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0c3dd1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | """Policy behavior in nominal ROM on a goal set: mean throttle and steering chatter (Sim2Sim, descriptive).
Usage: python behavior_stats.py A explore -> results/behavior_<set>_<pool>.json"""
import json
import sys
import numpy as np
import torch
import rom_batch
from common import RESULTS, Actor, make_goal_set, policy_registry, relative_obs
from run_rom import N_PER_RANGE, POOLS
torch.set_num_threads(2)
set_name, pool = sys.argv[1:3]
reg = policy_registry()
goals = make_goal_set(set_name, N_PER_RANGE[set_name])
gxy = np.array([[g['x'], g['y']] for g in goals])
out = {}
for p in POOLS[pool]:
actor = Actor(reg[p]['path'])
o = rom_batch.simulate(actor, gxy, {}, noise_seed=0, keep_traj=True)
tr = o['traj']
S = tr.shape[1]
acts = np.stack([actor(relative_obs(*(tr[:, k, i].astype(np.float64) for i in range(4)), gxy[:, 0], gxy[:, 1])).numpy()
for k in range(S)], 1)
steps = np.where(o['success'], np.round(o['time_s'] / 0.1).astype(int), S)
valid = np.arange(S)[None, :] < steps[:, None]
ds = np.abs(np.diff(acts[:, :, 1], axis=1))[valid[:, 1:]]
out[p] = dict(mean_throttle=float(acts[:, :, 0][valid].mean()), mean_abs_steer_change=float(ds.mean()),
share_steer_change_gt_0p1=float((ds > 0.1).mean()), share_saturated_steer=float((np.abs(acts[:, :, 1][valid]) > 0.95).mean()))
print(p, {k: round(v, 3) for k, v in out[p].items()}, flush=True)
(RESULTS / f'behavior_{set_name}_{pool}.json').write_text(json.dumps(out, indent=1) + '\n')
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