def _evaluate(env_config, num_episode):
s = time.time()
np.random.seed(0)
env = PGDriveEnv(env_config)
obs = env.reset()
success_list, reward_list, ep_reward, ep_len, ep_count = [], [], 0, 0, 0
while ep_count < num_episode:
action = expert(obs, deterministic=True)
obs, reward, done, info = env.step(action)
ep_reward += reward
ep_len += 1
if done:
ep_count += 1
success_list.append(1 if get_terminal_state(info) ==
"Success" else 0)
reward_list.append(ep_reward)
ep_reward = 0
ep_len = 0
obs = env.reset()
env.close()
t = time.time() - s
ep_reward_mean = sum(reward_list) / len(reward_list)
success_rate = sum(success_list) / len(success_list)
print(
f"Finish {ep_count} episodes in {t:.3f} s. Episode reward: {ep_reward_mean}, success rate: {success_rate}."
)
return ep_reward_mean, success_rate
def _evaluate(env_config, num_episode, has_traffic=True):
s = time.time()
np.random.seed(0)
env = PGDriveEnv(env_config)
try:
obs = env.reset()
lidar_success = False
success_list, reward_list, ep_reward, ep_len, ep_count = [], [], 0, 0, 0
while ep_count < num_episode:
action = expert(obs, deterministic=True)
obs, reward, done, info = env.step(action)
# double check lidar
lidar = [True if p == 1.0 else False for p in env.observations[DEFAULT_AGENT].cloud_points]
if not all(lidar):
lidar_success = True
ep_reward += reward
ep_len += 1
if done:
ep_count += 1
success_list.append(1 if get_terminal_state(info) == "Success" else 0)
reward_list.append(ep_reward)
ep_reward = 0
ep_len = 0
obs = env.reset()
if has_traffic:
assert lidar_success
lidar_success = False
env.close()
t = time.time() - s
ep_reward_mean = sum(reward_list) / len(reward_list)
success_rate = sum(success_list) / len(success_list)
print(
f"Finish {ep_count} episodes in {t:.3f} s. Episode reward: {ep_reward_mean}, success rate: {success_rate}."
)
finally:
env.close()
return ep_reward_mean, success_rate
import random
from pgdrive import PGDriveEnv
from pgdrive.examples import expert, get_terminal_state
if __name__ == '__main__':
env = PGDriveEnv(
dict(use_render=True,
environment_num=100,
start_seed=random.randint(0, 1000),
map=7))
obs = env.reset()
success_list, reward_list, ep_reward, ep_len, ep_count = [], [], 0, 0, 0
try:
while True:
action = expert(obs)
obs, reward, done, info = env.step(action)
ep_reward += reward
ep_len += 1
# env.render()
if done:
ep_count += 1
success_list.append(1 if get_terminal_state(info) ==
"Success" else 0)
reward_list.append(ep_reward)
print(
"{} episodes terminated! Length: {}, Reward: {:.4f}, Terminal state: {}."
.format(ep_count, ep_len, ep_reward,
get_terminal_state(info)))
ep_reward = 0
ep_len = 0