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Datasets:
harryzhang1018
/
TestRandomization

Tasks:
Robotics
Tags:
autonomous-vehicles
path-following
controller-evaluation
simulation
project-chrono
sim2real
License:
Dataset card Files
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harryzhang1018 commited on
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1 Parent(s): 0c3dd1d

Add reproduction code and README (part 2)

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Files changed (45) hide show
  1. code/case2/results/rom_A_explore/rate_0.1.npz +3 -0
  2. code/case2/results/rom_A_explore/rate_rand.json +83 -0
  3. code/case2/results/rom_A_explore/rate_rand.npz +3 -0
  4. code/case2/results/rom_A_explore/rate_rand_paper.json +101 -0
  5. code/case2/results/rom_A_explore/rate_rand_paper.npz +3 -0
  6. code/case2/results/rom_A_explore/tau0_logu.json +83 -0
  7. code/case2/results/rom_A_explore/tau0_logu.npz +3 -0
  8. code/case2/results/rom_A_explore/vmax_u.json +83 -0
  9. code/case2/results/rom_A_explore/vmax_u.npz +3 -0
  10. code/case2/results/rom_A_explore/wb_2.json +82 -0
  11. code/case2/results/rom_A_explore/wb_2.npz +3 -0
  12. code/case2/results/rom_A_explore/wb_3.4.json +82 -0
  13. code/case2/results/rom_A_explore/wb_3.4.npz +3 -0
  14. code/case2/results/rom_A_explore/wb_logu1_8.json +83 -0
  15. code/case2/results/rom_A_explore/wb_logu1_8.npz +3 -0
  16. code/case2/results/rom_A_explore/wb_u1_4.json +83 -0
  17. code/case2/results/rom_A_explore/wb_u1_4.npz +3 -0
  18. code/case2/results/rom_A_explore/yaw_45.json +83 -0
  19. code/case2/results/rom_A_explore/yaw_45.npz +3 -0
  20. code/case2/results/rom_B_confirm16/det_calibrated.json +90 -0
  21. code/case2/results/rom_B_confirm16/det_calibrated.npz +3 -0
  22. code/case2/results/rom_B_confirm16/det_rate_0.1.json +82 -0
  23. code/case2/results/rom_B_confirm16/det_rate_0.1.npz +3 -0
  24. code/case2/results/rom_B_confirm16/goals.json +1352 -0
  25. code/case2/results/rom_B_confirm16/nominal.json +77 -0
  26. code/case2/results/rom_B_confirm16/nominal.npz +3 -0
  27. code/case2/results/rom_B_confirm16/paper_tr.json +96 -0
  28. code/case2/results/rom_B_confirm16/paper_tr.npz +3 -0
  29. code/case2/results/rom_B_confirm16/tr_delay.json +105 -0
  30. code/case2/results/rom_B_confirm16/tr_delay.npz +3 -0
  31. code/case2/results/rom_B_confirm16/tr_rate.json +101 -0
  32. code/case2/results/rom_B_confirm16/tr_rate.npz +3 -0
  33. code/case2/results/rom_B_confirm16/tr_rate_double.json +101 -0
  34. code/case2/results/rom_B_confirm16/tr_rate_double.npz +3 -0
  35. code/case2/results/rom_B_confirm16/tr_rate_half.json +101 -0
  36. code/case2/results/rom_B_confirm16/tr_rate_half.npz +3 -0
  37. code/case2/results/rom_B_confirm16/tr_rate_uniform.json +101 -0
  38. code/case2/results/rom_B_confirm16/tr_rate_uniform.npz +3 -0
  39. code/case2/results/rom_parity.json +26 -0
  40. code/case2/rom_batch.py +122 -0
  41. code/case2/run_rom.py +90 -0
  42. code/case2/train_all.sh +16 -0
  43. code/case2/train_ppo.py +169 -0
  44. code/case2/variants.py +100 -0
  45. code/case2/verify_rom_parity.py +69 -0
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+ "note": "Mismatch = different first-entry step between the release loop and the batched ROM."
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+ }
code/case2/rom_batch.py ADDED
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1
+ """Batched reduced-order model (ROM) for nominal and test-randomized (TR) evaluation. Sim2Sim only.
2
+
3
+ Plant = release GoalEnv.step (util-free copy): kinematic bicycle with car_length=1.0 m, steering scale 0.7 rad,
4
+ motor torque map with tau0/omega0, speed clipped to [0, 5] m/s, dt = 0.1 s, state cast to float32 each step.
5
+ Success = first post-step distance < 3 m; time = (step index + 1) * 0.1 s (release logger is one step early).
6
+
7
+ Each episode carries its own perturbation parameters (one row in `params`). Nominal ROM = all defaults.
8
+ Command path for every step: policy -> steering-rate limiter -> actuation delay -> start hold -> additive noise -> plant.
9
+ Common random numbers: all policies see the same params and noise for the same episode index.
