def test_basic(self):
e = env.DeepTraffic(lanes_side=1,
patches_ahead=20,
patches_behind=10,
history=0)
r = e.reset()
self.assertEqual(r.shape, (1, 3, 30))
def test_hist(self):
e = env.DeepTraffic(lanes_side=1,
patches_ahead=20,
patches_behind=10,
history=3)
r = e.reset()
self.assertEqual(r.shape, (19, 3, 30))
np.testing.assert_array_equal(r[4], 1.0)
np.testing.assert_array_equal(r[5:9], 0.0)
np.testing.assert_array_equal(r[9], 1.0)
np.testing.assert_array_equal(r[10:14], 0.0)
np.testing.assert_array_equal(r[14], 1.0)
np.testing.assert_array_equal(r[15:19], 0.0)
def test_agent(ini, net, steps=1000, rounds=5, device=torch.device('cpu')):
round_means = []
for _ in range(rounds):
speed_hist = []
test_env = env.DeepTraffic(lanes_side=ini.env_lanes_side,
patches_ahead=ini.env_patches_ahead,
patches_behind=ini.env_patches_behind,
history=ini.env_history,
obs=ini.env_obs)
obs = test_env.reset()
for _ in range(steps):
speed_hist.append(test_env.current_speed())
obs_v = torch.tensor([obs]).to(device)
q_v = net(obs_v)[0]
act_idx = torch.argmax(q_v).item()
obs, reward, _, _ = test_env.step(act_idx)
round_means.append(np.mean(speed_hist))
return np.mean(round_means), np.std(round_means)
action='store_true',
help="Display model and exit")
args = parser.parse_args()
ini = config.Settings(args.ini)
device = torch.device("cuda" if ini.train_cuda else "cpu")
if not args.show_model:
name = pathlib.Path(args.ini).stem + "-" + args.name
save_path = pathlib.Path("saves") / name
save_path.mkdir(parents=True, exist_ok=True)
writer = SummaryWriter(comment="-" + name)
e = env.DeepTraffic(lanes_side=ini.env_lanes_side,
patches_ahead=ini.env_patches_ahead,
patches_behind=ini.env_patches_behind,
history=ini.env_history,
obs=ini.env_obs)
orig_env = e
obs_shape = e.obs_shape
e = gym.wrappers.TimeLimit(e, max_episode_steps=ini.env_steps_limit)
log.info("Environment created, obs shape %s", obs_shape)
model_class = model.MODELS[ini.train_model]
net = model_class(obs_shape, e.action_space.n).to(device)
log.info("Model: %s", net)
if args.show_model:
sys.exit(0)
tgt_net = ptan.agent.TargetNet(net)