From 5f061bec0653222097eebc16dbc1c9ed9ec79242 Mon Sep 17 00:00:00 2001
From: Frank14f <1515444314@qq.com>
Date: Fri, 3 Jul 2026 00:21:49 +0800
Subject: [PATCH] =?UTF-8?q?feat(train):=20cross-Re=20transfer=20pipeline?=
 =?UTF-8?q?=20=E2=80=94=20re60/re200/re400=20calibrations=20+=20script?=
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- Add crossre_transfer.sh: calibrate → transfer-train for re60→re200→re400
- Add re60 config (ν=0.006667, SI=800, uniform+free-slip, very weak shedding)
- Calibrate re60, re200, re400: FORCE_SCALE, SENS_SCALE, dtw_norm_scale, SIM_BP
- Fix all paths: use DynamisLab submodule CelerisLab, remove external ~/CelerisLab
- Remove _clean_cache() from envs/calibrate — CelerisLab handles internally
- Move V4 backups to old/: env_karman_2000x600, train_karman_2000x600, etc.
- train_karman.py: save model + vecnormalize every episode (non-optional)
- Update TRAIN_KNOWLEDGE.md: file structure, calibration table, cross-re guide
- All 3 Re verified: 5-episode transfer test passed (re60: 0.64, re200: 0.43, re400: 0.49)

Co-authored-by: Cursor <cursoragent@cursor.com>
---
 configs/config_lbm_karman_2000x600_re60.json  |  50 ++
 src/drl_pinball/train/TRAIN_KNOWLEDGE.md      | 557 ++++++------------
 src/drl_pinball/train/calibrate.py            |  40 +-
 .../calibrations/illusion_1L/calibration.json |  12 +-
 .../illusion_1L/target_harmonics.json         | 176 +++---
 .../train/calibrations/re200/calibration.json |  49 ++
 .../train/calibrations/re400/calibration.json |  49 ++
 .../train/calibrations/re60/calibration.json  |  49 ++
 src/drl_pinball/train/crossre_transfer.sh     | 115 ++++
 src/drl_pinball/train/env_illusion.py         |  40 +-
 src/drl_pinball/train/env_karman.py           |  10 -
 src/drl_pinball/train/env_vortex.py           |  10 -
 src/drl_pinball/train/launch_multi.sh         |   8 +-
 .../train/{ => old}/analyze_final.py          |   0
 .../train/{ => old}/env_karman_2000x600.py    |   2 +-
 .../{ => old}/phase0_baseline_measure.py      |   2 +-
 src/drl_pinball/train/{ => old}/prompt.md     |   0
 .../train/{ => old}/train_karman_2000x600.py  |   0
 src/drl_pinball/train/train_karman.py         |   6 +-
 19 files changed, 641 insertions(+), 534 deletions(-)
 create mode 100644 configs/config_lbm_karman_2000x600_re60.json
 create mode 100644 src/drl_pinball/train/calibrations/re200/calibration.json
 create mode 100644 src/drl_pinball/train/calibrations/re400/calibration.json
 create mode 100644 src/drl_pinball/train/calibrations/re60/calibration.json
 create mode 100755 src/drl_pinball/train/crossre_transfer.sh
 rename src/drl_pinball/train/{ => old}/analyze_final.py (100%)
 rename src/drl_pinball/train/{ => old}/env_karman_2000x600.py (99%)
 rename src/drl_pinball/train/{ => old}/phase0_baseline_measure.py (99%)
 rename src/drl_pinball/train/{ => old}/prompt.md (100%)
 rename src/drl_pinball/train/{ => old}/train_karman_2000x600.py (100%)

diff --git a/configs/config_lbm_karman_2000x600_re60.json b/configs/config_lbm_karman_2000x600_re60.json
new file mode 100644
index 0000000..5772628
--- /dev/null
+++ b/configs/config_lbm_karman_2000x600_re60.json
@@ -0,0 +1,50 @@
+{
+    "_doc": "Karman Cloak Re60: uniform inlet, free-slip walls, 2000x600 grid. nu = U0*2D/60 = 0.006667.",
+    "grid": {
+        "lattice_model": "D2Q9",
+        "nx": 2000,
+        "ny": 600,
+        "nz": 1
+    },
+    "physics": {
+        "data_type": "FP32",
+        "viscosity": 0.006667,
+        "velocity": 0.01,
+        "rho": 1.0
+    },
+    "method": {
+        "collision": "MRT",
+        "streaming": "double_buffer",
+        "store_precision": "FP32",
+        "ddf_shifting": false,
+        "les": {
+            "enabled": false,
+            "cs": 0.16,
+            "closed_form": true
+        },
+        "trt": {
+            "magic_param": 0.1875
+        },
+        "inlet": {
+            "profile": "uniform",
+            "scheme": "regularized",
+            "trt_neq_damp": 0.5,
+            "regularized_neq_damp": 0.5
+        },
+        "outlet": {
+            "mode": "neq_extrap",
+            "backflow_clamp": true,
+            "blend_alpha": 0.7,
+            "srt_neq_damp": 0.5
+        },
+        "y_wall_bc": "free_slip",
+        "omega_guard": {
+            "min": 0.01,
+            "max": 1.99
+        }
+    },
+    "cuda": {
+        "threads_per_block": 256,
+        "compute_capability": "auto"
+    }
+}
diff --git a/src/drl_pinball/train/TRAIN_KNOWLEDGE.md b/src/drl_pinball/train/TRAIN_KNOWLEDGE.md
index 9216d9f..4d181a1 100644
--- a/src/drl_pinball/train/TRAIN_KNOWLEDGE.md
+++ b/src/drl_pinball/train/TRAIN_KNOWLEDGE.md
@@ -1,7 +1,10 @@
 # Karman Cloak Training — Knowledge Document (V5)
 
-> **V5 (2026-07-01)**: Parameterized, calibration-driven, no_bias only, multi-GPU ready.
-> Original V4 files preserved as `env_karman_2000x600.py` and `train_karman_2000x600.py`.
+> **V5 (2026-07-03)**: Parameterized, calibration-driven, no_bias only.
+> All paths use DynamisLab submodule `CelerisLab/` (no external dev dir).
+> Cross-Re transfer pipeline verified: re60, re200, re400.
+> Every episode saves model checkpoint.
+> V4 backups moved to `old/`.
 
 ---
 
@@ -12,257 +15,199 @@ hydrodynamic cloaking — making the downstream flow match the "undisturbed" flo
 (as if the pinball weren't there). The upstream disturbance cylinder generates a
 Kármán vortex street; the pinball must cancel it.
 
-- **CFD**: CelerisLab LBM solver, D2Q9 MRT, 2000x600 grid, uniform inlet, free-slip walls
+- **CFD**: CelerisLab LBM solver (DynamisLab submodule), D2Q9 MRT, 2000x600 grid, uniform inlet, free-slip walls
 - **DRL**: PPO with Sin activation, 64x64 MLP, SB3 + VecNormalize
 - **V5 mode**: No_bias only (ACTION_SCALE=12, ACTION_BIAS=[0,0,0]). All cases use calibration-first workflow.
 
-## 0. V5 Quick Start (Server Deployment)
+## 0. Quick Start
+
+### 0.1 Single-Re training (re100)
 
 ```bash
-# 1. Calibrate (once per case, ~5 min on single GPU)
+# 1. Calibrate (~5 min)
 cd src/drl_pinball/train
 conda run -n pycuda_3_10 python calibrate.py \
     --case re100 --device-id 0 \
     --config configs/config_lbm_karman_2000x600.json
 
-# 2. Launch multi-seed training on server (sequential, 7 min between GPUs)
+# 2. Multi-seed training (server, sequential 7-min delay between GPUs)
 bash launch_multi.sh \
     --case-name re100_karman --seeds 42,43,44,45,46,47 \
     --gpus 0,1,2,3,4,5 --episodes 500 \
     --config configs/config_lbm_karman_2000x600.json \
     --calibration calibrations/re100/calibration.json
 
-# 3. Transfer learning to another Re
-conda run -n pycuda_3_10 python calibrate.py \
-    --case re200 --device-id 0 --si 500 \
-    --config configs/config_lbm_karman_2000x600_re200.json
-
-bash launch_multi.sh \
-    --case-name re200_karman --seeds 42,43,44 --gpus 0,1,2 \
-    --config configs/config_lbm_karman_2000x600_re200.json \
-    --calibration calibrations/re200/calibration.json \
-    --transfer output/re100_karman_seed42/models/best_model.zip
-
-# 4. Monitor
+# 3. Monitor
 tail -f output/re100_karman_seed42/train.log
 tensorboard --logdir output/re100_karman_seed42/tb --port 6006
 ```
 
+### 0.2 Cross-Re transfer (re60, re200, re400)
+
+```bash
+# Local test (5 episodes each, quick verification):
+bash crossre_transfer.sh --re-list 60,200,400 --test-episodes 5
+
+# Server production (200 episodes each):
+# !! BEFORE PUSHING TO SERVER: update BEST_MODEL path in crossre_transfer.sh !!
+bash crossre_transfer.sh --re-list 60,200,400
+# Then push to server and run there.
+```
+
+### 0.3 Path notes for server deployment
+
+`crossre_transfer.sh` has relative paths via `SCRIPT_DIR`/`REPO_DIR`.
+Only `BEST_MODEL` needs updating — point to the best Re100 model from
+multi-seed training (e.g. `output/re100_karman_seed42/models/best_model.zip`).
+
 ---
 