10
+ """
11
+ import numpy as np
12
+ import torch
13
+
14
+ from common import DT, MAX_STEPS, GOAL_RADIUS
15
+
16
+ DEFAULTS = dict(
17
+ tau0=1000.0, omega0=2600.0, # powertrain (release nominal)
18
+ sig_thr=0.0, sig_steer=0.0, # per-step additive Gaussian sd on normalized throttle/steering
19
+ delay=0, # actuation delay (control steps of 0.1 s)
20
+ steer_rate=np.inf, # max |change| of normalized steering per control step
21
+ hold=0, # start hold (control steps with zero command and zero speed)
22
+ wheelbase=1.0, # ROM car_length (m)
23
+ vmax=5.0, # ROM speed cap (m/s)
24
+ x0=0.0, y0=0.0, yaw0=0.0, v0=0.0, # initial state
25
+ gx_scale=1.0, g_rot=0.0, # goal radius scale and bearing rotation (rad), applied to the base goal
26
+ obs_pos_sig=0.0, # sd of observation noise on relative goal position (m), per step
27
+ )
28
+ R_WHEEL, I_WHEEL, GAMMA, C1, C0, STEER_SCALE = 0.47, 6.69, 1.0 / 3.0, 1e-4, 0.02, 0.7
29
+
30
+
31
+ def default_params(n):
32
+ return {k: np.full(n, v, dtype=np.float64) for k, v in DEFAULTS.items()}
33
+
34
+
35
+ def simulate(actor, goals_xy, params, noise_seed, device='cpu', max_steps=MAX_STEPS, keep_traj=False):
36
+ """Run len(goals_xy) episodes for one policy. Returns dict of per-episode arrays.
37
+
38
+ goals_xy: (N,2) base goals. params: dict name -> (N,) arrays (missing keys use defaults).
39
+ noise_seed: seed of the per-step noise stream (same seed -> same noise for every policy).
40
+ """
41
+ n = len(goals_xy)
42
+ P = default_params(n)
43
+ for k, v in params.items():
44
+ P[k] = np.broadcast_to(np.asarray(v, dtype=np.float64), (n,)).copy()
45
+ t = lambda a: torch.as_tensor(a, dtype=torch.float64, device=device)
46
+ tau0, omega0, wb, vmax = t(P['tau0']), t(P['omega0']), t(P['wheelbase']), t(P['vmax'])
47
+ sig = torch.stack([t(P['sig_thr']), t(P['sig_steer'])], 1)
48
+ rate = t(P['steer_rate'])
49
+ hold = torch.as_tensor(P['hold'].astype(np.int64), device=device)
50
+ delay = P['delay'].astype(np.int64)
51
+ dmax = int(delay.max()) if n else 0
52
+ # goal perturbation (radius scale and bearing rotation about the start point)
53
+ g = np.asarray(goals_xy, dtype=np.float64)
54
+ c, s = np.cos(P['g_rot']), np.sin(P['g_rot'])
55
+ gx = P['gx_scale'] * (c * g[:, 0] - s * g[:, 1])
56
+ gy = P['gx_scale'] * (s * g[:, 0] + c * g[:, 1])
57
+ gx, gy = t(gx), t(gy)
58
+ f32 = lambda a: a.to(torch.float32).to(torch.float64)
59
+ x, y, yaw, v = f32(t(P['x0'])), f32(t(P['y0'])), f32(t(P['yaw0'])), f32(t(P['v0']))
60
+ gen = torch.Generator(device=device).manual_seed(int(noise_seed))
61
+ obs_sig = t(P['obs_pos_sig'])
62
+ prev_steer = torch.zeros(n, dtype=torch.float64, device=device)
63
+ buf = torch.zeros((dmax + 1, n, 2), dtype=torch.float64, device=device) # ring buffer for delay
64
+ didx = torch.as_tensor(delay, device=device)
65
+ done = torch.zeros(n, dtype=torch.bool, device=device)
66
+ hit_step = torch.full((n,), -1, dtype=torch.int64, device=device)
67
+ min_d = torch.full((n,), np.inf, dtype=torch.float64, device=device)
68
+ traj = [] if keep_traj else None
69
+ ar = torch.arange(n, device=device)
70
+ for k in range(max_steps):
71
+ # observation (release relative_state_to_goal), optional position noise
72
+ cth, sth = torch.cos(yaw), torch.sin(yaw)
73
+ dx, dy = gx - x, gy - y
74
+ rx, ry = cth * dx + sth * dy, -sth * dx + cth * dy
75
+ if bool((obs_sig > 0).any()):
76
+ rx = rx + obs_sig * torch.randn(n, generator=gen, device=device, dtype=torch.float64)
77
+ ry = ry + obs_sig * torch.randn(n, generator=gen, device=device, dtype=torch.float64)
78
+ obs = torch.stack([rx, ry, torch.atan2(ry, rx), v], 1).to(torch.float32)
79
+ a = actor(obs).to(torch.float64)
80
+ # steering-rate limiter (as in the release Chrono loop)
81
+ d = a[:, 1] - prev_steer
82
+ lim = torch.where(d.abs() > rate, prev_steer + rate * torch.sign(d), a[:, 1])
83
+ prev_steer = lim
84
+ cmd = torch.stack([a[:, 0], lim], 1)
85
+ # actuation delay: write at slot k, read slot k - delay (zeros before the first command)
86
+ buf[k % (dmax + 1)] = cmd
87
