foxhunt/docs/codebase-cleanup/WAVE26_P0.2_BATCH_DIVERSITY_SUMMARY.md

WAVE 26 P0.2: Batch Diversity Enforcement - Implementation Summary

Objective

Prevent sampling the same experience twice in consecutive batches to improve gradient diversity and training stability.

Changes Made

1. Added HashSet Tracking (/home/jgrusewski/Work/foxhunt/ml/src/dqn/prioritized_replay.rs)

Imports:

use std::collections::HashSet;

Struct Fields:

pub struct PrioritizedReplayBuffer {
    // ... existing fields ...

    /// Tracks recently sampled indices to prevent duplicates (WAVE 26 P0.2)
    recently_sampled: Arc<Mutex<HashSet<usize>>>,

    /// Counter for cooldown management - cleared every 50 batches (WAVE 26 P0.2)
    sample_counter: AtomicUsize,
}

Constructor:

pub fn new(config: PrioritizedReplayConfig) -> Result<Self, MLError> {
    // ...
    Ok(Self {
        // ... existing fields ...
        recently_sampled: Arc::new(Mutex::new(HashSet::new())),
        sample_counter: AtomicUsize::new(0),
        config,
    })
}

2. Modified sample() Method

Rejection Sampling with Fallback:

let mut recently_sampled = self.recently_sampled.lock();

for _ in 0..batch_size {
    // WAVE 26 P0.2: Batch diversity enforcement
    let mut attempts = 0;
    let max_attempts = 100;
    let idx = loop {
        let value = rng.gen::<f32>() * total_priority;
        let sampled_idx = tree.sample(value)?;

        // Check if this index was recently sampled
        if !recently_sampled.contains(&sampled_idx) {
            break sampled_idx;
        }

        attempts += 1;
        if attempts >= max_attempts {
            // Fallback: clear cooldown and use this sample
            recently_sampled.clear();
            break sampled_idx;
        }
    };

    // ... process experience ...
    recently_sampled.insert(idx);
}

// Clear cooldown every 50 batches
let sample_count = self.sample_counter.fetch_add(1, Ordering::Relaxed);
if sample_count % 50 == 49 {
    recently_sampled.clear();
}

3. Updated clear() Method

Reset Diversity Tracking:

pub fn clear(&self) {
    // ... existing clearing code ...

    self.sample_counter.store(0, Ordering::Release);

    // WAVE 26 P0.2: Clear diversity tracking
    {
        let mut recently_sampled = self.recently_sampled.lock();
        recently_sampled.clear();
    }

    // ... reset metrics ...
}

Test Coverage

Test 1: test_batch_diversity_no_duplicates

Purpose: Verify consecutive batches don't share indices Setup:

• Buffer capacity: 200
• Add 100 experiences
• Sample 2 batches of 32 each

Validation:

let set1: HashSet<_> = batch1_indices.iter().copied().collect();
let set2: HashSet<_> = batch2_indices.iter().copied().collect();
assert!(set1.is_disjoint(&set2));

Test 2: test_batch_diversity_cooldown_cleared_after_50_batches

Purpose: Verify cooldown resets after 50 batches Setup:

• Buffer capacity: 500
• Add 500 experiences
• Sample 51 batches of 10 each

Validation:

• First 50 batches: all indices must be unique
• 51st batch: can reuse indices (cooldown cleared)

Test 3: test_batch_diversity_fallback_on_exhaustion

Purpose: Verify graceful fallback when buffer too small Setup:

• Buffer capacity: 100
• Add only 40 experiences
• Sample 2 batches of 32 each

Validation:

• Both batches should succeed (no infinite loop)
• Overlap is expected and acceptable
• Fallback clears cooldown after 100 failed attempts

Algorithm Details

Rejection Sampling

1. Sample index from prioritized distribution
2. Check if index in recently_sampled HashSet
3. If yes, retry (max 100 attempts)
4. If max attempts reached, clear cooldown and use current sample
5. Add sampled index to recently_sampled

Cooldown Management

• Counter incremented after each sample() call
• Every 50 batches (sample_count % 50 == 49), clear recently_sampled
• Prevents unbounded HashSet growth during long training runs

Edge Cases Handled

1. Small buffers: Fallback prevents infinite loops
2. Long training: Automatic cooldown every 50 batches
3. Thread safety: Mutex protects HashSet
4. Buffer clear: Resets both counter and HashSet

Performance Characteristics

• Space Complexity: O(batch_size) for recently_sampled HashSet
• Time Complexity: O(1) expected per sample, O(100) worst case
• Memory Overhead: Minimal - HashSet cleared every 50 batches
• Fallback Safety: Prevents infinite loops in edge cases

Benefits

1. No Duplicate Experiences: Consecutive batches are guaranteed disjoint (up to 50 batches)
2. Better Gradient Diversity: Each training update uses unique experiences
3. Graceful Degradation: Handles small buffers without failing
4. Automatic Cleanup: Prevents memory growth over long runs
5. Minimal Overhead: O(1) HashSet lookups per sample

Integration Notes

• No API Changes: Existing sample() signature unchanged
• Backward Compatible: Feature automatically enabled
• Configuration: Hardcoded to 50-batch cooldown (can be parameterized later)
• Thread Safe: Uses Arc<Mutex<HashSet<usize>>> for concurrent access

Next Steps (Future Enhancements)

1. Make cooldown period configurable via PrioritizedReplayConfig
2. Add metrics to track fallback frequency
3. Consider adaptive cooldown based on buffer utilization
4. Expose diversity statistics in PrioritizedReplayMetrics

Files Modified

• /home/jgrusewski/Work/foxhunt/ml/src/dqn/prioritized_replay.rs
  • Added HashSet import
  • Added recently_sampled and sample_counter fields
  • Modified new(), sample(), and clear() methods
  • Added 3 comprehensive tests

Documentation

• Implementation details: /home/jgrusewski/Work/foxhunt/docs/codebase-cleanup/batch_diversity_implementation.md
• Summary: This file

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Status: ✅ Implementation complete, tests added, awaiting test results Wave: WAVE 26 P0.2 Date: 2025-11-27