-## 2. File Structure (V5)
+## 2. File Structure (V5 Final)
 
 ```
 train/
 ├── __init__.py
 │
-├── # V5 ACTIVE FILES (parameterized, no_bias, calibration-driven)
-│
+├── # ACTIVE FILES
 ├── calibrate.py                # Phase 0 calibration (produces calibration.json + target.npy)
-├── env_karman.py               # Parameterized Karman cloak env (loads calibration JSON)
+├── env_karman.py               # Parameterized Karman cloak env
+├── env_illusion.py             # Parameterized Illusion env
 ├── env_vortex.py               # Vortex cloak env (lamb/taylor, MAX_STEPS=150)
-├── train_karman.py             # Parameterized training script (--calibration, --config)
+├── train_karman.py             # Parameterized training script (every ep saves model)
+├── train_illusion.py           # Illusion training script
 ├── launch_multi.sh             # Sequential multi-GPU server launcher
-│
-├── # V5 SUPPORT FILES (unchanged from V4)
-│
+├── crossre_transfer.sh         # Cross-Re transfer: calibrate + train (re60→re200→re400)
 ├── symmetry_wrapper.py         # G-mirror symmetry augmentation (per-rollout)
-├── phase0_baseline_measure.py  # Legacy baseline measurement tool (reference)
 ├── visualize_and_analyze.py    # Flow-field visualization & analysis
+├── SERVER_DEPLOY.md            # Server deployment instructions
+├── TRAIN_KNOWLEDGE.md          # This file
 │
-├── # V4 BACKUP FILES (preserved, NOT active)
+├── old/                        # Archived V4 files (NOT active)
+│   ├── env_karman_2000x600.py
+│   ├── train_karman_2000x600.py
+│   ├── phase0_baseline_measure.py
+│   └── analyze_final.py
 │
-├── env_karman_2000x600.py      # Original V4 env (hardcoded FORCE_SCALE, bias support)
-├── train_karman_2000x600.py    # Original V4 training (--no-bias flag)
-├── analyze_final.py            # V3 training curve plotting (legacy paths)
-│
-├── # Calibration & output
-│
-├── calibrations/               # Per-case calibration files
-│   └── re100/
-│       ├── calibration.json    # FORCE_SCALE, SENS_SCALE, SIM_BP/VAL, etc.
-│       ├── target.npy          # Target sensor signals (FIFO_LEN, 6)
-│       └── calibrate.log       # Calibration run log
+├── calibrations/               # Per-case calibration files (IMMUTABLE)
+│   ├── re60/
+│   │   ├── calibration.json    # SI=800, FORCE_SCALE=0.0021, SENS_SCALE=0.72
+│   │   ├── target.npy
+│   │   └── calibrate.log
+│   ├── re100/
+│   │   ├── calibration.json    # SI=800, FORCE_SCALE=0.0024, SENS_SCALE=0.75
+│   │   └── calibrate.log
+│   ├── re200/
+│   │   ├── calibration.json    # SI=500, FORCE_SCALE=0.0026, SENS_SCALE=0.90
+│   │   ├── target.npy
+│   │   └── calibrate.log
+│   ├── re400/
+│   │   ├── calibration.json    # SI=400, FORCE_SCALE=0.0042, SENS_SCALE=0.98
+│   │   ├── target.npy
+│   │   └── calibrate.log
+│   └── illusion_1L/
+│       ├── calibration.json
+│       ├── target.npy
+│       ├── target_harmonics.json
+│       └── calibrate.log
 │
 └── output/                     # Training outputs
-    └── re100_karman_seed42/
-        ├── models/             # best_model.zip, final_model.zip, chkpt_ep*.zip
+    └── <case>_seed<N>/
+        ├── models/             # ep0001_model.zip, ..., best_model.zip, final_model.zip
         ├── tb/                 # TensorBoard logs
-        ├── train.log           # Training run log
-        ├── calibration.json    # Copy of calibration used
-        ├── vec_normalize.pkl   # VecNormalize statistics
-        └── meta.json           # Run metadata
+        ├── train.log
+        ├── calibration.json
+        ├── vec_normalize.pkl
+        └── meta.json
 ```
 
 ---
 
-## 3. Calibration Workflow (V5 — ALWAYS RUN FIRST)
+## 3. Calibration Workflow (ALWAYS RUN FIRST)
 
 Every case MUST run `calibrate.py` before training. This produces:
 
 - `calibration.json`: FORCE_SCALE, SENS_SCALE, dtw_norm_scale, SIM_BP, SIM_VAL, reward constants
-- `target.npy`: Target sensor signals (150 steps x 6 channels, legacy-equiv)
+- `target.npy`: Target sensor signals (150 steps x 6 channels)
 
-The calibration measures Stage0 (zero rotation) and Stage1 (open-loop reference action
-equivalent to legacy [0,-4,4]*U0 bias) to compute:
-- FORCE_SCALE = combined max|force| across both stages
-- SENS_SCALE = combined max|sensor| (legacy-equiv) across both stages
-- dtw_norm_scale = target's 3 uy-channel avg std
-- SIM_BP/SIM_VAL = piecewise DTW mapping from Stage0/Stage1 sim mean values
+The calibration measures Stage0 (zero rotation) and Stage1 (open-loop reference) to compute normalization
+constants. Calibration is **IMMUTABLE** — once produced, never modify.
 
-Calibration is IMMUTABLE — once produced, never modify. Training and inference use it as-is.
+### Calibration Results (all Re)
+
+| Case | SI | FORCE_SCALE | SENS_SCALE | dtw_norm_scale | Stage0 sim | Stage1 sim |
+|------|-----|-------------|------------|----------------|-----------|-----------|
+| re60 | 800 | 0.0021 | 0.72 | 0.107 | 0.41 | 0.61 |
+| re100 | 800 | 0.0024 | 0.75 | 0.204 | 0.32 | 0.82 |
+| re200 | 500 | 0.0026 | 0.90 | 0.269 | 0.45 | 0.77 |
+| re400 | 400 | 0.0042 | 0.98 | 0.310 | 0.56 | 0.73 |
 
 ### Cross-Re SI guidance
 
 Based on ~18 samples per vortex shedding cycle:
 | Case | SI | Rationale |
 |------|----|-----------|
-| re50 | 1600 | Lower Re, longer period |
+| re60 | 800 | FFT shows very weak/absent shedding at this Re with free-slip |
 | re100 | 800 | Verified (~19 samples/cycle) |
 | re200 | 500 | ~18 samples/cycle |
 | re400 | 400 | ~18 samples/cycle |
 
----
+### re60 note
 
-## 4. Calibration Results (re100)
-
-```
-FORCE_SCALE = 0.002429
-SENS_SCALE  = 0.7543
-dtw_norm_scale = 0.2043
-Stage0 sim_mean = 0.3166  (-> SIM_VAL=0.2)
-Stage1 sim_mean = 0.8158  (-> SIM_VAL=0.5)
-SIM_BP = [0.0, 0.3166, 0.8158, 0.8895, 0.9448, 1.0]
-SIM_VAL = [0.0, 0.2, 0.5, 0.8, 0.9, 1.0]
-```
-
-Verified: env with calibration gives zero-action reward = 0.076 (Stage0 level, correct).
+At Re=60 with uniform inlet + free-slip walls, the upstream disturbance cylinder
+produces very weak periodic shedding (FFT dominant period ~5625 samples).
+This is a different regime from legacy parabolic+no-slip where re50 did shed.
+SI=800 is adequate; the DRL essentially learns a steady-state control policy.
 
 ---
 
-## 5. Cross-Re Configs
-
-All share the same 2000x600 grid, uniform inlet, free-slip walls. Only viscosity differs:
-- `configs/config_lbm_karman_2000x600.json`      (v=0.004, Re=100)
-- `configs/config_lbm_karman_2000x600_re50.json`  (v=0.008, Re=50)
-- `configs/config_lbm_karman_2000x600_re200.json` (v=0.002, Re=200)
-- `configs/config_lbm_karman_2000x600_re400.json` (v=0.001, Re=400)
-
----
-
-## 6. Original "How to Start Training" (V4 — kept for reference)
-
-### Prerequisites
-- conda env `pycuda_3_10` with PyCUDA, PyTorch, SB3, tensorboard
-- CelerisLab installed at `/home/frank14f/CelerisLab`
-- `target.npy` in the train directory (pre-recorded, reusable)
-
-### Starting a training run (CRITICAL: start sequentially, not simultaneously!)
-
-Two trainings on two GPUs MUST be started one after another with a ~7 min gap,
-because they share the same CelerisLab kernel cache. Simultaneous startup causes
-kernel compilation race conditions → corrupted kernel → reward=0.000 forever.
-
-```bash
-# 1. Clean stale cache
-rm -f /home/frank14f/CelerisLab/src/CelerisLab/lbm/kernels/config/config_objects.h
-rm -f /home/frank14f/CelerisLab/src/CelerisLab/lbm/kernels/kernel.ptx
-
-# 2. Start Bias on GPU0 (with nohup so it survives terminal close)
-cd /home/frank14f/DynamisLab/src/drl_pinball/train
-nohup conda run --no-capture-output -n pycuda_3_10 python -u train_karman_2000x600.py \
-    --device-id 0 --seed 42 --total-episodes 500 --target target.npy \
-    > nohup_bias.log 2>&1 &
-
-# 3. WAIT ~7 minutes for Bias Ep1 to appear (check train.log)
-#    Confirm reward is non-zero before proceeding!
-
-# 4. Start NoBias on GPU1
-nohup conda run --no-capture-output -n pycuda_3_10 python -u train_karman_2000x600.py \
-    --device-id 1 --seed 42 --total-episodes 500 --target target.npy --no-bias \
-    > nohup_nobias.log 2>&1 &
-```
-
-### Monitoring
-```bash
-# Check progress
-tail -5 output/bias_seed42_s2048_e10_v4/train.log
-tail -5 output/nobias_seed42_s2048_e10_v4/train.log
-
-# TensorBoard
-tensorboard --logdir output/bias_seed42_s2048_e10_v4/tb --port 6006
-
-# Plot training curves
-conda run -n pycuda_3_10 python -u analyze_final.py
-```
-
-### Stopping
-```bash
-ps aux | grep train_karman | grep -v grep | awk '{print $2}' | xargs kill
-```
-
----
-
-## 4. Reward Design (V3 — Current)
+## 4. Reward Design
 
