9c7833faf9
* Vegetarian Filet o Fish model * fix.. atc.. * test cluster_speed_limit * fix.. cluster_speed_limit.. 2 * fix.. clusterspeedlimit3 * cruise speed to roadlimit speed * fix.. * fix.. eng * deltaUp/Down for lanechange * fix.. atc desire... * fix.. * ff * ff * fix.. * fix.. eng * fix engsound * Update desire_helper.py * fix.. connect... * fix curve_min speed * Revert "fix curve_min speed" This reverts commit fcc9c2eb14eb3504abef3e420db93e8882e56f37. * Reapply "fix curve_min speed" This reverts commit 2d2bba476c58a7b4e13bac3c3ad0e4694c95515d. * fix.. auto speed up.. roadlimit * fix.. atc auto lanechange... * Update desire_helper.py * Update cruise.py * debug atc... * fix.. waze alert offset.. * fix.. * test atc.. * fix.. * fix.. atc * atc test.. * fix.. atc * fix.. atc2 * fix.. atc3 * KerryGold Model. latsmooth_sec = 0.0 * lat smooth seconds 0.13 * fix comment * fix.. auto cruise, and speed unit * change lanemode switching. * erase mazda lkas button.
77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
import os
|
|
import numpy as np
|
|
import math, random
|
|
from tinygrad.tensor import Tensor
|
|
from tinygrad.nn.state import get_parameters, get_state_dict, safe_save, safe_load, load_state_dict
|
|
from tinygrad.engine.search import actions, bufs_from_lin, get_kernel_actions
|
|
from tinygrad.nn.optim import Adam
|
|
from extra.optimization.extract_policynet import PolicyNet
|
|
from extra.optimization.helpers import load_worlds, ast_str_to_lin, lin_to_feats, time_linearizer
|
|
|
|
if __name__ == "__main__":
|
|
net = PolicyNet()
|
|
if os.path.isfile("/tmp/policynet.safetensors"): load_state_dict(net, safe_load("/tmp/policynet.safetensors"))
|
|
optim = Adam(get_parameters(net))
|
|
|
|
ast_strs = load_worlds()
|
|
|
|
# select a world
|
|
all_feats, all_acts, all_rews = [], [], []
|
|
while 1:
|
|
Tensor.training = False
|
|
lin = ast_str_to_lin(random.choice(ast_strs))
|
|
rawbufs = bufs_from_lin(lin)
|
|
tm = last_tm = base_tm = time_linearizer(lin, rawbufs)
|
|
|
|
# take actions
|
|
feats, acts, rews = [], [], []
|
|
while 1:
|
|
feat = lin_to_feats(lin)
|
|
feats.append(feat)
|
|
probs = net(Tensor([feat])).exp()[0].numpy()
|
|
|
|
# mask valid actions
|
|
valid_action_mask = np.zeros((len(actions)+1), dtype=np.float32)
|
|
for x in get_kernel_actions(lin): valid_action_mask[x] = 1
|
|
probs *= valid_action_mask
|
|
probs /= sum(probs)
|
|
|
|
act = np.random.choice(len(probs), p=probs)
|
|
acts.append(act)
|
|
if act == 0:
|
|
rews.append(0)
|
|
break
|
|
try:
|
|
lin.apply_opt(actions[act-1])
|
|
tm = time_linearizer(lin, rawbufs)
|
|
if math.isinf(tm): raise Exception("failed")
|
|
rews.append(((last_tm-tm)/base_tm))
|
|
last_tm = tm
|
|
except Exception:
|
|
rews.append(-0.5)
|
|
break
|
|
#print(f"{tm*1e6:10.2f}", lin.colored_shape())
|
|
|
|
assert len(feats) == len(acts) and len(acts) == len(rews)
|
|
#print(rews)
|
|
print(f"***** EPISODE {len(rews)} steps, {sum(rews):5.2f} reward, {base_tm*1e6:12.2f} -> {tm*1e6:12.2f} : {lin.colored_shape()}")
|
|
all_feats += feats
|
|
all_acts += acts
|
|
# rewards to go
|
|
for i in range(len(rews)-2, -1, -1): rews[i] += rews[i+1]
|
|
all_rews += rews
|
|
|
|
BS = 32
|
|
if len(all_feats) >= BS:
|
|
Tensor.training = True
|
|
x = Tensor(all_feats[:BS])
|
|
mask = np.zeros((BS, len(actions)+1), dtype=np.float32)
|
|
mask[range(BS), all_acts[:BS]] = all_rews[:BS]
|
|
loss = -(net(x) * Tensor(mask)).mean()
|
|
optim.zero_grad()
|
|
loss.backward()
|
|
optim.step()
|
|
all_feats = all_feats[BS:]
|
|
all_acts = all_acts[BS:]
|
|
all_rews = all_rews[BS:]
|