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_hyperopt_direct.sh

@@ -0,0 +1,176 @@
+#!/bin/bash
+set -e
+
+# Load RunPod credentials
+source .env.runpod
+
+TIMESTAMP=$(date +%Y%m%d_%H%M%S)
+
+echo "========================================="
+echo "RunPod Hyperopt Deployment (Direct REST API)"
+echo "========================================="
+echo ""
+echo "Deploying 2 pods:"
+echo "  1. DQN Hyperopt"
+echo "  2. PPO Hyperopt"
+echo ""
+
+# Deploy DQN Hyperopt Pod
+echo "========================================="
+echo "1. DEPLOYING DQN HYPEROPT POD"
+echo "========================================="
+
+DQN_OUTPUT_DIR="dqn_hyperopt_${TIMESTAMP}"
+
+DQN_PAYLOAD=$(cat <<EOF
+{
+  "cloudType": "SECURE",
+  "computeType": "GPU",
+  "dataCenterIds": ["EUR-IS-1"],
+  "dataCenterPriority": "availability",
+  "gpuTypeIds": ["NVIDIA RTX A4000"],
+  "gpuCount": 1,
+  "name": "foxhunt-dqn-hyperopt-${TIMESTAMP}",
+  "imageName": "jgrusewski/foxhunt-hyperopt:latest",
+  "containerDiskInGb": 50,
+  "networkVolumeId": "${RUNPOD_VOLUME_ID}",
+  "volumeMountPath": "/runpod-volume",
+  "dockerStartCmd": [
+    "hyperopt_dqn_demo",
+    "--parquet-file", "/runpod-volume/test_data/ES_FUT_180d.parquet",
+    "--trials", "50",
+    "--epochs", "100",
+    "--base-dir", "/runpod-volume/ml_training/${DQN_OUTPUT_DIR}"
+  ],
+  "containerRegistryAuthId": "${RUNPOD_CONTAINER_REGISTRY_AUTH_ID}",
+  "ports": ["8888/http", "22/tcp"],
+  "interruptible": false
+}
+EOF
+)
+
+echo "Deploying DQN pod..."
+DQN_RESPONSE=$(curl -s -X POST https://rest.runpod.io/v1/pods \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer ${RUNPOD_API_KEY}" \
+  -d "$DQN_PAYLOAD")
+
+DQN_POD_ID=$(echo "$DQN_RESPONSE" | jq -r '.id // "ERROR"')
+
+if [ "$DQN_POD_ID" = "ERROR" ] || [ "$DQN_POD_ID" = "null" ]; then
+  echo "ERROR: Failed to deploy DQN pod"
+  echo "Response: $DQN_RESPONSE"
+  exit 1
+fi
+
+echo "✅ DQN Pod Deployed Successfully"
+echo "  Pod ID: $DQN_POD_ID"
+echo "  GPU: $(echo "$DQN_RESPONSE" | jq -r '.machine.gpuType.displayName // "RTX A4000"')"
+echo "  Datacenter: $(echo "$DQN_RESPONSE" | jq -r '.machine.dataCenterId // "EUR-IS-1"')"
+echo "  Cost: \$$(echo "$DQN_RESPONSE" | jq -r '.costPerHr // "0.25"')/hr"
+echo "  Output: /runpod-volume/ml_training/${DQN_OUTPUT_DIR}"
+echo ""
+
+# Deploy PPO Hyperopt Pod
+echo "========================================="
+echo "2. DEPLOYING PPO HYPEROPT POD"
+echo "========================================="
+
+PPO_OUTPUT_DIR="ppo_hyperopt_${TIMESTAMP}"
+
+PPO_PAYLOAD=$(cat <<EOF
+{
+  "cloudType": "SECURE",
+  "computeType": "GPU",
+  "dataCenterIds": ["EUR-IS-1"],
+  "dataCenterPriority": "availability",
+  "gpuTypeIds": ["NVIDIA RTX A4000"],
+  "gpuCount": 1,
+  "name": "foxhunt-ppo-hyperopt-${TIMESTAMP}",
+  "imageName": "jgrusewski/foxhunt-hyperopt:latest",
+  "containerDiskInGb": 50,
+  "networkVolumeId": "${RUNPOD_VOLUME_ID}",
+  "volumeMountPath": "/runpod-volume",
+  "dockerStartCmd": [
+    "hyperopt_ppo_demo",
+    "--parquet-file", "/runpod-volume/test_data/ES_FUT_180d.parquet",
+    "--trials", "50",
+    "--episodes", "2000",
+    "--base-dir", "/runpod-volume/ml_training/${PPO_OUTPUT_DIR}",
+    "--early-stopping-min-epochs", "50"
+  ],
+  "containerRegistryAuthId": "${RUNPOD_CONTAINER_REGISTRY_AUTH_ID}",
+  "ports": ["8888/http", "22/tcp"],
+  "interruptible": false
+}
+EOF
+)
+
+echo "Deploying PPO pod..."