 ### Formula
 
 ```python
-FORCE_SCALE = 0.0025       # fixed physical constant (combined max|force| from Phase 0)
+# Gaussian reward (no zero-crossing spikes)
+r_cd_raw = exp(-cd_norm² * K_CD)      # cd_norm = (Σfx)/3 / FORCE_SCALE
+r_cl_raw = exp(-cl_norm² * K_CL)
 
-# Gaussian reward (no zero-crossing spikes that exp(-|x|) causes)
-r_cd_raw = exp(-cd_norm² * 50)     # cd_norm = (Σfx)/3 / FORCE_SCALE
-r_cl_raw = exp(-cl_norm² * 100)    # cl_norm = (Σfy)/3 / FORCE_SCALE
-
-# EMA smoothing for cd/cl only (r_sim is already smooth from DTW)
+# EMA smoothing for cd/cl (r_sim uses DTW, already smooth)
 r_cd = EMA(r_cd_raw, weight=0.2)
 r_cl = EMA(r_cl_raw, weight=0.2)
 
-# Normalized DTW similarity (raw, no EMA)
-# norm_scale = mean of target's 3 uy-channel stds (fixed once target is recorded)
-r_sim = piecewise_map(sim, breakpoints, values)
+# Normalized DTW similarity (piecewise-mapped to [0,1])
+r_sim = piecewise_map(sim, SIM_BP, SIM_VAL)
 
-# Floor penalty: prevents DRL from sacrificing one component
-if r_cd < 0.10: penalty += 0.05 * (0.10 - r_cd) / 0.10
-if r_cl < 0.10: penalty += 0.05 * (0.10 - r_cl) / 0.10
-if r_sim < 0.10: penalty += 0.05 * (0.10 - r_sim) / 0.10
+# Floor penalty: prevents sacrificing one component
+penalty = 0.05 * sum(max(0, FLOOR - r) / FLOOR for r, FLOOR in zip(...))
 
-reward = max(0, 0.30*r_cd + 0.30*r_cl + 0.40*r_sim - penalty)
+reward = max(0, W_CD*r_cd + W_CL*r_cl + W_SIM*r_sim - penalty)
+# W_CD=0.30, W_CL=0.30, W_SIM=0.40
 ```
 
+### Three-stage targets
+
+| Stage | r_cd | r_cl | r_sim | Description |
+|-------|------|------|-------|-------------|
+| Stage0 (zero rotation) | ~0 | ~0 | ~0.2 | No control baseline |
+| Stage1 (reference open-loop) | ~0.7 | ~0.5 | ~0.5 | Legacy-equiv bias |
+| Optimal (trained) | ~0.9 | ~0.9 | ~0.9 | Full cloaking |
+
 ### Why Gaussian not exp(-|x|)
 
-`exp(-|x|)` has maximum gradient at x=0. When cd/cl oscillates around zero,
-the reward swings wildly (spikes at every zero-crossing). `exp(-x²)` has zero
-gradient at x=0, giving smooth reward near the optimum.
+`exp(-|x|)` has maximum gradient at x=0, causing reward spikes at zero-crossings
+of oscillating cd/cl. `exp(-x²)` has zero gradient at x=0 → smooth near optimum.
 
-### Why Normalized DTW
+### Why normalized DTW
 
-Raw DTW `1 - cost/n` has very narrow dynamic range (0.70-0.97) because cost
-depends on absolute signal amplitude. Normalizing by target's uy-channel std
-(≈0.20) extends the range to 0.0-0.9, giving DRL a meaningful gradient.
+Raw DTW has narrow dynamic range (0.70-0.97). Normalizing by target's uy-channel
+std extends range to 0.0-0.9, giving DRL meaningful gradient.
 
-The norm_scale is computed once from the target signal and is fixed for all
-training and inference. It's universal — same formula works for cloak and
-illusion (each scenario has its own target → its own norm_scale).
+### Why no EMA on r_sim
 
-### Why r_sim has no EMA
-
-DTW is already a smooth signal (30-step windowed comparison). Adding EMA
-over-smoothed it and introduced artificial delay. The 40-step physical delay
-(convection 25 steps + DTW window 15 steps) is handled by GAE with gamma=0.995.
-
-### Three-stage targets (from Phase 0 measurement)
-
-| Stage | r_cd | r_cl | r_sim | total | Target |
-|-------|------|------|-------|-------|--------|
-| Stage0 (no rotation) | 0.03 | 0.22 | 0.17 | 0.14 | ~0.2 |
-| Stage1 (bias action) | 0.72 | 0.54 | 0.49 | 0.58 | ~0.5 |
-| Optimal (trained) | ~0.9 | ~0.9 | ~0.9 | ~0.9 | ~0.9 |
+DTW is already a smooth 30-step windowed signal. Adding EMA over-smooths.
 
 ---
 
-## 5. PPO Configuration (V4 — Current)
+## 5. PPO Configuration
 
 ```python
 PPO(
@@ -270,203 +215,106 @@ PPO(
     policy_kwargs={"activation_fn": Sin, "net_arch": [64, 64]},
     env=vec_env,
     device=torch.device("cuda:X"),
-    n_steps=2048,        # MUST be 2048 (legacy default). 512 causes noisy curves.
-    batch_size=64,       # SB3 default
-    n_epochs=10,         # SB3 default. 3 was too few → slow learning.
-    learning_rate=3e-4,  # SB3 default
-    gamma=0.995,         # Higher than SB3 default (0.99) for r_sim delay propagation
-    # ent_coef=0.0       # SB3 default (not set). n_steps=2048 provides enough diversity.
-    # target_kl=None     # SB3 default (not set). Well-estimated gradients don't need KL stop.
-    verbose=0,
+    n_steps=2048,        # MUST be 2048. 512 → noisy curves.
+    batch_size=64,
+    n_epochs=10,
+    learning_rate=3e-4,
+    gamma=0.995,         # Higher than default for DTW delay propagation
 )
 ```
 
-### Key lesson: Don't deviate from SB3 defaults without strong reason
-
-V1-V3 used n_steps=512, n_epochs=3, ent_coef=0.01, target_kl=0.03. This caused:
-- Noisy training curves (high gradient variance from small n_steps)
-- Slow learning (3 epochs too few)
-- Degradation after peak (policy oscillation from noisy gradients)
-
-V4 matches SB3 defaults (n_steps=2048, n_epochs=10, no ent_coef, no target_kl)
-and produces smooth, steadily rising curves — matching the legacy training quality.
-
-### Evaluation: stochastic (NOT deterministic)
-
-```python
-action, _ = model.predict(eval_obs)  # NO deterministic=True
-```
-
-Legacy training used stochastic eval. Deterministic eval gives sharp, noisy
-rewards. Stochastic eval averages over the policy distribution → smoother curves.
-For paper presentation, stochastic is more honest (shows expected performance).
-
-### Symmetry: per-rollout, not per-step
-
-The G-mirror wrapper decides ONCE per rollout (every 2048 steps) whether to
-mirror. If mirrored, ALL steps in that rollout use G-transform consistently.
-Per-step random mirroring adds noise; per-rollout is clean.
-
-During evaluation, symmetry is disabled (prob=0.0) for clean policy assessment.
+- **Evaluation**: stochastic (no deterministic=True) — smoother curves
+- **Symmetry**: per-rollout G-mirror (50% probability). Disabled during eval.
+- **Every episode saves**: `ep0001_model.zip` + `ep0001_vecnormalize.pkl` saved each episode
 
 ---
 
-## 6. Obs Normalization
+## 6. Obs Normalization (Two-layer)
 
-### Two-layer approach
-
-1. **Env physical norm** (fixed, reproducible):
-   - forces / FORCE_SCALE (0.0025)
-   - sensors / SENS_SCALE (0.8, legacy-equiv scale)
-   - No clipping (VecNormalize handles that)
+1. **Env physical norm** (fixed, from calibration):
+   - forces / FORCE_SCALE, sensors / SENS_SCALE
+   - No clipping (VecNormalize handles)
 
 2. **SB3 VecNormalize** (running mean/std):
-   - norm_obs=True, norm_reward=False
-   - clip_obs=10.0 (generous, physical norm already keeps obs ~[-1,1])
+   - norm_obs=True, norm_reward=False, clip_obs=10.0
    - Saved to `vec_normalize.pkl` for inference
 
-### Why not just VecNormalize
-
-Physical norm ensures the obs is in a reasonable range even before VecNormalize
-has collected enough statistics. This prevents extreme values in the first few
-hundred steps. The physical norm constants (FORCE_SCALE, SENS_SCALE) are fixed
-and do not change between training and inference.
-
-### Why not just physical norm (like legacy)
-
-VecNormalize adapts to the actual obs distribution, giving each dimension
-equal footing. Without it, sensor ux (range 0.6-0.9) dominates sensor uy
-(range 0.2) after physical norm. VecNormalize corrects this.
-
 ---
 
-## 7. Action Configuration
+## 7. Action Configuration (V5: no_bias only)
 
-### Bias mode (default)
-```python
-ACTION_SCALE = 8.0
-ACTION_BIAS = [0, -4, 4]  # front, top(+y), bot(-y)
-# omega = -(action * 8 + [0,-4,4]) * U0 / RADIUS
-# Physical range: front [-8,8], top [-4,12], bot [-12,4] × U0
-```
-
-### NoBias mode (--no-bias flag)
 ```python
 ACTION_SCALE = 12.0
 ACTION_BIAS = [0, 0, 0]
-# omega = -(action * 12 + [0,0,0]) * U0 / RADIUS
-# Physical range: all cylinders [-12,12] × U0
+# omega = -(action * 12) * U0 / RADIUS
+# Physical range: all cylinders [-12, 12] × U0
 ```
 
-NoBias uses scale=12 (not 8) to cover the same omega range as Bias.
-With scale=8 and no bias, range is only [-8,8] — missing the ±12×U0 extremes
-that Bias reaches. This caused NoBias to fail at learning lift control (r_cl
-collapsed to 0.05) because it couldn't reach the necessary rotation speeds.
-
-### Sign convention (CelerisLab new kernel)
-
-The new CelerisLab kernel uses `Uw = -omega * ry` (negative sign).
-Legacy used `Uw = action * (y_c - y) / radius` (no negative).
-Conversion: `omega = -surface_vel / radius`
+Sign convention: `Uw = -omega * ry` (omega>0 = clockwise).
 
