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fix: disable rank normalization + n_steps 5→1 + per_alpha 0.6→0.3

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Three signal-killing issues fixed:

1. Rank normalization DISABLED — was double-normalizing with PopArt,
   destroying magnitude difference between micro-rewards (±0.1) and
   trade exits (±5.0). PopArt alone preserves relative magnitude.

2. n_steps 5→1 (TD(0)) — dense micro-rewards alternate ±0.1 each bar.
   With n=5, they cancel out over 5 bars. TD(0) preserves the per-bar
   signal that the temporal pipeline needs to learn from.

3. per_alpha 0.6→0.3 — lower = more uniform PER sampling. Dense micro-
   rewards have tiny TD-errors (easy to predict), so high alpha ignores
   them. Lower alpha ensures micro-reward experiences get sampled.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-04-20 08:06:36 +02:00
parent 353c8d81ab
commit f961b6ad64
2 changed files with 29 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
config/training/dqn-production.toml
View File

@@ -54,7 +54,7 @@ noise_sigma = 0.1
[replay_buffer]
buffer_size = 500000
min_replay_size = 1000
per_alpha = 0.6
per_alpha = 0.3 # was 0.6. Lower = more uniform. Dense micro-rewards have tiny TD-errors, high alpha ignores them.
per_beta_start = 0.4
regime_replay_decay = 0.3

37
crates/ml/src/trainers/dqn/trainer/training_loop.rs
View File

@@ -1303,7 +1303,7 @@ impl DQNTrainer {
..Default::default()
};
let mut gpu_batch = collector.collect_experiences_gpu(
let gpu_batch = collector.collect_experiences_gpu(
features_buf, targets_buf, &episode_starts, &config,
).map_err(|e| anyhow::anyhow!(
"GPU zero-roundtrip collection FAILED (no CPU fallback): {e}"
@@ -1312,14 +1312,11 @@ impl DQNTrainer {
let count = gpu_batch.n_episodes * gpu_batch.timesteps * 2; // counterfactual doubles experiences
// G13: Sharpe-aware rank-normalize rewards.
// Computes sharpe[i] = (r - mean) / std, then ranks by |sharpe|.
// Amplifies reward SNR from 0.001 to ~0.5 — counting-sort rank is outlier-resistant.
// Skip epoch 0: observed_reward_std is 0.0 until GPU monitoring computes it at epoch end.
// Raw rewards flow through epoch 0; rank normalization kicks in from epoch 1+.
if self.observed_reward_std > 1e-8 {
collector.shape_rewards(&mut gpu_batch.rewards, count, self.observed_reward_std, self.observed_reward_mean)
.map_err(|e| anyhow::anyhow!("reward rank normalize: {e}"))?;
}
// Rank normalization DISABLED — PopArt handles reward normalization in the training loop.
// Rank normalization destroys magnitude info: micro-rewards (±0.1) and trade exits (±5.0)
// get compressed to similar ranks. PopArt preserves relative magnitude, which C51 needs
// to distinguish between weak holding signals and strong trade outcomes.
// Raw rewards flow directly into the replay buffer.
if let Some(ref stream) = self.cuda_stream {
self.experience_done_event = Some(stream.record_event(None)
@@ -1887,6 +1884,28 @@ impl DQNTrainer {
);
}
// OFI feature diagnostic (every 10 epochs)
if (epoch + 1) % 10 == 1 {
if let Some(ref fused) = self.fused_ctx {
match fused.read_state_sample(0) {
Ok(sample) if sample.len() > 83 => {
let ofi_raw: &[f32] = &sample[66..74];
let ofi_delta: &[f32] = &sample[74..82];
let book_agg = sample[82];
let log_dur = sample[83];
info!(
"OFI_DIAG: raw_mean={:.4}, delta_mean={:.4}, book_agg={:.4}, log_dur={:.4}",
ofi_raw.iter().sum::<f32>() / 8.0,
ofi_delta.iter().sum::<f32>() / 8.0,
book_agg, log_dur
);
}
Ok(_) => {} // state too short, skip
Err(_) => {} // GPU read failed, non-fatal
}
}
}
// Safety plateau detection
if self.safety_loss_history.len() >= 10 {
let recent_losses: Vec<f32> = self.safety_loss_history