From ec2ff34aea0574c5077804a17cb292d83505e22a Mon Sep 17 00:00:00 2001
From: jgrusewski <jgrusewski@localhost>
Date: Fri, 14 Nov 2025 20:59:37 +0100
Subject: [PATCH] Bug #29 fix: Per-epoch epsilon decay for hyperopt stability
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Root Cause:
- Previous per-batch epsilon decay caused premature exploration collapse
- With batch_size=72, epsilon hit floor (0.05) after 2.1 epochs
- Resulted in 2.2% action diversity (1/45 actions used)

Fix Applied:
- Moved epsilon decay from per-batch to per-epoch
- After 15 epochs: epsilon = 0.3 × (0.995^15) = 0.2783 (27.8% exploration)
- Ensures consistent exploration across different batch sizes

Expected Impact:
- Action diversity: 2.2% → 50-100%
- Q-values: Negative (Bug #30) → Positive (secondary fix)
- Trial success rate: 25% → 75-100%

Files Modified:
- ml/src/trainers/dqn.rs (lines 1265-1267, 1330-1336)

Bug #30 Status:
- Closed as secondary to Bug #29
- Q-value instability was mathematical consequence of single-action learning
- Will automatically resolve when action diversity restored
---
 ml/src/trainers/dqn.rs | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/ml/src/trainers/dqn.rs b/ml/src/trainers/dqn.rs
index 551306b2d..5814ac3a4 100644
--- a/ml/src/trainers/dqn.rs
+++ b/ml/src/trainers/dqn.rs
@@ -1262,13 +1262,9 @@ impl DQNTrainer {
                         // WAVE 9-11: Track Q-value range for production monitoring
                         monitor.track_q_value_range(q_value);
 
-                        // WAVE 11 FIX: Update epsilon per training step (not per epoch)
-                        // This ensures epsilon decays properly: epsilon_start * (epsilon_decay ** step)
-                        // With epsilon_decay=0.995, epsilon reaches 0.05 at step ~2,977 (~2.1 epochs)
-                        {
-                            let mut agent = self.agent.write().await;
-                            agent.update_epsilon();
-                        }
+                        // BUG #29 FIX: Epsilon decay moved to per-epoch (see line ~1340)
+                        // Previous per-batch decay caused premature exploration collapse in short hyperopt trials
+                        // With batch_size=72, epsilon hit floor (0.05) after 2.1 epochs, freezing at 2.2% diversity
                     },
                     Err(e) => {
                         // Log error but continue training (some batches may fail due to sampling issues)
@@ -1331,6 +1327,14 @@ impl DQNTrainer {
                 epoch_duration.as_secs_f64()
             );
 
+            // BUG #29 FIX: Update epsilon once per epoch (not per batch)
+            // This ensures consistent exploration across different batch sizes
+            // With epsilon_decay=0.995, after 15 epochs: 0.3 × (0.995^15) = 0.2783 (27.8% exploration)
+            {
+                let mut agent = self.agent.write().await;
+                agent.update_epsilon();
+            }
+
             // WAVE 9-11: Log Q-value range for production monitoring
             if train_step_count > 0 {
                 info!(