+ cmd_d = buf[(k - didx) % (dmax + 1), ar]
88
+ cmd_d = torch.where((k - didx >= 0)[:, None], cmd_d, torch.zeros_like(cmd_d))
89
+ # start hold: zero command and braked vehicle
90
+ held = k < hold
91
+ cmd_d = torch.where(held[:, None], torch.zeros_like(cmd_d), cmd_d)
92
+ # additive noise (release: added after the policy, not clipped again); drawn for all episodes every step
93
+ z = torch.randn((n, 2), generator=gen, device=device, dtype=torch.float64)
94
+ alpha, beta = (cmd_d + sig * z).unbind(1)
95
+ # plant (release GoalEnv.step)
96
+ omega_m = v / (R_WHEEL * GAMMA)
97
+ T = alpha * (-tau0 * omega_m / omega0 + tau0) - C1 * omega_m - C0
98
+ v_dot = T * GAMMA / I_WHEEL * R_WHEEL
99
+ nx = x + torch.cos(yaw) * v * DT
100
+ ny = y + torch.sin(yaw) * v * DT
101
+ nyaw = yaw + v * torch.tan(beta * STEER_SCALE) / wb * DT
102
+ nv = torch.clamp(v + v_dot * DT, min=torch.zeros_like(vmax), max=vmax)
103
+ nv = torch.where(held, torch.zeros_like(nv), nv)
104
+ nx, ny, nyaw, nv = f32(nx), f32(ny), f32(nyaw), f32(nv)
105
+ live = ~done
106
+ x, y, yaw, v = (torch.where(live, nx, x), torch.where(live, ny, y), torch.where(live, nyaw, yaw), torch.where(live, nv, v))
107
+ dist = torch.sqrt((x - gx) ** 2 + (y - gy) ** 2)
108
+ min_d = torch.where(live, torch.minimum(min_d, dist), min_d)
109
+ newhit = live & (dist < GOAL_RADIUS)
110
+ hit_step = torch.where(newhit, torch.full_like(hit_step, k), hit_step)
111
+ done = done | newhit
112
+ if keep_traj:
113
+ traj.append(torch.stack([x, y, yaw, v], 1).cpu().numpy().astype(np.float32))
114
+ if bool(done.all()):
115
+ break
116
+ hs = hit_step.cpu().numpy()
117
+ out = dict(success=hs >= 0, time_s=np.where(hs >= 0, (hs + 1) * DT, np.nan),
118
+ terminal_distance_m=torch.sqrt((x - gx) ** 2 + (y - gy) ** 2).cpu().numpy(),
119
+ min_distance_m=min_d.cpu().numpy())
120
+ if keep_traj:
121
+ out['traj'] = np.stack(traj, 1)
122
+ return out
code/case2/run_rom.py ADDED
@@ -0,0 +1,90 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Evaluate a policy pool on a goal set under nominal ROM and TR variants (Sim2Sim, ROM side).
2
+
3
+ Usage: python run_rom.py --set A --pool explore --variants all
4
+ Writes results/rom_<set>/<variant>.npz (success, time_s, terminal/min distance [policy, goal, trial]; sampled
5
+ parameters [goal, trial]) and results/rom_<set>/<variant>.json (manifest: spec, K, seeds, policies, hashes).
6
+ Common random numbers: the same sampled parameters and noise stream for every policy.
7
+ """
8
+ import argparse
9
+ import json
10
+ import time
11
+ import zlib
12
+
13
+ import numpy as np
14
+ import torch
15
+
16
+ import rom_batch
17
+ import variants as V
18
+ from common import RESULTS, Actor, make_goal_set, policy_registry, rel_path, sha256
19
+
20
+ POOLS = {
21
+ 'explore': ['rel1', 'rel2', 'rel3', 'rel4'] + [f'{c}_s{s}' for c in ('nonoise', 'release', 'robust') for s in (1001, 1002, 1003, 1004)],
22
+ 'confirm': [f'{c}_s{s}' for c in ('nonoise', 'release', 'robust') for s in (2001, 2002, 2003, 2004)],
23
+ 'released': ['rel1', 'rel2', 'rel3', 'rel4'],
24
+ }
25
+ POOLS['confirm16'] = POOLS['confirm'] + POOLS['released'] # set B: primary = confirm (12 held-out), secondary = all 16
26
+ N_PER_RANGE = {'A': 50, 'B': 75}
27
+
28
+
29
+ def seed_of(*parts):
30
+ return zlib.crc32(':'.join(map(str, parts)).encode())
31
+
32
+
33
+ def run_variant(name, K, spec, goals, policies, reg, out_dir, set_name, device):
34
+ G = len(goals)
35
+ rng = np.random.default_rng(seed_of(set_name, name, 'params'))
36
+ params = V.sample(spec, G * K, rng)
37
+ noise_seed = seed_of(set_name, name, 'noise')
38
+ gxy = np.repeat(np.array([[g['x'], g['y']] for g in goals]), K, axis=0)
39
+ shape = (len(policies), G, K)
40
+ res = {k: np.zeros(shape, dtype=np.float32) for k in ('time_s', 'terminal_distance_m', 'min_distance_m')}
41
+ res['success'] = np.zeros(shape, dtype=bool)
42
+ t0 = time.time()
43
+ for i, p in enumerate(policies):
44
+ actor = Actor(reg[p]['path']).to_device(device)
45
+ o = rom_batch.simulate(actor, gxy, params, noise_seed=noise_seed, device=device)
