def test_measure_RL_performance_basics():
env = DummyEnvironment(start=0, target=5, max_value=10)
from sequoia.settings.active.continual.continual_rl_setting import \
ContinualRLSetting
# env = TypedObjectsWrapper(env, observations_type=ContinualRLSetting.Observations, actions_type=ContinualRLSetting.Actions, rewards_type=ContinualRLSetting.Rewards)
env = MeasureRLPerformanceWrapper(env)
env.seed(123)
all_episode_rewards = []
all_episode_steps = []
for episode in range(5):
episode_steps = 0
episode_reward = 0
obs = env.reset()
print(f"Episode {episode}, obs: {obs}")
done = False
while not done:
action = env.action_space.sample()
obs, reward, done, info = env.step(action)
episode_reward += reward
episode_steps += 1
# print(obs, reward, done, info)
all_episode_steps.append(episode_steps)
all_episode_rewards.append(episode_reward)
from itertools import accumulate
expected_metrics = {}
for episode_steps, cumul_step, episode_reward in zip(
all_episode_steps, accumulate(all_episode_steps),
all_episode_rewards):
expected_metrics[cumul_step] = EpisodeMetrics(
n_samples=1,
mean_episode_reward=episode_reward,
mean_episode_length=episode_steps,
)
assert env.get_online_performance() == expected_metrics
def test_measure_RL_performance_iteration():
env = DummyEnvironment(start=0, target=5, max_value=10)
from gym.wrappers import TimeLimit
max_episode_steps = 50
env = EnvDataset(env)
env = TimeLimit(env, max_episode_steps=max_episode_steps)
# env = TypedObjectsWrapper(env, observations_type=ContinualRLSetting.Observations, actions_type=ContinualRLSetting.Actions, rewards_type=ContinualRLSetting.Rewards)
env = MeasureRLPerformanceWrapper(env)
env.seed(123)
all_episode_rewards = []
all_episode_steps = []
for episode in range(5):
episode_steps = 0
episode_reward = 0
for step, obs in enumerate(env):
print(f"Episode {episode}, obs: {obs}")
action = env.action_space.sample()
reward = env.send(action)
episode_reward += reward
episode_steps += 1
# print(obs, reward, done, info)
assert step <= max_episode_steps, "shouldn't be able to iterate longer than that."
all_episode_steps.append(episode_steps)
all_episode_rewards.append(episode_reward)
expected_metrics = {}
for episode_steps, cumul_step, episode_reward in zip(
all_episode_steps, accumulate(all_episode_steps),
all_episode_rewards):
expected_metrics[cumul_step] = EpisodeMetrics(
n_samples=1,
mean_episode_reward=episode_reward,
mean_episode_length=episode_steps,
)
assert env.get_online_performance() == expected_metrics