {
       if let Ok(scheduler_opt) = self.dropout_scheduler.lock() {
           if let Some(scheduler) = scheduler_opt.as_ref() {
               return scheduler.get_rate();
           }
       }
       self.config.dropout_prob  // Fallback to static
   }
   

4. NetworkLayers Refactoring

Location: /home/jgrusewski/Work/foxhunt/ml/src/dqn/network.rs (lines 142-180)

Changes:

• Split new() to delegate to new_with_dropout_rate()
• new_with_dropout_rate() accepts explicit dropout rate parameter
• Allows dynamic dropout rate during forward pass

Test Coverage

Location: /home/jgrusewski/Work/foxhunt/ml/src/dqn/tests/dropout_scheduler_tests.rs

Test Cases (11 tests):

1. test_dropout_scheduler_creation - Verifies initialization
2. test_dropout_scheduler_linear_decay - Tests linear interpolation at 0%, 25%, 50%, 75%, 100%
3. test_dropout_scheduler_step_increment - Tests single-step increments
4. test_dropout_scheduler_current_step_tracking - Verifies step counter
5. test_qnetwork_config_with_dropout_schedule - Config validation
6. test_qnetwork_with_adaptive_dropout - Full integration test
7. test_dropout_scheduler_zero_decay_steps - Edge case: immediate final rate
8. test_dropout_scheduler_same_initial_and_final - Edge case: constant rate
9. test_dropout_scheduler_realistic_training_schedule - Real-world example (0.5 → 0.05 over 100k steps)
10. Tests boundary conditions and thread safety

Usage Example

// Configure adaptive dropout
let config = QNetworkConfig {
    state_dim: 64,
    num_actions: 3,
    dropout_schedule: Some((0.5, 0.1, 100_000)),  // 0.5 → 0.1 over 100k steps
    ..Default::default()
};

let network = QNetwork::new(config)?;

// During training:
// - Early (step 0): dropout_rate = 0.5 (high regularization)
// - Mid (step 50k): dropout_rate = 0.3 (moderate)
// - Late (step 100k+): dropout_rate = 0.1 (fine-tuning)

Benefits

1. Early Training (High Dropout):

   • Prevents overfitting to noisy early experiences
   • Encourages robust feature learning
   • Regularizes aggressive exploration

2. Late Training (Low Dropout):

   • Allows network to use full capacity
   • Enables precise fine-tuning
   • Improves final policy quality

3. Smooth Transition:

   • Linear decay avoids sudden behavior changes
   • Gradual adaptation matches learning progress

Backward Compatibility

• Default behavior unchanged: dropout_schedule: None uses static dropout_prob
• Opt-in feature: Only active when dropout_schedule is set
• No breaking changes: Existing code continues to work

Technical Notes

1. Thread Safety: Uses Mutex<Option<DropoutScheduler>> for safe concurrent access
2. Step Tracking: Increments on every forward() call
3. Saturation: saturating_add() prevents overflow
4. Progress Clamping: min(1.0) ensures rate doesn't go below final value

Files Modified

1. /home/jgrusewski/Work/foxhunt/ml/src/dqn/network.rs

   • Added DropoutScheduler struct (45 lines)
   • Extended QNetworkConfig with dropout_schedule field
   • Modified QNetwork to integrate scheduler
   • Refactored NetworkLayers::new() to support dynamic dropout

2. /home/jgrusewski/Work/foxhunt/ml/src/dqn/tests/dropout_scheduler_tests.rs (NEW)

   • 11 comprehensive tests (189 lines)
   • Unit tests for scheduler logic
   • Integration tests for QNetwork

3. /home/jgrusewski/Work/foxhunt/ml/src/dqn/tests/mod.rs

   • Added mod dropout_scheduler_tests;

Next Steps

To run tests (once existing compilation errors are fixed):

cargo test --package ml --lib dqn::tests::dropout_scheduler_tests
cargo test --package ml --lib dqn::network::tests  # Existing network tests

Status

✅ Implementation complete (TDD approach) ✅ Tests written (11 test cases) ⚠️ Tests pending - blocked by existing codebase compilation errors unrelated to this feature ✅ Backward compatible ✅ Documentation complete