46
+ for k in res:
47
+ res[k][i] = o[k].reshape(G, K)
48
+ np.savez_compressed(out_dir / f'{name}.npz', policies=np.array(policies), goal_ids=np.array([g['goal_id'] for g in goals]),
49
+ **res, **{f'param_{k}': v.reshape(G, K).astype(np.float32) for k, v in params.items()})
50
+ man = dict(variant=name, trials_per_goal=K, spec={k: list(v) for k, v in spec.items()}, goal_set=set_name, goals=G,
51
+ param_seed=seed_of(set_name, name, 'params'), noise_seed=noise_seed, device=device,
52
+ policies={p: dict(path=rel_path(reg[p]['path']), sha256=sha256(reg[p]['path'])) for p in policies},
53
+ wall_s=time.time() - t0)
54
+ (out_dir / f'{name}.json').write_text(json.dumps(man, indent=1) + '\n')
55
+ return res
56
+
57
+
58
+ def main():
59
+ ap = argparse.ArgumentParser()
60
+ ap.add_argument('--set', choices=['A', 'B'], required=True)
61
+ ap.add_argument('--pool', choices=list(POOLS), required=True)
62
+ ap.add_argument('--variants', default='all', help='comma list or all (EXPLORE table, or the fixed design with --table fixed)')
63
+ ap.add_argument('--device', default='cpu')
64
+ ap.add_argument('--tag', default='')
65
+ ap.add_argument('--table', choices=['explore', 'fixed'], default='explore')
66
+ a = ap.parse_args()
67
+ torch.set_num_threads(2)
68
+ if a.set == 'B' and a.table != 'fixed':
69
+ raise SystemExit('goal set B is the held-out set: use --table fixed')
70
+ if a.table == 'fixed':
71
+ import fixed_design
72
+ table = fixed_design.CONDITIONS
73
+ else:
74
+ table = V.EXPLORE
75
+ names = list(table) if a.variants == 'all' else a.variants.split(',')
76
+ goals = make_goal_set(a.set, N_PER_RANGE[a.set])
77
+ reg = policy_registry()
78
+ policies = POOLS[a.pool]
79
+ out_dir = RESULTS / f'rom_{a.set}_{a.pool}{a.tag}'
80
+ out_dir.mkdir(parents=True, exist_ok=True)
81
+ (out_dir / 'goals.json').write_text(json.dumps(goals, indent=1) + '\n')
82
+ for n in names:
83
+ K, spec = table[n]
84
+ r = run_variant(n, K, spec, goals, policies, reg, out_dir, a.set, a.device)
85
+ sr = r['success'].mean((1, 2))
86
+ print(f'{n:18s} K={K:2d} success ' + ' '.join(f'{p}:{s:.2f}' for p, s in zip(policies, sr)), flush=True)
87
+
88
+
89
+ if __name__ == '__main__':
90
+ main()
code/case2/train_all.sh ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env bash
2
+ # Train the 24 Case 2 PPO policies: 3 training-noise configs (nonoise, release, robust) x 8 seeds.
3
+ # Seeds 1001-1004: design policies (exploration, goal set A). Seeds 2001-2004: held-out policies (goal set B).
4
+ # Each run is single-threaded and seeded (about 50 min on one core of a Ryzen 7 5800X).
5
+ # Usage: ./train_all.sh [workers=10] [out_root=policies_retrained] (python: $PYTHON, default python)
6
+ # The released policies in policies/ are not overwritten. Each run writes <out_root>/<config>_s<seed>/
7
+ # (ckpt_1..4.zip at 0.5/1.0/1.5/2.0 M steps, train_meta.json, episode_returns.npy); ckpt_4.zip is the policy.
8
+ set -euo pipefail
9
+ cd "$(dirname "$0")"
10
+ WORKERS=${1:-10}
11
+ OUT=${2:-policies_retrained}
12
+ PY=${PYTHON:-python}
13
+ mkdir -p "$OUT/logs"
14
+ for s in 1001 1002 1003 1004 2001 2002 2003 2004; do
15
+ for cfg in nonoise release robust; do echo "$cfg $s"; done
16
+ done | xargs -P "$WORKERS" -L 1 bash -c 'OMP_NUM_THREADS=1 MKL_NUM_THREADS=1 '"$PY"' train_ppo.py --config $0 --seed $1 --out '"$OUT"'/$0_s$1 > '"$OUT"'/logs/$0_s$1.log 2>&1 && echo done $0 $1'
code/case2/train_ppo.py ADDED
@@ -0,0 +1,169 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Train new PPO goal-reaching policies on the ROM (Sim2Sim campaign, Case 2).
2
+
3
+ Copies the release GoalEnv dynamics, reward and PPO hyperparameters (rl_script/rl_train_goal_reach.py)
4
+ into a Gymnasium env, because SB3 2.7 needs Gymnasium. Only the training-noise config and the seed change.
5
+
6
+ Configs:
7
+ nonoise : no action noise; powertrain fixed at tau0=1000, omega0=2600.
8
+ release : release semantics. N(0,0.05) on throttle and steering each step; tau0, omega0 drawn once
9
+ at env construction with sd 50 and 100.