+PPO_RESPONSE=$(curl -s -X POST https://rest.runpod.io/v1/pods \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer ${RUNPOD_API_KEY}" \
+  -d "$PPO_PAYLOAD")
+
+PPO_POD_ID=$(echo "$PPO_RESPONSE" | jq -r '.id // "ERROR"')
+
+if [ "$PPO_POD_ID" = "ERROR" ] || [ "$PPO_POD_ID" = "null" ]; then
+  echo "ERROR: Failed to deploy PPO pod"
+  echo "Response: $PPO_RESPONSE"
+  exit 1
+fi
+
+echo "✅ PPO Pod Deployed Successfully"
+echo "  Pod ID: $PPO_POD_ID"
+echo "  GPU: $(echo "$PPO_RESPONSE" | jq -r '.machine.gpuType.displayName // "RTX A4000"')"
+echo "  Datacenter: $(echo "$PPO_RESPONSE" | jq -r '.machine.dataCenterId // "EUR-IS-1"')"
+echo "  Cost: \$$(echo "$PPO_RESPONSE" | jq -r '.costPerHr // "0.25"')/hr"
+echo "  Output: /runpod-volume/ml_training/${PPO_OUTPUT_DIR}"
+echo ""
+
+# Summary
+echo "========================================="
+echo "DEPLOYMENT SUMMARY"
+echo "========================================="
+echo ""
+echo "DQN Hyperopt:"
+echo "  Pod ID: $DQN_POD_ID"
+echo "  Output: /runpod-volume/ml_training/${DQN_OUTPUT_DIR}"
+echo "  Trials: 50"
+echo "  Epochs/trial: 100"
+echo "  Expected duration: 12-25 min"
+echo "  Expected cost: \$0.05-\$0.10"
+echo ""
+echo "PPO Hyperopt:"
+echo "  Pod ID: $PPO_POD_ID"
+echo "  Output: /runpod-volume/ml_training/${PPO_OUTPUT_DIR}"
+echo "  Trials: 50"
+echo "  Episodes/trial: 2000"
+echo "  Expected duration: 10-20 min"
+echo "  Expected cost: \$0.04-\$0.08"
+echo ""
+echo "TOTAL ESTIMATED COST: \$0.09-\$0.18"
+echo ""
+echo "========================================="
+echo "MONITORING COMMANDS"
+echo "========================================="
+echo ""
+echo "Monitor DQN logs:"
+echo "  ./monitor_dqn_hyperopt_pod.sh $DQN_POD_ID"
+echo ""
+echo "Monitor PPO logs:"
+echo "  ./monitor_ppo_hyperopt_pod.sh $PPO_POD_ID"
+echo ""
+echo "View pods in dashboard:"
+echo "  https://www.runpod.io/console/pods"
+echo ""
+echo "Check S3 results (after completion):"
+echo "  aws s3 ls s3://se3zdnb5o4/ml_training/${DQN_OUTPUT_DIR}/ --profile runpod --recursive"
+echo "  aws s3 ls s3://se3zdnb5o4/ml_training/${PPO_OUTPUT_DIR}/ --profile runpod --recursive"
+echo ""
+echo "Terminate pods (when complete):"
+echo "  curl -X POST https://rest.runpod.io/v1/pods/${DQN_POD_ID}/terminate \\"
+echo "    -H \"Authorization: Bearer \$RUNPOD_API_KEY\""
+echo "  curl -X POST https://rest.runpod.io/v1/pods/${PPO_POD_ID}/terminate \\"
+echo "    -H \"Authorization: Bearer \$RUNPOD_API_KEY\""
+echo ""