 ---
 
 ## 8. Environment Design
 
-### Two-phase initialization (avoids runtime sync_bodies)
+### Two-phase initialization
 
-1. `record_target()`: temporary Simulation (dist_cyl + sensors only) →
-   record 150 steps of target signal → close
-2. `KarmanCloakEnv.__init__()`: training Simulation with ALL 7 objects
-   upfront → warmup → zero-action FIFO → bias FIFO → snapshot
+1. `record_target()`: dist_cyl + sensors only → record 150-step target → close
+2. Training Simulation: all 7 objects → warmup → zero-action FIFO → snapshot
 
-All objects (1 dist_cyl + 3 sensors + 3 pinball) are added before `initialize()`.
-No runtime `sync_bodies()` — it causes recompilation and cache conflicts.
-
-### Body IDs (add order)
+Body IDs (add order):
 ```
-0: dist_cyl (force only, skipped in obs)
-1: sensor 0 (top, +y)
-2: sensor 1 (center)
-3: sensor 2 (bottom, -y)
-4: pinball_front
-5: pinball_top (rear, +y)
-6: pinball_bottom (rear, -y)
+0: dist_cyl (force, skipped in obs)
+1-3: sensors (top, center, bottom)
+4-6: pinball (front, top_rear, bottom_rear)
 ```
 
-### Obs layout (12-dim, env output before VecNormalize)
-```
-[0:6]  = forces / FORCE_SCALE: [front_fx, front_fy, top_fx, top_fy, bot_fx, bot_fy]
-[6:12] = sensors / SENS_SCALE: [s0_ux, s0_uy, s1_ux, s1_uy, s2_ux, s2_uy]
-```
+Obs layout (12-dim): forces[6] + sensors[6]
 
 ### No-reset training
 
-`step()` always returns `terminated=False`. The flow field is never reset
-during training (matches legacy behavior). Episode length is controlled by
-the external training loop (`model.learn(2048)` per call).
-
-Evaluation does `eval_env.reset()` (restores snapshot) for reproducible
-assessment. After eval, training continues from the post-eval state.
+`step()` returns `terminated=False`. Eval does `reset()` for reproducible assessment.
 
 ---
 
-## 9. Bugs Found & Fixed (Full History)
+## 9. Cross-Re Transfer Pipeline
 
-### Critical bugs
+### How to add a new Re
 
-| # | Bug | Symptom | Fix |
-|---|-----|---------|-----|
-| 1 | **Simultaneous startup** | Two trainings on 2 GPUs → kernel compilation race → reward=0.000 forever | Start sequentially: Bias first, wait 7 min, then NoBias |
-| 2 | **Bias FIFO not applying omega** | `_init_cfd` set bias_omega but never called `set_omega()` during bias FIFO | Added `self._set_omega(bias_omega)` in bias FIFO loop |
-| 3 | **CPU training** | `device="cpu"` was used to avoid PyCUDA/PyTorch conflict | Use GPU with `context.push()/pop()` around CFD calls |
-| 4 | **Symmetry during eval** | G-mirror prob=0.5 during evaluation → noisy, inaccurate reward | Set `inner.prob = 0.0` before eval, restore after |
-| 5 | **DTW not normalized** | Raw DTW `1-cost/n` → sim always 0.90+ → no gradient | Normalize by target uy-channel avg std |
-| 6 | **r_sim EMA + delay** | Added unnecessary EMA smoothing and 20-step delay buffer | Removed both — DTW is already smooth, use raw value |
-| 7 | **exp(-\|x\|) reward** | Zero-crossing spikes in r_cd/r_cl → noisy training | Changed to Gaussian `exp(-x²*K)` |
-| 8 | **NoBias scale=8** | Can't reach ±12×U0 → r_cl collapses | Use scale=12 for NoBias |
-| 9 | **n_steps=512** | High gradient variance → noisy curves, degradation | Use n_steps=2048 (SB3 default) |
-| 10 | **n_epochs=3** | Too few → slow learning | Use n_epochs=10 (SB3 default) |
-| 11 | **deterministic eval** | Sharp, noisy reward measurements | Use stochastic eval (no deterministic=True) |
-| 12 | **Stale config_objects.h** | N_OBJS mismatch after previous run | `_clean_cache()` before each Simulation creation |
+```bash
+# 1. Create config (copy existing, change viscosity)
+cp configs/config_lbm_karman_2000x600.json configs/config_lbm_karman_2000x600_reNNN.json
+# Edit "viscosity" to U0 * 2D / Re_NNN
 
-### Sensor scaling (from reproduce phase)
+# 2. Calibrate
+python calibrate.py --case reNNN --device-id 0 --si <SI> --config <config>
 
-New CelerisLab `read_sensor(normalize=True)` returns area-averaged velocity
-(÷cell_count). Legacy returned raw sum (~78x larger). For DTW comparison with
-legacy-recorded targets, multiply by `SENSOR_CC=78`.
+# 3. Test (5 episodes, local)
+python train_karman.py --case-name transfer_reNNN --device-id 0 --seed 41 \
+    --config <config> --calibration calibrations/reNNN/calibration.json \
+    --si <SI> --total-episodes 5 \
+    --transfer-model output/re100_karman_seed<BEST>/models/best_model.zip
 
-### Omega sign inversion (from reproduce phase)
+# 4. Production (add to crossre_transfer.sh or run directly with 200 episodes)
+```
 
-New kernel: `Uw = -omega * ry` (omega>0 = clockwise).
-Legacy: `Uw = action * (y_c - y) / radius` (no sign inversion).
-Conversion: `omega = -surface_vel / radius`
+### Transfer test results (5-episode verification, 2026-07-02)
+
+| Re | Best Reward | r_cd | r_cl | r_sim | Time/ep |
+|----|------------|------|------|-------|---------|
+| 60 | 0.637 | 0.879 | 0.402 | 0.641 | 281s |
+| 200 | 0.428 | 0.677 | 0.259 | 0.387 | 186s |
+| 400 | 0.489 | 0.787 | 0.491 | 0.274 | 153s |
+
+All show rapid learning from Re100 base. r_cl remains the hardest component across all Re.
 
 ---
 
-## 10. Training Results Summary
+## 10. CelerisLab Integration
 
-### V4 (current, 30 ep before OOM stop — full 500ep running)
-
-| Training | Ep1 | Ep10 | Ep20 | Ep30 | Smooth? |
-|----------|-----|------|------|------|---------|
-| Bias | 0.32 | 0.37 | 0.40 | 0.54 | Yes, steady rise |
-| NoBias | 0.11 | 0.23 | 0.43 | 0.41 | Yes, near-monotonic |
-
-### Previous versions (for reference)
-
-| Version | n_steps | Key change | Result |
-|---------|---------|------------|--------|
-| V1 | 512 | Config8 reward, exp(-\|x\|) | Peak 0.79 but noisy, possible degradation |
-| V2 | 512 | Gaussian + EMA r_sim | Stable but slow (0.62 peak) |
-| V3 | 512 | Normalized DTW, no r_sim EMA | Stable, NoBias reached 0.73 |
-| **V4** | **2048** | **SB3 defaults, stochastic eval** | **Smoothest curves, running** |
-
-### NoBias vs Bias
-
-NoBias requires more episodes to peak (~265 vs ~89 in V3) but can reach
-similar or higher peak reward. NoBias r_cl is the main challenge — it needs
-the scale=12 action range and floor penalty to prevent collapse.
+**CelerisLab is a git submodule** at `DynamisLab/CelerisLab/`.
+All Python imports use `from CelerisLab import Simulation`.
+No external paths — everything is self-contained within the repo.
+Do NOT reference `/home/frank14f/CelerisLab` anywhere.
 
 ---
 
@@ -474,62 +322,31 @@ the scale=12 action range and floor penalty to prevent collapse.
 
 | Parameter | Value | Where | Notes |
 |-----------|-------|-------|-------|
-| Grid | 2000×600 | config_lbm_karman_2000x600.json | uniform inlet, free_slip |
+| Grid | 2000×600 | config | uniform inlet, free_slip |
 | U0 | 0.01 | config | lattice inlet velocity |
-| ν | 0.004 | config | Re_D=50 (code Re=100) |
-| SI | 800 | env | LBM steps per action |
+| ν (re100) | 0.004 | config | Re_D=50 (code Re=100) |
+| SI | 400-800 | calibration | varies by Re |
 | FIFO_LEN | 150 | env | history buffer |
 | CONV_LEN | 30 | env | DTW comparison window |
-| FORCE_SCALE | 0.0025 | env | reward normalization |
-| SENS_SCALE | 0.8 | env | obs normalization (legacy-equiv) |
 | SENSOR_CC | 78 | env | sensor area→legacy conversion |
-| K_CD | 50 | env | Gaussian reward coefficient |
-| K_CL | 100 | env | Gaussian reward coefficient |
+| K_CD/K_CL | 50/100 | env | Gaussian reward coefficients |
 | W_CD/W_CL/W_SIM | 0.30/0.30/0.40 | env | reward weights |
-| FLOOR_CD/CL/SIM | 0.10 | env | floor penalty threshold |
 | n_steps | 2048 | train | PPO rollout size |
 | n_epochs | 10 | train | PPO epochs per update |
 | gamma | 0.995 | train | discount factor |
-| ACTION_SCALE | 8 (bias) / 12 (nobias) | env | action scaling |
-| ACTION_BIAS | [0,-4,4] / [0,0,0] | env | action offset |
+| ACTION_SCALE | 12.0 | env | no_bias only |
 
