def test_reward_clipping(self):
# Clipping: True (clip between -1.0 and 1.0).
ev = RolloutWorker(
env_creator=lambda _: MockEnv2(episode_length=10),
policy_spec=MockPolicy,
clip_rewards=True,
batch_mode="complete_episodes",
)
self.assertEqual(max(ev.sample()["rewards"]), 1)
result = collect_metrics(ev, [])
self.assertEqual(result["episode_reward_mean"], 1000)
ev.stop()
from ray.rllib.examples.env.random_env import RandomEnv
# Clipping in certain range (-2.0, 2.0).
ev2 = RolloutWorker(
env_creator=lambda _: RandomEnv(
dict(
reward_space=gym.spaces.Box(low=-10, high=10, shape=()),
p_done=0.0,
max_episode_len=10,
)
),
policy_spec=MockPolicy,
clip_rewards=2.0,
batch_mode="complete_episodes",
)
sample = ev2.sample()
self.assertEqual(max(sample["rewards"]), 2.0)
self.assertEqual(min(sample["rewards"]), -2.0)
self.assertLess(np.mean(sample["rewards"]), 0.5)
self.assertGreater(np.mean(sample["rewards"]), -0.5)
ev2.stop()
# Clipping: Off.
ev2 = RolloutWorker(
env_creator=lambda _: MockEnv2(episode_length=10),
policy_spec=MockPolicy,
clip_rewards=False,
batch_mode="complete_episodes",
)
self.assertEqual(max(ev2.sample()["rewards"]), 100)
result2 = collect_metrics(ev2, [])
self.assertEqual(result2["episode_reward_mean"], 1000)
ev2.stop()
def test_hard_horizon(self):
ev = RolloutWorker(
env_creator=lambda _: MockEnv2(episode_length=10),
policy_spec=MockPolicy,
batch_mode="complete_episodes",
rollout_fragment_length=10,
episode_horizon=4,
soft_horizon=False,
)
samples = ev.sample()
# Three logical episodes and correct episode resets (always after 4
# steps).
self.assertEqual(len(set(samples["eps_id"])), 3)
for i in range(4):
self.assertEqual(np.argmax(samples["obs"][i]), i)
self.assertEqual(np.argmax(samples["obs"][4]), 0)
# 3 done values.
self.assertEqual(sum(samples["dones"]), 3)
ev.stop()
# A gym env's max_episode_steps is smaller than Trainer's horizon.
ev = RolloutWorker(
env_creator=lambda _: gym.make("CartPole-v0"),
policy_spec=MockPolicy,
batch_mode="complete_episodes",
rollout_fragment_length=10,
episode_horizon=6,
soft_horizon=False,
)
samples = ev.sample()
# 12 steps due to `complete_episodes` batch_mode.
self.assertEqual(len(samples["eps_id"]), 12)
# Two logical episodes and correct episode resets (always after 6(!)
# steps).
self.assertEqual(len(set(samples["eps_id"])), 2)
# 2 done values after 6 and 12 steps.
check(
samples["dones"],
[
False,
False,
False,
False,
False,
True,
False,
False,
False,
False,
False,
True,
],
)
ev.stop()
def test_multi_env_seed(self):
ev = RolloutWorker(env_creator=lambda _: MockEnv2(100),
num_envs=3,
policy_spec=MockPolicy,
seed=1)
# Make sure we can properly sample from the wrapped env.
ev.sample()
# Make sure all environments got a different deterministic seed.
seeds = ev.foreach_env(lambda env: env.rng_seed)
self.assertEqual(seeds, [1, 2, 3])
ev.stop()
def __init__(self, num, increment_obs=False):
if increment_obs:
# Observations are 0, 1, 2, 3... etc. as time advances
self.agents = [MockEnv2(5) for _ in range(num)]
else:
# Observations are all zeros
self.agents = [MockEnv(5) for _ in range(num)]
self.dones = set()
self.last_obs = {}
self.last_rew = {}
self.last_done = {}
self.last_info = {}
self.i = 0
self.num = num
self.observation_space = gym.spaces.Discrete(10)
self.action_space = gym.spaces.Discrete(2)
def test_wrap_gym_env(self):
record_env_dir = os.popen("mktemp -d").read()[:-1]
print(f"tmp dir for videos={record_env_dir}")
if not os.path.exists(record_env_dir):
sys.exit(1)
num_steps_per_episode = 10
wrapped = record_env_wrapper(
env=MockEnv2(num_steps_per_episode),
record_env=record_env_dir,
log_dir="",
policy_config={
"in_evaluation": False,
},
)
# Non MultiAgentEnv: Wrapper's type is wrappers.Monitor.
self.assertTrue(isinstance(wrapped, gym.wrappers.Monitor))
self.assertFalse(isinstance(wrapped, VideoMonitor))
wrapped.reset()
# Expect one video file to have been produced in the tmp dir.
os.chdir(record_env_dir)
ls = glob.glob("*.mp4")
self.assertTrue(len(ls) == 1)
# 10 steps for a complete episode.
for i in range(num_steps_per_episode):
wrapped.step(0)
# Another episode.
wrapped.reset()
for i in range(num_steps_per_episode):
wrapped.step(0)
# Expect another video file to have been produced (2nd episode).
ls = glob.glob("*.mp4")
self.assertTrue(len(ls) == 2)
# MockEnv2 returns a reward of 100.0 every step.
# So total reward is 1000.0 per episode (10 steps).
check(
np.array([100.0, 100.0]) * num_steps_per_episode,
wrapped.get_episode_rewards(),
)
# Erase all generated files and the temp path just in case,
# as to not disturb further CI-tests.
shutil.rmtree(record_env_dir)
def test_wrap_gym_env(self):
wrapped = record_env_wrapper(env=MockEnv2(10),
record_env=tempfile.gettempdir(),
log_dir="",
policy_config={
"in_evaluation": False,
})
# Type is wrappers.Monitor.
self.assertTrue(isinstance(wrapped, wrappers.Monitor))
self.assertFalse(isinstance(wrapped, VideoMonitor))
wrapped.reset()
# 10 steps for a complete episode.
for i in range(10):
wrapped.step(0)
# MockEnv2 returns a reward of 100.0 every step.
# So total reward is 1000.0.
self.assertEqual(wrapped.get_episode_rewards(), [1000.0])