10
+ robust : N(0,0.10) action noise; tau0, omega0 redrawn every episode with sd 100 and 200;
11
+ actuation delay drawn every episode uniform from {0,1,2} control steps (0-0.2 s).
12
+
13
+ Usage: python train_ppo.py --config release --seed 11 --out policies/release_s11
14
+ """
15
+ import argparse
16
+ import json
17
+ import platform
18
+ import time
19
+ from pathlib import Path
20
+
21
+ import gymnasium as gym
22
+ import numpy as np
23
+ import torch as th
24
+ from gymnasium import spaces
25
+ from stable_baselines3 import PPO
26
+ from stable_baselines3.common.callbacks import BaseCallback
27
+ from stable_baselines3.common.vec_env import DummyVecEnv
28
+
29
+ CONFIGS = {
30
+ 'nonoise': dict(act_sigma=0.0, tau_sigma=0.0, omega_sigma=0.0, powertrain_redraw='never', delay_steps=[0]),
31
+ 'release': dict(act_sigma=0.05, tau_sigma=50.0, omega_sigma=100.0, powertrain_redraw='construction', delay_steps=[0]),
32
+ 'robust': dict(act_sigma=0.10, tau_sigma=100.0, omega_sigma=200.0, powertrain_redraw='episode', delay_steps=[0, 1, 2]),
33
+ }
34
+
35
+
36
+ class GoalEnvG(gym.Env):
37
+ """Release GoalEnv (same constants, dynamics, reward, termination), Gymnasium API, configurable noise."""
38
+
39
+ def __init__(self, cfg, seed):
40
+ super().__init__()
41
+ self.cfg = cfg
42
+ self.rng = np.random.default_rng(seed)
43
+ self.action_space = spaces.Box(low=np.array([0.2, -1.0]), high=np.array([0.5, 1.0]), shape=(2,), dtype=np.float32)
44
+ self.observation_space = spaces.Box(low=-np.inf, high=np.inf, shape=(4,), dtype=np.float32)
45
+ self.r_wheel, self.i_wheel, self.gamma = 0.47, 6.69, 1 / 3
46
+ self.c1, self.c0, self.car_length = 1e-4, 0.02, 1.0
47
+ self.dt, self.max_steps = 0.1, 400
48
+ self._draw_powertrain()
49
+ self.old_distance = 0.0
50
+
51
+ def _draw_powertrain(self):
52
+ self.tau_0 = 1000 + self.rng.normal(0, self.cfg['tau_sigma'])
53
+ self.omega_0 = 2600 + self.rng.normal(0, self.cfg['omega_sigma'])
54
+
55
+ def f_vdot(self, alpha, v):
56
+ omega_m = v / (self.r_wheel * self.gamma)
57
+ f1_m = -self.tau_0 * omega_m / self.omega_0 + self.tau_0
58
+ T_fun = alpha * f1_m - self.c1 * omega_m - self.c0
59
+ return T_fun * self.gamma / self.i_wheel * self.r_wheel
60
+
61
+ def relative_state_to_goal(self):
62
+ x, y, theta, v = self.state[:4]
63
+ rot_mat = np.array([[np.cos(theta), np.sin(theta)], [-np.sin(theta), np.cos(theta)]])
64
+ relative_pos = np.dot(rot_mat, np.array(self.goal) - np.array([x, y]))
65
+ relative_theta = np.arctan2(relative_pos[1], relative_pos[0])
66
+ return np.array([relative_pos[0], relative_pos[1], relative_theta, v], dtype=np.float32)
67
+
68
+ def reset(self, seed=None, options=None):
69
+ if self.cfg['powertrain_redraw'] == 'episode':
70
+ self._draw_powertrain()
71
+ self.delay = int(self.rng.choice(self.cfg['delay_steps']))
72
+ self.queue = [np.array([0.0, 0.0])] * self.delay
73
+ r = self.rng.uniform(10.0, 20.0)
74
+ theta = self.rng.uniform(0, 2 * np.pi)
75
+ self.goal = [r * np.cos(theta), r * np.sin(theta)]
76
+ self.state = np.array([0, 0, 0, 0.0], dtype=np.float32)
77
+ self.steps = 0
78
+ self.old_distance = np.sqrt(self.goal[0] ** 2 + self.goal[1] ** 2)
79
+ return self.relative_state_to_goal(), {}
80
+
81
+ def step(self, action):
82
+ self.queue.append(np.asarray(action, dtype=np.float64))
83
+ alpha, beta = self.queue.pop(0)
84
+ if self.cfg['act_sigma'] > 0:
85
+ alpha = alpha + self.rng.normal(0, self.cfg['act_sigma'])
86
+ beta = beta + self.rng.normal(0, self.cfg['act_sigma'])
87
+ x, y, theta, v = self.state
88
+ x_dot = np.cos(theta) * v
89
+ y_dot = np.sin(theta) * v
90
+ theta_dot = v * np.tan(beta * 0.7) / self.car_length
91
+ v_dot = self.f_vdot(alpha, v)
92
+ x += x_dot * self.dt
93
+ y += y_dot * self.dt
94
+ theta += theta_dot * self.dt
95
+ v += v_dot * self.dt
96
+ v = np.clip(v, 0.0, 5.0)
97
+ self.state = np.array([x, y, theta, v], dtype=np.float32)
98
+ self.steps += 1
99
+ distance = np.sqrt((x - self.goal[0]) ** 2 + (y - self.goal[1]) ** 2)
100
+ progress = self.old_distance - distance
101
+ reward = 0.0
102
+ if progress > 0:
103
+ reward = progress * 50
104
+ if progress < 0.1:
105
+ reward -= 100
106
+ done = False
107
+ if distance < 1.0:
108
+ reward += self.max_steps * 15
109
+ done = True
110
+ if self.steps >= self.max_steps:
111
+ reward -= 1000 * distance
112
+ done = True
113
+ self.old_distance = distance
114
+ # Release treats both goal and timeout as terminal (old Gym done=True); keep that.