 ---
 
-## 12. CFD Config
+## 12. Bugs Found & Fixed
 
-File: `configs/config_lbm_karman_2000x600.json`
-
-```
-Grid: 2000×600, D2Q9, MRT, double_buffer, FP32
-Inlet: uniform, regularized scheme
-Walls: free_slip (matches experimental water tunnel)
-Outlet: neq_extrap with backflow_clamp
-```
-
-### EsoPull note
-
-EsoPull streaming was tested — it computes correctly but gives ~5% slowdown
-(not the expected 50% speedup) on this grid size. Use double_buffer for speed.
-EsoPull + zou_he_local inlet gives very different physics (different force
-balances) — only use if you re-record the target with the same inlet scheme.
-
----
-
-## 13. Future Work
-
-1. **Re50/re200/re400 transfer learning**: Calibrate each Re, then transfer from re100 best model using `--transfer` flag. Use adjusted SI per Re.
-2. **Vortex cloak**: `env_vortex.py` ready. Transfer from re100 model. Lamb and Taylor variants.
-3. **Illusion**: `env_illusion.py` needed. S_DIM=14, harmonics target reconstruction. Adjusted pinball/sensor positions.
-4. **Steady cloak**: Simpler case (no upstream disturbance cylinder). Target is uniform flow.
-5. **Symmetry ablation study**: Compare with/without G-mirror to quantify benefit.
-6. **Longer training**: 500 episodes may not be enough. Try 1000 episodes.
-7. **Learning rate schedule**: Try lr decay after peak to prevent degradation.
-
-## 14. V5 Design Principles
-
-1. **Calibration-first**: Every case runs calibrate.py before training. Produces a single immutable JSON.
-2. **No-bias only**: action_scale=12, action_bias=[0,0,0]. Simplifies G-mirror, removes human priors.
-3. **Parameterized envs**: env_karman.py accepts calibration dict + config_path + si. No module-level hardcode.
-4. **Sequential GPU startup**: launch_multi.sh enforces 7-min delay between launches.
-5. **Immutable calibration**: Once produced, calibration is read-only for training and inference. Saved alongside model.
-6. **r_sim maps to [0,1]**: SIM_VAL[-1]=1.0. Full-range normalized DTW similarity.
+| # | Bug | Symptom | Fix |
+|---|-----|---------|-----|
+| 1 | Simultaneous GPU startup | Kernel compilation race → reward=0.000 | Sequential launch, 7-min delay |
+| 2 | Bias FIFO not applying omega | Wrong normalization baseline | Added set_omega in bias FIFO |
+| 3 | DTW not normalized | sim always 0.90+ → flat gradient | Normalize by target uy-channel avg std |
+| 4 | Gaussian vs exp(-|x|) | Zero-crossing reward spikes | Use exp(-x² · K) |
+| 5 | n_steps=512 | Noisy curves, degradation | Use 2048 (SB3 default) |
+| 6 | deterministic eval | Sharp, noisy reward | Stochastic eval |
+| 7 | External CelerisLab paths | Permission issues, server mismatch | Use DynamisLab submodule only |
+| 8 | Manual kernel cache cleaning | Unnecessary, root-only files | CelerisLab handles internally |
diff --git a/src/drl_pinball/train/calibrate.py b/src/drl_pinball/train/calibrate.py
index dd67376..fdf8f5c 100644
--- a/src/drl_pinball/train/calibrate.py
+++ b/src/drl_pinball/train/calibrate.py
@@ -35,15 +35,6 @@ if str(_REPO) not in sys.path:
 
 from CelerisLab import Simulation
 
-_CELERIS = Path("/home/frank14f/CelerisLab")
-_CONFIG_H = _CELERIS / "src/CelerisLab/lbm/kernels/config/config_objects.h"
-_PTX = _CELERIS / "src/CelerisLab/lbm/kernels/kernel.ptx"
-
-def _clean_cache():
-    for p in [_CONFIG_H, _PTX]:
-        if p.exists():
-            p.unlink()
-
 # ---------------------------------------------------------------------------
 # Physics / geometry constants
 # ---------------------------------------------------------------------------
@@ -72,6 +63,7 @@ _REF_OMEGA = np.array([0.0, 0.004, -0.004], dtype=np.float32)
 
 # Reward constants
 K_CD = 50.0; K_CL = 100.0
+K_CD_ILLUSION = 12.0; K_CL_ILLUSION = 25.0   # lower: 3-pinball forces vs 1-target cylinder, wider error range
 W_CD = 0.30; W_CL = 0.30; W_SIM = 0.40
 FLOOR_CD = 0.10; FLOOR_CL = 0.10; FLOOR_SIM = 0.10
 FLOOR_PENALTY = 0.05
@@ -199,7 +191,7 @@ _ILL_PINBALL_REAR_X = 406.0
 _ILL_SENSOR_X = 600.0
 _ILL_TARGET_X = 400.0
 _ILL_TARGET_RADIUS = 1.0 * L0
-_ILL_REF_OMEGA = np.array([0.0, 0.002, -0.002], dtype=np.float32)  # legacy-equiv bias
+_ILL_REF_OMEGA = np.array([0.0, 0.001, -0.001], dtype=np.float32)  # surface_vel = [0,-1,1]*U0, FIFO-init level
 
 def _analyze_harmonics_for_calib(states, n_harmonics=5):
     N, D = states.shape
@@ -224,7 +216,6 @@ def _analyze_harmonics_for_calib(states, n_harmonics=5):
 def _calibrate_illusion(case, config_path, device_id, si, out_dir, log, warmup):
     # ---- Step 1: Record target (target cylinder + 3 sensors) ----
     log("Step 1: Recording illusion target (target cyl + sensors)...")
-    _clean_cache()
     sim = Simulation(lbm_config_path=config_path, device_id=device_id)
     sim._assert_object_count_contract = lambda *a, **kw: None
     sim.add_body("circle", center=(_ILL_TARGET_X, CENTER_Y, 0.0),
@@ -266,7 +257,6 @@ def _calibrate_illusion(case, config_path, device_id, si, out_dir, log, warmup):
 
     # ---- Step 2: Training sim (3 sensors + 3 pinball) ----
     log("Step 2: Creating training sim (6 objects)...")
-    _clean_cache()
     sim = Simulation(lbm_config_path=config_path, device_id=device_id)
     sim._assert_object_count_contract = lambda *a, **kw: None
     sensor_ids = [
@@ -315,9 +305,23 @@ def _calibrate_illusion(case, config_path, device_id, si, out_dir, log, warmup):
     s0_sim = float(np.mean(stage0["sims"]))
     s1_sim = float(np.mean(stage1["sims"]))
 
-    sim_bp = [0.0, s0_sim, s1_sim,
-              s1_sim + (1.0 - s1_sim) * 0.4,
-              s1_sim + (1.0 - s1_sim) * 0.7,
+    # Build SIM_BP: [0, worst_measured, better_measured, ..., 1.0]
+    # Normally (Karman) Stage0 < Stage1 (zero is worse, reference is better).
+    # But for Illusion, Stage0 may already match the target well, and reference
+    # rotation (steady-cloak-like) makes it worse.  Swap roles in that case.
+    if s1_sim < s0_sim:
+        # Reference action is worse than zero (Illusion case).
+        # Stage1 (reference) -> r_sim ~0.2, Stage0 (zero) -> r_sim ~0.4
+        worst_sim = s1_sim
+        better_sim = s0_sim
+    else:
+        # Normal (Karman) case.
+        worst_sim = s0_sim
+        better_sim = s1_sim
+
+    sim_bp = [0.0, worst_sim, better_sim,
+              better_sim + (1.0 - better_sim) * 0.4,
+              better_sim + (1.0 - better_sim) * 0.7,
               1.0]
     sim_val = [0.0, 0.2, 0.5, 0.8, 0.9, 1.0]
 
@@ -411,7 +415,6 @@ def main() -> int:
 
     # ---- Step 1: Record target (Karman: dist_cyl + sensors only) ----
     log("Step 1: Recording target signal...")
-    _clean_cache()
     sim = Simulation(lbm_config_path=config_path, device_id=device_id)
     sim._assert_object_count_contract = lambda *a, **kw: None
     dist_id_t = sim.add_body("circle", center=(DIST_X, CENTER_Y, 0.0), radius=1.0 * L0)
@@ -444,7 +447,6 @@ def main() -> int:
 
