def test_monitor(tmp_path):
"""
Test the monitor wrapper
"""
env = gym.make("CartPole-v1")
env.seed(0)
monitor_file = os.path.join(
str(tmp_path),
"stable_baselines-test-{}.monitor.csv".format(uuid.uuid4()))
monitor_env = Monitor(env, monitor_file)
monitor_env.reset()
total_steps = 1000
ep_rewards = []
ep_lengths = []
ep_len, ep_reward = 0, 0
for _ in range(total_steps):
_, reward, done, _ = monitor_env.step(
monitor_env.action_space.sample())
ep_len += 1
ep_reward += reward
if done:
ep_rewards.append(ep_reward)
ep_lengths.append(ep_len)
monitor_env.reset()
ep_len, ep_reward = 0, 0
monitor_env.close()
assert monitor_env.get_total_steps() == total_steps
assert sum(ep_lengths) == sum(monitor_env.get_episode_lengths())
assert sum(monitor_env.get_episode_rewards()) == sum(ep_rewards)
_ = monitor_env.get_episode_times()
with open(monitor_file, "rt") as file_handler:
first_line = file_handler.readline()
assert first_line.startswith("#")
metadata = json.loads(first_line[1:])
assert metadata["env_id"] == "CartPole-v1"
assert set(metadata.keys()) == {"env_id", "t_start"
}, "Incorrect keys in monitor metadata"
last_logline = pandas.read_csv(file_handler, index_col=None)
assert set(
last_logline.keys()) == {"l", "t",
"r"}, "Incorrect keys in monitor logline"
os.remove(monitor_file)
out_ref=inflow.expectation(),
**env_cfg)
with open(f'{timestamp}/env.txt', 'w') as f:
print(str(env), file=f)
with open(f'{timestamp}/inflow.txt', 'w') as f:
print(str(inflow), file=f)
env = Monitor(env)
model = PPO('MlpPolicy',
env,
verbose=1,
tensorboard_log=f'{timestamp}/',
gamma=.5)
model.learn(total_timesteps=5000000)
model.save(f'{timestamp}/model')
visualize_nets(env, model, timestamp)
rec = VideoRecorder(env, f'{timestamp}/vid.mp4')
obs = env.reset()
for i in range(1000):
action, _states = model.predict(obs, deterministic=True)
obs, reward, done, info = env.step(action)
env.unwrapped.render()
rec.capture_frame()
if done:
obs = env.reset()
env.close()
rec.close()