115
+ return self.relative_state_to_goal(), float(reward), done, False, {'velocity': float(v)}
116
+
117
+
118
+ class SaveEvery(BaseCallback):
119
+ def __init__(self, every, out):
120
+ super().__init__()
121
+ self.every, self.out, self.k = every, Path(out), 0
122
+ self.ep_returns, self.cur = [], 0.0
123
+
124
+ def _on_step(self):
125
+ self.cur += float(self.locals['rewards'][0])
126
+ if self.locals['dones'][0]:
127
+ self.ep_returns.append(self.cur)
128
+ self.cur = 0.0
129
+ if self.n_calls % self.every == 0:
130
+ self.k += 1
131
+ self.model.save(self.out / f'ckpt_{self.k}.zip')
132
+ return True
133
+
134
+
135
+ def main():
136
+ p = argparse.ArgumentParser()
137
+ p.add_argument('--config', choices=list(CONFIGS), required=True)
138
+ p.add_argument('--seed', type=int, required=True)
139
+ p.add_argument('--out', required=True)
140
+ p.add_argument('--timesteps', type=int, default=2_000_000)
141
+ p.add_argument('--save-every', type=int, default=500_000)
142
+ a = p.parse_args()
143
+ th.set_num_threads(1)
144
+ out = Path(a.out)
145
+ out.mkdir(parents=True, exist_ok=False)
146
+ cfg = CONFIGS[a.config]
147
+ env = GoalEnvG(cfg, seed=a.seed + 7)
148
+ vec = DummyVecEnv([lambda: env])
149
+ model = PPO('MlpPolicy', vec, learning_rate=3e-4, n_steps=env.max_steps * 3, batch_size=64, n_epochs=20,
150
+ gamma=0.99, clip_range=0.2, verbose=0, seed=a.seed, device='cpu',
151
+ policy_kwargs=dict(net_arch=dict(pi=[64, 128, 128, 64], vf=[64, 128, 128, 64]), activation_fn=th.nn.ReLU))
152
+ cb = SaveEvery(a.save_every, out)
153
+ t0 = time.time()
154
+ model.learn(total_timesteps=a.timesteps, callback=cb)
155
+ rets = np.array(cb.ep_returns)
156
+ meta = dict(config=a.config, config_values=cfg, seed=a.seed, env_seed=a.seed + 7, timesteps=a.timesteps,
157
+ save_every=a.save_every, wall_s=time.time() - t0,
158
+ fixed_construction_powertrain=dict(tau0=env.tau_0, omega0=env.omega_0) if cfg['powertrain_redraw'] == 'construction' else None,
159
+ episodes=len(rets), mean_return_last100=float(rets[-100:].mean()) if len(rets) else None,
160
+ hyperparameters=dict(learning_rate=3e-4, n_steps=1200, batch_size=64, n_epochs=20, gamma=0.99, clip_range=0.2,
161
+ net_arch=[64, 128, 128, 64], activation='ReLU'),
162
+ versions=dict(python=platform.python_version(), torch=th.__version__, numpy=np.__version__,
163
+ sb3=__import__('stable_baselines3').__version__))
164
+ np.save(out / 'episode_returns.npy', rets.astype(np.float32))
165
+ (out / 'train_meta.json').write_text(json.dumps(meta, indent=2) + '\n')
166
+
167
+
168
+ if __name__ == '__main__':
169
+ main()
code/case2/variants.py ADDED
@@ -0,0 +1,100 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """ROM evaluation conditions: nominal ROM and test-randomization (TR) variants. Sim2Sim only.
2
+
3
+ Each variant = per-episode parameter distributions + number of trials K per goal.
4
+ Distributions: ('const', v), ('normal', mu, sd), ('uniform', lo, hi), ('loguniform', lo, hi), ('choice', [values]).
5
+ Parameters (see rom_batch.DEFAULTS): tau0, omega0, sig_thr, sig_steer, delay, steer_rate, hold, wheelbase, vmax,
6
+ x0, y0, yaw0, v0, gx_scale, g_rot, obs_pos_sig.
7
+ Every exploration variant tried on goal set A is listed here and in results/exploration_report.md.
8
+ Condition names also seed the random streams (run_rom.seed_of), so they are kept unchanged; 'paper_tr' is the
9
+ noise-only TR (action and powertrain noise).