     # ---- Step 2: Create training sim ----
     log("Step 2: Creating training sim (all 7 objects)...")
-    _clean_cache()
     sim = Simulation(lbm_config_path=config_path, device_id=device_id)
     sim._assert_object_count_contract = lambda *a, **kw: None
     dist_id = sim.add_body("circle", center=(DIST_X, CENTER_Y, 0.0), radius=1.0 * L0)
@@ -545,8 +547,8 @@ def main() -> int:
         "dtw_norm_scale": float(dtw_norm_scale),
         "SIM_BP": [float(x) for x in sim_bp],
         "SIM_VAL": [float(x) for x in sim_val],
-        "K_CD": K_CD,
-        "K_CL": K_CL,
+        "K_CD": K_CD_ILLUSION,
+        "K_CL": K_CL_ILLUSION,
         "W_CD": W_CD,
         "W_CL": W_CL,
         "W_SIM": W_SIM,
diff --git a/src/drl_pinball/train/calibrations/illusion_1L/calibration.json b/src/drl_pinball/train/calibrations/illusion_1L/calibration.json
index 3dbc1b6..e5f4853 100644
--- a/src/drl_pinball/train/calibrations/illusion_1L/calibration.json
+++ b/src/drl_pinball/train/calibrations/illusion_1L/calibration.json
@@ -10,15 +10,15 @@
   "FIFO_LEN": 150,
   "CONV_LEN": 30,
   "SENSOR_CC": 78.0,
-  "FORCE_SCALE": 0.0029,
+  "FORCE_SCALE": 0.002,
   "SENS_SCALE": 0.93,
   "dtw_norm_scale": 0.251,
   "SIM_BP": [
     0.0,
+    0.73,
     0.81,
-    0.82,
-    0.83,
-    0.84,
+    0.89,
+    0.94,
     1.0
   ],
   "SIM_VAL": [
@@ -29,8 +29,8 @@
     0.9,
     1.0
   ],
-  "K_CD": 50.0,
-  "K_CL": 100.0,
+  "K_CD": 12.0,
+  "K_CL": 25.0,
   "W_CD": 0.3,
   "W_CL": 0.3,
   "W_SIM": 0.4,
diff --git a/src/drl_pinball/train/calibrations/illusion_1L/target_harmonics.json b/src/drl_pinball/train/calibrations/illusion_1L/target_harmonics.json
index e94d202..0a97e29 100644
--- a/src/drl_pinball/train/calibrations/illusion_1L/target_harmonics.json
+++ b/src/drl_pinball/train/calibrations/illusion_1L/target_harmonics.json
@@ -1,12 +1,12 @@
 [
   {
-    "dc": 0.6848811427752177,
+    "dc": 0.6848811439673106,
     "amps": [
-      0.20348444790268552,
-      0.02915900547523486,
-      0.02784031012371259,
-      0.026538733180849892,
-      0.01919840514104942
+      0.2034844510278786,
+      0.029159004511650252,
+      0.027840311609468333,
+      0.02653873514475916,
+      0.01919840709106637
     ],
     "freqs": [
       0.02666666666666667,
@@ -16,21 +16,21 @@
       0.04666666666666667
     ],
     "phases": [
-      -1.2362005505810982,
-      -1.1766679461536396,
-      1.8863477863626301,
-      -2.834029481822404,
-      -0.7286212732915559
+      -1.2362005574545905,
+      -1.1766679293247424,
+      1.886347686621138,
+      -2.834029604726311,
+      -0.7286213249887804
     ]
   },
   {
-    "dc": -0.026989686206604045,
+    "dc": -0.026989686344750227,
     "amps": [
-      0.2790809917571785,
-      0.07572308632544687,
-      0.04473095779630388,
-      0.039113569175253486,
-      0.038411479291876154
+      0.2790809917939555,
+      0.07572308617806586,
+      0.044730957946728864,
+      0.03911356916608822,
+      0.03841147969973682
     ],
     "freqs": [
       0.02666666666666667,
@@ -40,21 +40,21 @@
       0.03333333333333333
     ],
     "phases": [
-      3.0547687538583306,
-      0.6711306028525605,
-      -0.10499736264498984,
-      -2.7955698201715835,
-      3.1220861518480647
+      3.0547687544981676,
+      0.671130604261652,
+      -0.10499736216933567,
+      -2.7955698151736605,
+      3.1220861483228126
     ]
   },
   {
-    "dc": 0.5674182403087616,
+    "dc": 0.5674182399113973,
     "amps": [
-      0.06514963980633084,
-      0.02472242867395058,
-      0.014768360330077245,
-      0.010156019183366295,
-      0.00963919337572356
+      0.06514964076759504,
+      0.024722427490218583,
+      0.01476835974183025,
+      0.010156020328112192,
+      0.009639191161046853
     ],
     "freqs": [
       0.05333333333333334,
@@ -64,21 +64,21 @@
       0.1
     ],
     "phases": [
-      -0.7488772609862894,
-      2.444199252505853,
-      -0.7923018439616549,
-      2.505537406241232,
-      -1.0758943656055078
+      -0.7488773712604715,
+      2.444199243833346,
+      -0.7923014988253196,
+      2.5055369932207086,
+      -1.0758944221483198
     ]
   },
   {
-    "dc": 0.014232361110819814,
+    "dc": 0.014232359210921763,
     "amps": [
-      0.4461623192829029,
-      0.0779793315804837,
-      0.0550568453231777,
-      0.05331603051277121,
-      0.04874852247514895
+      0.446162318961067,
+      0.07797933262227957,
+      0.05505684614794328,
+      0.05331603052134459,
+      0.04874852338883084
     ],
     "freqs": [
       0.02666666666666667,
@@ -88,21 +88,21 @@
       0.07333333333333333
     ],
     "phases": [
-      -3.1049303564692066,
-      0.027203636269757633,
-      0.09727455160562376,
-      -3.095222555392279,
-      -3.044905056768087
+      -3.104930355366964,
+      0.027203661003797012,
+      0.09727456602426711,
+      -3.0952225671159,
+      -3.0449050417637897
     ]
   },
   {
-    "dc": 0.6885532836119334,
+    "dc": 0.688553271094958,
     "amps": [
-      0.2018316634259717,
-      0.033110505502103905,
-      0.029481533925939486,
-      0.027209600513899444,
-      0.0210247548932384
+      0.20183164156868158,
+      0.033110505614057664,
+      0.029481548571705242,
+      0.02720959921763942,
+      0.02102475262837555
     ],
     "freqs": [
       0.02666666666666667,
@@ -112,21 +112,21 @@
       0.07333333333333333
     ],
     "phases": [
-      1.8817739461803038,
-      2.742416499482124,
-      -1.1159813908192473,
-      1.7448219790472927,
-      1.8563271918371878
+      1.8817739067162356,
+      2.7424167422444903,
+      -1.1159816805254985,
+      1.7448225406868731,
+      1.8563274842269628
     ]
   },
   {
-    "dc": 0.0464287880451108,
+    "dc": 0.046428785625806386,
     "amps": [
-      0.266793500575173,
-      0.08445240294008177,
-      0.05549481483267607,
-      0.03176632041902732,
-      0.025647047642157136
+      0.2667935014251649,
+      0.08445240278954501,
+      0.05549481355853139,
+      0.031766322660930275,
+      0.025647051108265238
     ],
     "freqs": [
       0.02666666666666667,
@@ -136,21 +136,21 @@
       0.04666666666666667
     ],
     "phases": [
-      3.0338443035387885,
-      -2.634676303912741,
-      -0.03215847385538798,
-      -0.005548069481591528,
-      0.8284453225261742
+      3.0338443211965744,
+      -2.6346763210802586,
+      -0.032158500100019624,
+      -0.005548092544019917,
+      0.8284453426622406
     ]
   },
   {
-    "dc": 0.002807500216489037,
+    "dc": 0.0028075010624403754,
     "amps": [
-      1.712089159739782e-05,
-      6.84709236632855e-06,
-      3.6652517552615438e-06,
-      2.9729489026324893e-06,
-      2.0179845909227172e-06
+      1.7116459800799798e-05,
+      6.866397692253015e-06,
+      3.6591197181741872e-06,
+      2.979711101541413e-06,
+      2.0364590024389495e-06
     ],
     "freqs": [
       0.05333333333333334,
@@ -160,21 +160,21 @@
       0.06666666666666667
     ],
     "phases": [
-      -2.4049945951829694,
-      0.6548426535356905,
-      -2.327050319985697,
-      0.5677103875435425,
-      -2.2688377992989093
+      -2.4055008082312566,
+      0.6560206596369318,
+      -2.338324550342246,
+      0.5697953506705974,
+      -2.2632313796302874
     ]
   },
   {
-    "dc": -1.4845058922219323e-05,
+    "dc": -1.484534581322805e-05,
     "amps": [
-      0.0006606466483213061,
-      0.00010502206909109318,
-      8.38977596666718e-05,
-      4.8485264824562914e-05,
-      4.61590173098796e-05
+      0.000660645874022141,
+      0.00010502076564124189,
+      8.389696757021492e-05,
+      4.848746760651764e-05,
+      4.616076991013906e-05
     ],
     "freqs": [
       0.02666666666666667,
@@ -184,11 +184,11 @@
       0.04
     ],
     "phases": [
-      -0.8350712750146696,
-      2.4283127217217664,
-      -0.9184054668600495,
-      2.6043773129885874,
-      -0.9754872507212389
+      -0.8350715543078073,
+      2.428305305182952,
+      -0.9184117032214737,
+      2.6043964384951956,
+      -0.9755175136337735
     ]
   }
 ]
\ No newline at end of file
diff --git a/src/drl_pinball/train/calibrations/re200/calibration.json b/src/drl_pinball/train/calibrations/re200/calibration.json
new file mode 100644
index 0000000..ad9aa67
--- /dev/null
+++ b/src/drl_pinball/train/calibrations/re200/calibration.json
@@ -0,0 +1,49 @@
+{
+  "case": "re200",
+  "grid": {
+    "nx": 2000,
+    "ny": 600
+  },
+  "config_path": "/home/frank14f/DynamisLab/configs/config_lbm_karman_2000x600_re200.json",
+  "SI": 500,
+  "FIFO_LEN": 150,
+  "CONV_LEN": 30,
+  "SENSOR_CC": 78.0,
+  "FORCE_SCALE": 0.0026,
+  "SENS_SCALE": 0.9,
+  "dtw_norm_scale": 0.269,
+  "SIM_BP": [
+    0.0,
+    0.45,
+    0.77,
+    0.86,
+    0.93,
+    1.0
+  ],
+  "SIM_VAL": [
+    0.0,
+    0.2,
+    0.5,
+    0.8,
+    0.9,
+    1.0
+  ],
+  "K_CD": 12.0,
+  "K_CL": 25.0,
+  "W_CD": 0.3,
+  "W_CL": 0.3,
+  "W_SIM": 0.4,
+  "FLOOR_CD": 0.1,
+  "FLOOR_CL": 0.1,
+  "FLOOR_SIM": 0.1,
+  "FLOOR_PENALTY": 0.05,
+  "ACTION_SCALE": 12.0,
+  "ACTION_BIAS": [
+    0.0,
+    0.0,
+    0.0
+  ],
+  "U0": 0.01,
+  "RADIUS": 10.0,
+  "L0": 20.0
+}
\ No newline at end of file
diff --git a/src/drl_pinball/train/calibrations/re400/calibration.json b/src/drl_pinball/train/calibrations/re400/calibration.json
new file mode 100644
index 0000000..a537d4b
--- /dev/null
+++ b/src/drl_pinball/train/calibrations/re400/calibration.json
@@ -0,0 +1,49 @@
+{
+  "case": "re400",
+  "grid": {
+    "nx": 2000,
+    "ny": 600
+  },
+  "config_path": "/home/frank14f/DynamisLab/configs/config_lbm_karman_2000x600_re400.json",
+  "SI": 400,
+  "FIFO_LEN": 150,
+  "CONV_LEN": 30,
+  "SENSOR_CC": 78.0,
+  "FORCE_SCALE": 0.0042,
+  "SENS_SCALE": 0.98,
+  "dtw_norm_scale": 0.31,
+  "SIM_BP": [
+    0.0,
+    0.56,
+    0.73,
+    0.84,
+    0.92,
+    1.0
+  ],
+  "SIM_VAL": [
+    0.0,
+    0.2,
+    0.5,
+    0.8,
+    0.9,
+    1.0
+  ],
+  "K_CD": 12.0,
+  "K_CL": 25.0,
+  "W_CD": 0.3,
+  "W_CL": 0.3,
+  "W_SIM": 0.4,
+  "FLOOR_CD": 0.1,
+  "FLOOR_CL": 0.1,
+  "FLOOR_SIM": 0.1,
+  "FLOOR_PENALTY": 0.05,
+  "ACTION_SCALE": 12.0,
+  "ACTION_BIAS": [
+    0.0,
+    0.0,
+    0.0
+  ],
+  "U0": 0.01,
+  "RADIUS": 10.0,
+  "L0": 20.0
+}
\ No newline at end of file
diff --git a/src/drl_pinball/train/calibrations/re60/calibration.json b/src/drl_pinball/train/calibrations/re60/calibration.json
new file mode 100644
index 0000000..2c464f4
--- /dev/null
+++ b/src/drl_pinball/train/calibrations/re60/calibration.json
@@ -0,0 +1,49 @@
+{
+  "case": "re60",
+  "grid": {
+    "nx": 2000,
+    "ny": 600
+  },
+  "config_path": "/home/frank14f/DynamisLab/configs/config_lbm_karman_2000x600_re60.json",
+  "SI": 800,
+  "FIFO_LEN": 150,
+  "CONV_LEN": 30,
+  "SENSOR_CC": 78.0,
+  "FORCE_SCALE": 0.0021,
+  "SENS_SCALE": 0.72,
+  "dtw_norm_scale": 0.107,
+  "SIM_BP": [
+    0.0,
+    0.41,
+    0.61,
+    0.77,
+    0.88,
+    1.0
+  ],
+  "SIM_VAL": [
+    0.0,
+    0.2,
+    0.5,
+    0.8,
+    0.9,
+    1.0
+  ],
+  "K_CD": 12.0,
+  "K_CL": 25.0,
+  "W_CD": 0.3,
+  "W_CL": 0.3,
+  "W_SIM": 0.4,
+  "FLOOR_CD": 0.1,
+  "FLOOR_CL": 0.1,
+  "FLOOR_SIM": 0.1,
+  "FLOOR_PENALTY": 0.05,
+  "ACTION_SCALE": 12.0,
+  "ACTION_BIAS": [
+    0.0,
+    0.0,
+    0.0
+  ],
+  "U0": 0.01,
+  "RADIUS": 10.0,
+  "L0": 20.0
+}
\ No newline at end of file
diff --git a/src/drl_pinball/train/crossre_transfer.sh b/src/drl_pinball/train/crossre_transfer.sh
new file mode 100755
index 0000000..37bef41
--- /dev/null
+++ b/src/drl_pinball/train/crossre_transfer.sh
@@ -0,0 +1,115 @@
+#!/bin/bash
+# Sequential cross-Re transfer learning: Re60 -> Re200 -> Re400
+# Each: calibrate (~5 min) + train N episodes
+#
+# Usage (local test, 5 episodes):
+#   bash crossre_transfer.sh --re-list 60 --test-episodes 5
+#
+# Usage (production on server, 200 episodes each):
+#   bash crossre_transfer.sh --re-list 60,200,400
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+REPO_DIR="$(cd "$SCRIPT_DIR/../../.." && pwd)"
+TRAIN_DIR="$SCRIPT_DIR"
+CONFIG_DIR="$REPO_DIR/configs"
+# TODO: BEFORE PUSHING TO SERVER, update BEST_MODEL to the correct path
+BEST_MODEL="$TRAIN_DIR/output/re100_karman_seed539439/models/best_model.zip"
+GPU=0
+SEED=41
+EPISODES=200
+TEST_EPISODES=0
+RE_LIST=""
+LOG_BASE="/tmp/crossre_transfer"
+CONDA_ENV="pycuda_3_10"
+
+usage() {
+    echo "Usage: $0 [--re-list 60,200,400] [--test-episodes N] [--best-model PATH]"
+    echo "  --re-list        Comma-separated Re numbers (default: 60,200,400)"
+    echo "  --test-episodes  Run only N episodes per Re for quick verification"
+    echo "  --best-model     Path to Re100 best model .zip"
+    exit 1
+}
+
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --re-list)       RE_LIST="$2"; shift 2 ;;
+        --test-episodes) TEST_EPISODES="$2"; shift 2 ;;
+        --best-model)    BEST_MODEL="$2"; shift 2 ;;
+        *)               echo "Unknown option: $1"; usage ;;
+    esac
+done
+
+if [[ -z "$RE_LIST" ]]; then
+    RE_LIST="60,200,400"
+fi
+
+IFS=',' read -ra RE_ARR <<< "$RE_LIST"
+
+echo "=== Cross-Re Transfer ==="
+echo "  Re list:     ${RE_ARR[*]}"
+echo "  Best model:  ${BEST_MODEL}"
+echo "  Episodes:    ${EPISODES} (test-mode: ${TEST_EPISODES})"
+echo "  GPU:         ${GPU}, Seed: ${SEED}"
+echo ""
+
+if [[ ! -f "$BEST_MODEL" ]]; then
+    echo "ERROR: Best model not found at $BEST_MODEL"
+    exit 1
+fi
+
+mkdir -p "$LOG_BASE"
+
+if [[ "$TEST_EPISODES" -gt 0 ]]; then
+    EPISODES="$TEST_EPISODES"
+    echo "  TEST MODE: only $TEST_EPISODES episodes per Re"
+fi
+
+for re in "${RE_ARR[@]}"; do
+    case $re in
+        60)  SI=800;  vis_label="re60" ;;
+        200) SI=500;  vis_label="re200" ;;
+        400) SI=400;  vis_label="re400" ;;
+        *)   echo "ERROR: Unknown Re=$re (supported: 60, 200, 400)"; exit 1 ;;
+    esac
+    CONFIG="$CONFIG_DIR/config_lbm_karman_2000x600_${vis_label}.json"
+    CASE="transfer_${vis_label}"
+    LOG="$LOG_BASE/${vis_label}.log"
+
+    echo "=== $(date): Starting $CASE (SI=$SI) ===" | tee -a "$LOG"
+
+    if [[ ! -f "$CONFIG" ]]; then
+        echo "  ERROR: Config not found: $CONFIG" | tee -a "$LOG"
+        exit 1
+    fi
+
+    # Step 1: Calibrate (skip if calibration.json already exists)
+    CAL_JSON="$TRAIN_DIR/calibrations/$vis_label/calibration.json"
+    if [[ -f "$CAL_JSON" ]]; then
+        echo "  [SKIP] Calibration already exists: $CAL_JSON" | tee -a "$LOG"
+        echo "  (Delete calibrations/$vis_label/ to force re-calibration)"
+    else
+        echo "  [$(date '+%H:%M:%S')] Calibrating..." | tee -a "$LOG"
+        conda run --no-capture-output -n "$CONDA_ENV" python -u \
+            "$TRAIN_DIR/calibrate.py" \
+            --case "$vis_label" --device-id $GPU --si $SI \
+            --config "$CONFIG" >> "$LOG" 2>&1
+        echo "  [$(date '+%H:%M:%S')] Calibration done." | tee -a "$LOG"
+    fi
+
+    # Step 2: Train with transfer
+    echo "  [$(date '+%H:%M:%S')] Training ${EPISODES} episodes..." | tee -a "$LOG"
+    conda run --no-capture-output -n "$CONDA_ENV" python -u \
+        "$TRAIN_DIR/train_karman.py" \
+        --case-name "$CASE" --device-id $GPU --seed $SEED \
+        --config "$CONFIG" \
+        --calibration "$CAL_JSON" \
+        --si $SI --total-episodes $EPISODES \
+        --transfer-model "$BEST_MODEL" >> "$LOG" 2>&1
+    echo "  [$(date '+%H:%M:%S')] Training done." | tee -a "$LOG"
+
+    echo "=== $(date): $CASE complete ===" | tee -a "$LOG"
+    echo ""
+done
+
+echo "=== ALL DONE ===" | tee -a "$LOG_BASE/summary.log"
diff --git a/src/drl_pinball/train/env_illusion.py b/src/drl_pinball/train/env_illusion.py
index 6461945..ce70dec 100644
--- a/src/drl_pinball/train/env_illusion.py
+++ b/src/drl_pinball/train/env_illusion.py
@@ -1,21 +1,33 @@
 #!/usr/bin/env python3
-"""Hydrodynamic Illusion environment (V5 - calibration-driven, no_bias, 2000x600).
+"""Hydrodynamic Illusion environment for 2000x600 config (uniform, free-slip).
 
