feat(ml): WAVE 29 DQN Codebase Cleanup & Refactoring Campaign

BREAKING CHANGES:
- Removed orphaned dqn.rs monolithic trainer (4,975 lines)
- Removed orphaned dqn_ensemble.rs module (816 lines)
- Removed orphaned tft.rs and tft_complete_int8_integration_test.rs
- TFT trainer split into modular directory structure

DQN Module Refactoring:
- Split trainers/dqn.rs into modular structure (config.rs, statistics.rs, trainer.rs)
- Fixed hyperopt 39D search space (continuous params only)
- Boolean flags (use_dueling, use_double_dqn, use_per, use_noisy_nets) are now FIXED architectural decisions
- use_distributional defaults to false (Candle BUG #36 - scatter_add gradient issues)

Clean Module Structure:
- ml/src/trainers/dqn/ directory with proper mod.rs exports
- ml/src/trainers/tft/ directory with config.rs, types.rs, model.rs, trainer.rs, tests.rs
- All P0 features validated: TD-error clamping, batch diversity, LR scheduler, priority staleness

Documentation:
- Added comprehensive docs in docs/codebase-cleanup/
- ADR-001 for DQN refactoring decisions
- Rainbow DQN component matrix and quick reference guides

Build Status: Compiles with zero errors

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

archive/scripts/deploy_dqn_hyperopt_optimized.sh

@@ -0,0 +1,98 @@
+#!/usr/bin/env bash
+# DQN Hyperparameter Optimization Deployment (Optimized Parameters)
+# Generated: 2025-11-02
+# Based on: DQN_HYPEROPT_RESULTS_SUMMARY.md (Trial #8, Run 1)
+#
+# BEST HYPERPARAMETERS:
+# - Learning Rate: 4.89e-5 (ultra-low, critical for DQN stability)
+# - Batch Size: 151
+# - Gamma: 0.9838
+# - Epsilon Decay: 0.9917
+# - Buffer Size: 185066
+# - Trials: 50 (complete the hyperopt properly)
+#
+# CRITICAL NOTE: DQN requires ultra-low learning rates (4.89e-5 to 1.40e-4)
+# This is 10-100x lower than PPO's optimal range due to off-policy replay buffer dynamics.
+
+set -euo pipefail
+
+# Configuration
+GPU_TYPE="${1:-RTX A4000}"  # Default: RTX A4000 ($0.25/hr), alternative: RTX 4090 ($0.59/hr)
+DOCKER_IMAGE="jgrusewski/foxhunt:dqn-checkpoint-fix"
+PARQUET_FILE="/runpod-volume/test_data/ES_FUT_180d.parquet"
+OUTPUT_BASE="/runpod-volume/ml_training"
+TIMESTAMP=$(date +%Y%m%d_%H%M%S)
+OUTPUT_DIR="${OUTPUT_BASE}/dqn_hyperopt_optimized_${TIMESTAMP}"
+
+# Best hyperparameters from Trial #8 (Run 1)
+TRIALS=50
+EPOCHS=20  # Per trial
+N_INITIAL=2  # Initial random samples
+SEED=42  # Reproducibility
+
+# Early stopping configuration
+EARLY_STOPPING_PLATEAU_WINDOW=5
+EARLY_STOPPING_MIN_EPOCHS=10
+
+# Display configuration
+echo "=========================================="
+echo "DQN Hyperopt Deployment (Optimized)"
+echo "=========================================="
+echo "GPU: ${GPU_TYPE}"
+echo "Docker Image: ${DOCKER_IMAGE}"
+echo "Parquet File: ${PARQUET_FILE}"
+echo "Output Directory: ${OUTPUT_DIR}"
+echo ""
+echo "Hyperopt Configuration:"
+echo "  Trials: ${TRIALS}"
+echo "  Epochs per trial: ${EPOCHS}"
+echo "  Initial random samples: ${N_INITIAL}"
+echo "  Random seed: ${SEED}"
+echo ""
+echo "Expected Duration: ~40 min (RTX A4000) or ~25 min (RTX 4090)"
+echo "Expected Cost: ~\$0.17 (RTX A4000) or ~\$0.25 (RTX 4090)"
+echo "=========================================="
+echo ""
+
+# Build hyperopt command
+COMMAND="hyperopt_dqn_demo \
+  --parquet-file ${PARQUET_FILE} \
+  --trials ${TRIALS} \
+  --epochs ${EPOCHS} \
+  --n-initial ${N_INITIAL} \
+  --seed ${SEED} \
+  --base-dir ${OUTPUT_DIR} \
+  --run-type hyperopt \
+  --early-stopping-plateau-window ${EARLY_STOPPING_PLATEAU_WINDOW} \
+  --early-stopping-min-epochs ${EARLY_STOPPING_MIN_EPOCHS}"
+
+echo "Command: ${COMMAND}"
+echo ""
+
+# Deploy using foxhunt-deploy CLI
+if [ ! -f "/home/jgrusewski/Work/foxhunt/target/release/foxhunt-deploy" ]; then
+    echo "ERROR: foxhunt-deploy CLI not found at /home/jgrusewski/Work/foxhunt/target/release/foxhunt-deploy"
+    echo "Please build it first: cargo build --release -p foxhunt-deploy"
+    exit 1
+fi
+
+# Deploy pod
+echo "Deploying RunPod pod..."
+/home/jgrusewski/Work/foxhunt/target/release/foxhunt-deploy deploy \
+    --gpu-type "${GPU_TYPE}" \
+    --tag dqn-checkpoint-fix \
+    --command "${COMMAND}" \
+    --name "dqn-hyperopt-optimized-$(date +%Y%m%d-%H%M%S)" \
+    --yes
+
+echo ""
+echo "=========================================="
+echo "Deployment Complete!"
+echo "=========================================="
+echo ""
+echo "Monitor progress:"
+echo "  python3 scripts/python/runpod/monitor_logs.py <pod_id>"
+echo ""
+echo "Verify results (after completion):"
+echo "  aws s3 ls s3://se3zdnb5o4/ml_training/dqn_hyperopt_optimized_${TIMESTAMP}/ --profile runpod --endpoint-url https://s3api-eur-is-1.runpod.io --recursive"
+echo ""