10
+ """
11
+ import numpy as np
12
+
13
+ PI = float(np.pi)
14
+ REL = dict(tau0=('normal', 1000.0, 50.0), omega0=('normal', 2600.0, 100.0),
15
+ sig_thr=('const', 0.05), sig_steer=('const', 0.05)) # noise-only TR (noise of the release training script)
16
+
17
+
18
+ def scaled_release(c):
19
+ return dict(tau0=('normal', 1000.0, 50.0 * c), omega0=('normal', 2600.0, 100.0 * c),
20
+ sig_thr=('const', 0.05 * c), sig_steer=('const', 0.05 * c))
21
+
22
+
23
+ def agnostic(c):
24
+ return dict(wheelbase=('loguniform', 1.0 / c, 1.0 * c), tau0=('loguniform', 1000.0 / c, 1000.0 * c),
25
+ omega0=('loguniform', 2600.0 / c, 2600.0 * c), vmax=('loguniform', 5.0 / c, 5.0 * c),
26
+ sig_thr=('const', 0.05), sig_steer=('const', 0.05))
27
+
28
+
29
+ EXPLORE = {
30
+ # baseline
31
+ 'nominal': (1, {}),
32
+ # family 1: noise-only TR and scaled versions
33
+ 'paper_tr': (20, REL),
34
+ 'paper_tr_x2': (20, scaled_release(2)),
35
+ 'paper_tr_x4': (20, scaled_release(4)),
36
+ # family 2: action noise only
37
+ 'act_0.1': (20, dict(sig_thr=('const', 0.1), sig_steer=('const', 0.1))),
38
+ 'act_0.2': (20, dict(sig_thr=('const', 0.2), sig_steer=('const', 0.2))),
39
+ 'act_0.3': (20, dict(sig_thr=('const', 0.3), sig_steer=('const', 0.3))),
40
+ # family 3: actuation delay (0.1 s steps)
41
+ 'delay_1': (1, dict(delay=('const', 1))),
42
+ 'delay_2': (1, dict(delay=('const', 2))),
43
+ 'delay_3': (1, dict(delay=('const', 3))),
44
+ 'delay_rand03': (20, dict(delay=('choice', [0, 1, 2, 3]))),
45
+ # family 4: steering-rate limit and start hold (deployment-loop details, deterministic)
46
+ 'rate_0.1': (1, dict(steer_rate=('const', 0.1))),
47
+ 'rate_rand': (20, dict(steer_rate=('loguniform', 0.05, 0.5))),
48
+ 'hold_1s': (1, dict(hold=('const', 10))),
49
+ 'loop_rate_hold': (1, dict(steer_rate=('const', 0.1), hold=('const', 10))),
50
+ # family 5: powertrain parameters (broad) and speed cap
51
+ 'tau0_logu': (20, dict(tau0=('loguniform', 100.0, 1000.0))),
52
+ 'vmax_u': (20, dict(vmax=('uniform', 5.0, 15.0))),
53
+ 'powertrain_speed': (20, dict(tau0=('loguniform', 100.0, 1000.0), vmax=('uniform', 5.0, 15.0))),
54
+ # family 6: steering authority (ROM wheelbase)
55
+ 'wb_2': (1, dict(wheelbase=('const', 2.0))),
56
+ 'wb_3.4': (1, dict(wheelbase=('const', 3.378))),
57
+ 'wb_u1_4': (20, dict(wheelbase=('uniform', 1.0, 4.0))),
58
+ 'wb_logu1_8': (20, dict(wheelbase=('loguniform', 1.0, 8.0))),
59
+ # family 7: initial pose and goal shift
60
+ 'pose_release': (20, dict(x0=('uniform', -1.0, 1.0), y0=('uniform', -1.0, 1.0), yaw0=('uniform', -PI, PI))),
61
+ 'yaw_45': (20, dict(yaw0=('uniform', -PI / 4, PI / 4))),
62
+ 'goal_far': (20, dict(gx_scale=('uniform', 1.0, 2.0))),
63
+ 'goal_rot': (20, dict(g_rot=('uniform', -PI / 6, PI / 6))),
64
+ # family 8: observation noise
65
+ 'obs_0.5': (20, dict(obs_pos_sig=('const', 0.5))),
66
+ # family 9: agnostic parameter TR. Every ROM model parameter x LogUniform(1/c, c) around its nominal value,
67
+ # plus the action noise of the noise-only TR. No direction of model error is assumed.
68
+ 'agn_c2': (20, agnostic(2.0)),
69
+ 'agn_c4': (20, agnostic(4.0)),
70
+ 'agn_c8': (20, agnostic(8.0)),
71
+ 'agn_c4_loop': (20, {**agnostic(4.0), 'steer_rate': ('const', 0.1), 'hold': ('const', 10)}),
72
+ 'agn_c8_loop': (20, {**agnostic(8.0), 'steer_rate': ('const', 0.1), 'hold': ('const', 10)}),
73
+ # family 10: deterministic "calibrated" ROM baselines (not TR): HMMWV wheelbase and/or the deployment loop
74
+ 'calib_wb_loop': (1, dict(wheelbase=('const', 3.378), steer_rate=('const', 0.1), hold=('const', 10))),
75
+ # family 11 (added after a first analysis of the set-A results): actuator TR = random steering-rate limit and
76
+ # random delay, alone and with the noise-only TR
77
+ 'act_rate_delay': (20, dict(steer_rate=('loguniform', 0.05, 0.5), delay=('choice', [0, 1, 2, 3]))),
78
+ 'act_rate_delay_paper': (20, {**REL, 'steer_rate': ('loguniform', 0.05, 0.5), 'delay': ('choice', [0, 1, 2, 3])}),
79
+ 'rate_rand_paper': (20, {**REL, 'steer_rate': ('loguniform', 0.05, 0.5)}),
80
+ }
81
+
82
+
83
+ def sample(spec, n, rng):
84
+ """spec: dict param -> distribution. Returns dict param -> (n,) array."""