-Two-phase initialization:
-  Phase 1: target cylinder (diameter=1.0*L0) + 3 sensors -> warmup ->
-           record 150-step signal -> FFT harmonics -> close
-  Phase 2: Training sim (3 sensors + 3 pinball) -> warmup ->
+Design: Two-phase initialization to AVOID runtime sync_bodies().
+  Phase 1: Temporary Simulation(target cyl + sensors) -> record 150-step signal
+           + FFT harmonics -> close
+  Phase 2: Training Simulation(3 sensors + 3 pinball upfront) -> warmup ->
            zero-action FIFO -> snapshot
 
-Observation (14-dim, physical norm, NO clip):
+CUDA context: mirrors legacy pattern - push CFD context before GPU ops, pop after.
+
+Geometry: pinball@19/20.3L0, sensor@30L0, target cylinder@20L0 (1L diameter).
+  Equivalent pinball-to-sensor distance matched to illusion target.
+
+Observation (14-dim, physical norm, NO clip - VecNormalize handles that):
   [0:6]   = raw_forces / FORCE_SCALE   (front_fx,fy, top_fx,fy, bot_fx,fy)
   [6:12]  = raw_sensors / SENS_SCALE   (s0_ux,uy, s1_ux,uy, s2_ux,uy)
-  [12:14] = target_cd, target_cl       (from harmonics reconstruction)
+  [12:14] = target_cd, target_cl       (from FFT harmonics reconstruction)
 
 Action (3-dim): no_bias only
-  [-1,1] -> omega = -(action * 12 + [0,0,0]) * U0 / R
+  [-1,1] -> omega = -(action*12 + [0,0,0]) * U0 / RADIUS
 
-Reward: Gaussian cd/cl (compared to harmonics-reconstructed target) + normalized DTW.
+Reward (V3: Gaussian + EMA smoothing + normalized DTW):
+  r_cd  = EMA(exp(-(cd - target_cd)^2 * K_CD), EMA_FAST)
+  r_cl  = EMA(exp(-(cl - target_cl)^2 * K_CL), EMA_FAST)
+  r_sim = piecewise_map(sim, SIM_BP, SIM_VAL)
+  reward = W_CD*r_cd + W_CL*r_cl + W_SIM*r_sim - floor_penalty
+
+K_CD/K_CL are LOWER than Karman (12/25 vs 50/100) because 3-pinball total force
+is compared to 1-target-cylinder force, giving wider normalized error range.
 """
 from __future__ import annotations
 
@@ -34,14 +46,6 @@ if str(_REPO) not in sys.path:
 
 from CelerisLab import Simulation
 
-_CELERIS = Path("/home/frank14f/CelerisLab")
-_CONFIG_H = _CELERIS / "src/CelerisLab/lbm/kernels/config/config_objects.h"
-_PTX = _CELERIS / "src/CelerisLab/lbm/kernels/kernel.ptx"
-
-def _clean_cache():
-    for p in [_CONFIG_H, _PTX]:
-        if p.exists(): p.unlink()
-
 # ---------------------------------------------------------------------------
 L0 = 20.0; U0 = 0.01; RADIUS = L0 / 2.0
 NX = 2000; NY = 600
@@ -151,7 +155,6 @@ def gen_target_states_at(t, harmonics):
 
 # ---------------------------------------------------------------------------
 def record_illusion_target(config_path, device_id, si, target_diam=1.0):
-    _clean_cache()
     warmup = int(4.0 * NX / U0)
     sim = Simulation(lbm_config_path=config_path, device_id=device_id)
     sim._assert_object_count_contract = lambda *a, **kw: None
@@ -255,7 +258,6 @@ class IllusionCloakEnv(gym.Env):
 
         # ---- Phase 2: Training sim ----
         print("  [illusion] Phase 2: Training sim...", flush=True)
-        _clean_cache()
         self.sim = Simulation(lbm_config_path=self._config_path, device_id=self.device_id)
         self.sim._assert_object_count_contract = lambda *a, **kw: None
         self.sensor_ids = [
diff --git a/src/drl_pinball/train/env_karman.py b/src/drl_pinball/train/env_karman.py
index 3bf47f2..0872989 100644
--- a/src/drl_pinball/train/env_karman.py
+++ b/src/drl_pinball/train/env_karman.py
@@ -41,14 +41,6 @@ if str(_REPO) not in sys.path:
 
 from CelerisLab import Simulation
 
-_CELERIS = Path("/home/frank14f/CelerisLab")
-_CONFIG_H = _CELERIS / "src/CelerisLab/lbm/kernels/config/config_objects.h"
-_PTX = _CELERIS / "src/CelerisLab/lbm/kernels/kernel.ptx"
-
-def _clean_cache():
-    for p in [_CONFIG_H, _PTX]:
-        if p.exists(): p.unlink()
-
 # ---------------------------------------------------------------------------
 # Geometry constants (fixed across all Karman cloak cases)
 # ---------------------------------------------------------------------------
@@ -124,7 +116,6 @@ class ActionSmoother:
 # ---------------------------------------------------------------------------
 def record_target(config_path: str, device_id: int, si: int) -> np.ndarray:
     """Record target signal (dist_cyl + sensors, no pinball)."""
-    _clean_cache()
     warmup = int(4.0 * NX / U0)
     sim = Simulation(lbm_config_path=config_path, device_id=device_id)
     sim._assert_object_count_contract = lambda *a, **kw: None
@@ -242,7 +233,6 @@ class KarmanCloakEnv(gym.Env):
         if self.target_states is None:
             self.target_states = record_target(self._config_path, self.device_id, self._si)
 
-        _clean_cache()
         self.sim = Simulation(lbm_config_path=self._config_path, device_id=self.device_id)
         self.sim._assert_object_count_contract = lambda *a, **kw: None
 
diff --git a/src/drl_pinball/train/env_vortex.py b/src/drl_pinball/train/env_vortex.py
index 13caa5c..1ea4d78 100644
--- a/src/drl_pinball/train/env_vortex.py
+++ b/src/drl_pinball/train/env_vortex.py
@@ -36,14 +36,6 @@ if str(_REPO) not in sys.path:
 from CelerisLab import Simulation
 from CelerisLab.lbm.initializers import add_vortex
 
-_CELERIS = Path("/home/frank14f/CelerisLab")
-_CONFIG_H = _CELERIS / "src/CelerisLab/lbm/kernels/config/config_objects.h"
-_PTX = _CELERIS / "src/CelerisLab/lbm/kernels/kernel.ptx"
-
-def _clean_cache():
-    for p in [_CONFIG_H, _PTX]:
-        if p.exists(): p.unlink()
-
 # ---------------------------------------------------------------------------
 # Geometry constants
 # ---------------------------------------------------------------------------
@@ -177,7 +169,6 @@ class VortexCloakEnv(gym.Env):
 
         # ---- Phase 1: Target (sensors + vortex only, no pinball) ----
         print("  [vortex] Phase 1: Target recording...", flush=True)
-        _clean_cache()
         sim_t = Simulation(lbm_config_path=self._config_path, device_id=self.device_id)
         sim_t._assert_object_count_contract = lambda *a, **kw: None
         s0 = sim_t.add_body("sensor", center=(SENSOR_X, CENTER_Y + 40.0, 0.0), radius=5.0)
@@ -209,7 +200,6 @@ class VortexCloakEnv(gym.Env):
 
         # ---- Phase 2: Training sim (sensors + pinball + vortex) ----
         print("  [vortex] Phase 2: Training sim...", flush=True)
-        _clean_cache()
         self.sim = Simulation(lbm_config_path=self._config_path, device_id=self.device_id)
         self.sim._assert_object_count_contract = lambda *a, **kw: None
         self.sensor_ids = [
diff --git a/src/drl_pinball/train/launch_multi.sh b/src/drl_pinball/train/launch_multi.sh
index c96ccad..5fbe0b3 100755
--- a/src/drl_pinball/train/launch_multi.sh
+++ b/src/drl_pinball/train/launch_multi.sh
@@ -19,13 +19,11 @@
 #
 # Requirements:
 #   - conda env pycuda_3_10
-#   - CelerisLab at /home/frank14f/CelerisLab
+#   - CelerisLab submodule at DynamisLab/CelerisLab
 #   - train_karman.py, env_karman.py, symmetry_wrapper.py in same directory
 set -euo pipefail
 
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-CACHE_H="${HOME}/CelerisLab/src/CelerisLab/lbm/kernels/config/config_objects.h"
-CACHE_PTX="${HOME}/CelerisLab/src/CelerisLab/lbm/kernels/kernel.ptx"
 
 # --- Defaults ---
 CASE_NAME=""
@@ -97,10 +95,6 @@ echo "  Delay:      ${DELAY_SECONDS}s between launches"
 echo "  Jobs:       ${#SEED_ARR[@]}"
 echo ""
 
-# Clean stale cache before starting
-rm -f "$CACHE_H" "$CACHE_PTX"
-echo "  Cleaned kernel cache."
-
 # Build transfer arg
 TRANSFER_ARG=""
 if [[ -n "$TRANSFER" ]]; then
diff --git a/src/drl_pinball/train/analyze_final.py b/src/drl_pinball/train/old/analyze_final.py
similarity index 100%
rename from src/drl_pinball/train/analyze_final.py
rename to src/drl_pinball/train/old/analyze_final.py
diff --git a/src/drl_pinball/train/env_karman_2000x600.py b/src/drl_pinball/train/old/env_karman_2000x600.py
similarity index 99%
rename from src/drl_pinball/train/env_karman_2000x600.py
rename to src/drl_pinball/train/old/env_karman_2000x600.py
index 7391277..7266f71 100644
--- a/src/drl_pinball/train/env_karman_2000x600.py
+++ b/src/drl_pinball/train/old/env_karman_2000x600.py
@@ -38,7 +38,7 @@ if str(_REPO) not in sys.path:
 
 from CelerisLab import Simulation
 
-_CELERIS = Path("/home/frank14f/CelerisLab")
+_CELERIS = _REPO / "CelerisLab"
 _CONFIG_H = _CELERIS / "src/CelerisLab/lbm/kernels/config/config_objects.h"
 _PTX = _CELERIS / "src/CelerisLab/lbm/kernels/kernel.ptx"
 
diff --git a/src/drl_pinball/train/phase0_baseline_measure.py b/src/drl_pinball/train/old/phase0_baseline_measure.py
similarity index 99%
rename from src/drl_pinball/train/phase0_baseline_measure.py
rename to src/drl_pinball/train/old/phase0_baseline_measure.py
index 4f23077..49b6516 100644
--- a/src/drl_pinball/train/phase0_baseline_measure.py
+++ b/src/drl_pinball/train/old/phase0_baseline_measure.py
@@ -37,7 +37,7 @@ from CelerisLab import Simulation
 # ---------------------------------------------------------------------------
 # Hard-coded CelerisLab paths for cache cleaning
 # ---------------------------------------------------------------------------
-_CELERIS = Path("/home/frank14f/CelerisLab")
+_CELERIS = _REPO / "CelerisLab"
 _CONFIG_H = _CELERIS / "src/CelerisLab/lbm/kernels/config/config_objects.h"
 _PTX = _CELERIS / "src/CelerisLab/lbm/kernels/kernel.ptx"
 
diff --git a/src/drl_pinball/train/prompt.md b/src/drl_pinball/train/old/prompt.md
similarity index 100%
rename from src/drl_pinball/train/prompt.md
rename to src/drl_pinball/train/old/prompt.md
diff --git a/src/drl_pinball/train/train_karman_2000x600.py b/src/drl_pinball/train/old/train_karman_2000x600.py
similarity index 100%
rename from src/drl_pinball/train/train_karman_2000x600.py
rename to src/drl_pinball/train/old/train_karman_2000x600.py
diff --git a/src/drl_pinball/train/train_karman.py b/src/drl_pinball/train/train_karman.py
index 5b3d79f..104de6f 100644
--- a/src/drl_pinball/train/train_karman.py
+++ b/src/drl_pinball/train/train_karman.py
@@ -200,9 +200,9 @@ def main() -> int:
         elif ep % 5 == 0:
             log(f"  Ep {ep:3d}: reward={avg_r:.4f} (best={best_reward:.4f}, {dt:.0f}s/ep)")
 
-        if ep % 10 == 0:
-            model.save(str(out_dir / "models" / f"chkpt_ep{ep}.zip"))
-            vec_env.save(norm_path)
+        # Save model + eval summary after EVERY episode
+        model.save(str(out_dir / "models" / f"ep{ep:04d}_model.zip"))
+        vec_env.save(str(out_dir / "models" / f"ep{ep:04d}_vecnormalize.pkl"))
 
     model.save(str(out_dir / "models" / "final_model.zip"))
     vec_env.save(norm_path)