85
+ out = {}
86
+ for k, d in spec.items():
87
+ kind = d[0]
88
+ if kind == 'const':
89
+ out[k] = np.full(n, float(d[1]))
90
+ elif kind == 'normal':
91
+ out[k] = rng.normal(d[1], d[2], n)
92
+ elif kind == 'uniform':
93
+ out[k] = rng.uniform(d[1], d[2], n)
94
+ elif kind == 'loguniform':
95
+ out[k] = np.exp(rng.uniform(np.log(d[1]), np.log(d[2]), n))
96
+ elif kind == 'choice':
97
+ out[k] = rng.choice(np.asarray(d[1], dtype=np.float64), n)
98
+ else:
99
+ raise ValueError(kind)
100
+ return out
code/case2/verify_rom_parity.py ADDED
@@ -0,0 +1,69 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Parity check: batched nominal ROM (rom_batch.py) vs the release GoalEnv loop (l2_testing.py logic) with SB3 predict.
2
+
3
+ Needs a checkout of the original repository (github.com/uwsbel/sbel-reproducibility, folder
4
+ 2024/PathFollowingSim2real/hmmwv_goal_reach) and the old `gym` package that its training script imports.
5
+ Release GoalEnv is imported unchanged; its np.random.normal draws are scaled to zero (nominal ROM).
6
+ Goals: 64 seeded goals (seed 77, radius 10-40 m; not goal set A or B). Writes results/rom_parity.json.
7
+
8
+ Usage: python verify_rom_parity.py /path/to/sbel-reproducibility/2024/PathFollowingSim2real/hmmwv_goal_reach
9
+ """
10
+ import io
11
+ import json
12
+ import sys
13
+ from contextlib import redirect_stdout
14
+ from pathlib import Path
15
+
16
+ import numpy as np
17
+ import torch
18
+ from stable_baselines3 import PPO
19
+
20
+ from common import RELEASED_DIR, RESULTS, Actor, sha256
21
+ import rom_batch
22
+
23
+ PROJECT = Path(sys.argv[1]).resolve()
24
+ sys.path.insert(0, str(PROJECT))
25
+ from rl_script.rl_train_goal_reach import GoalEnv # noqa: E402
26
+
27
+ torch.set_num_threads(1)
28
+
29
+
30
+ def release_loop(model, goal):
31
+ orig = np.random.normal
32
+ np.random.normal = lambda loc=0.0, scale=1.0, size=None: orig(loc, 0.0 * scale, size)
33
+ try:
34
+ with redirect_stdout(io.StringIO()):
35
+ env = GoalEnv()
36
+ env.goal = [goal[0], goal[1]]
37
+ obs = env.relative_state_to_goal()
38
+ for i in range(400):
39
+ a, _ = model.predict(obs, deterministic=True)
40
+ obs, _, _, _ = env.step(a)
41
+ if env.old_distance < 3.0:
42
+ return round((i + 1) * 0.1, 1)
43
+ finally:
44
+ np.random.normal = orig
45
+ return None
46
+
47
+
48
+ def main():
49
+ rng = np.random.default_rng(77)
50
+ r, b = rng.uniform(10, 40, 64), rng.uniform(-np.pi, np.pi, 64)
51
+ goals = np.array([[float(ri * np.cos(bi)), float(ri * np.sin(bi))] for ri, bi in zip(r, b)])
52
+ report = dict(goals=len(goals), policies={})
53
+ for k in range(1, 5):
54
+ path = RELEASED_DIR / f'ppo_{k}.zip'
55
+ model = PPO.load(path, device='cpu')
56
+ ref = [release_loop(model, g) for g in goals]
57
+ out = rom_batch.simulate(Actor(path), goals, {}, noise_seed=0)
58
+ mine = [round(float(t), 1) if s else None for s, t in zip(out['success'], out['time_s'])]
59
+ diff = [i for i, (a, b) in enumerate(zip(ref, mine)) if a != b]
60
+ report['policies'][f'rel{k}'] = dict(sha256=sha256(path), mismatches_vs_release_loop=len(diff),
61
+ mismatch_detail=[(i, ref[i], mine[i]) for i in diff])
62
+ print(k, 'mismatches', len(diff), [(i, ref[i], mine[i]) for i in diff][:5], flush=True)
63
+ report['note'] = 'Mismatch = different first-entry step between the release loop and the batched ROM.'
64
+ RESULTS.mkdir(exist_ok=True)
65
+ (RESULTS / 'rom_parity.json').write_text(json.dumps(report, indent=2) + '\n')
66
+
67
+
68
+ if __name__ == '__main__':
69
+ main()