def test_crash_only_one_worker_during_sampling_but_recreate(self):
"""Expect some sub-envs to fail (and not recover), but re-create worker."""
config = (
pg.PGConfig().rollouts(
num_rollout_workers=2,
rollout_fragment_length=10,
num_envs_per_worker=3,
# Re-create failed workers (then continue).
recreate_failed_workers=True,
).training(train_batch_size=20).environment(
env=CartPoleCrashing,
env_config={
# Crash prob=1%.
"p_crash": 0.01,
# Only crash on worker with index 2.
"crash_on_worker_indices": [2],
# Make sure nothing happens during pre-checks.
"skip_env_checking": True,
},
))
# Pre-checking disables, so building the Algorithm is save.
algo = config.build()
# Try to re-create for infinite amount of times.
# The worker recreation/ignore tolerance used to be hard-coded to 3, but this
# has now been
for _ in range(10):
# Expect some errors being logged here, but in general, should continue
# as we recover from all worker failures.
algo.train()
# One worker has been removed, then re-created -> Still 2 left.
self.assertTrue(len(algo.workers.remote_workers()) == 2)
algo.stop()
def test_crash_only_one_worker_during_sampling_but_ignore(self):
"""Expect some sub-envs to fail (and not recover), but ignore."""
config = (
pg.PGConfig().rollouts(
num_rollout_workers=2,
num_envs_per_worker=3,
# Ignore worker failures (continue with worker #2).
ignore_worker_failures=True,
).environment(
env=CartPoleCrashing,
env_config={
# Crash prob=80%.
"p_crash": 0.8,
# Only crash on worker with index 1.
"crash_on_worker_indices": [1],
# Make sure nothing happens during pre-checks.
"skip_env_checking": True,
},
))
# Pre-checking disables, so building the Algorithm is save.
algo = config.build()
# Expect some errors being logged here, but in general, should continue
# as we ignore worker failures.
algo.train()
# One worker has been removed -> Only one left.
self.assertTrue(len(algo.workers.remote_workers()) == 1)
algo.stop()
def test_crash_sub_envs_during_sampling_but_restart_sub_envs(self):
"""Expect sub-envs to fail (and not recover), but re-start them individually."""
config = (
pg.PGConfig().rollouts(
num_rollout_workers=2,
num_envs_per_worker=3,
# Re-start failed individual sub-envs (then continue).
# This means no workers will ever fail due to individual env errors
# (only maybe for reasons other than the env).
restart_failed_sub_environments=True,
# If the worker was affected by an error (other than the env error),
# allow it to be removed, but training will continue.
ignore_worker_failures=True,
).environment(
env=CartPoleCrashing,
env_config={
# Crash prob=1%.
"p_crash": 0.01,
# Make sure nothing happens during pre-checks.
"skip_env_checking": True,
},
))
# Pre-checking disables, so building the Algorithm is save.
algo = config.build()
# Try to re-create the sub-env for infinite amount of times.
# The worker recreation/ignore tolerance used to be hard-coded to 3, but this
# has now been
for _ in range(10):
# Expect some errors being logged here, but in general, should continue
# as we recover from all sub-env failures.
algo.train()
# No worker has been removed. Still 2 left.
self.assertTrue(len(algo.workers.remote_workers()) == 2)
algo.stop()
def test_crash_during_env_pre_checking(self):
"""Expect the env pre-checking to fail on each worker."""
config = (
pg.PGConfig().rollouts(num_rollout_workers=2,
num_envs_per_worker=4).
environment(
env=CartPoleCrashing,
env_config={
# Crash prob=100% (during pre-checking's `step()` test calls).
"p_crash": 1.0,
"init_time_s": 0.5,
},
))
# Expect ValueError due to pre-checking failing (our pre-checker module
# raises a ValueError if `step()` fails).
self.assertRaisesRegex(
ValueError,
"Simulated env crash!",
lambda: config.build(),
)
def test_crash_during_sampling(self):
"""Expect some sub-envs to fail (and not recover)."""
config = (
pg.PGConfig().rollouts(
num_rollout_workers=2, num_envs_per_worker=3).environment(
env=CartPoleCrashing,
env_config={
# Crash prob=20%.
"p_crash": 0.2,
"init_time_s": 0.3,
# Make sure nothing happens during pre-checks.
"skip_env_checking": True,
},
))
# Pre-checking disables, so building the Algorithm is save.
algo = config.build()
# Expect EnvError due to the sub-env(s) crashing on the different workers
# and `ignore_worker_failures=False` (so the original EnvError should
# just be bubbled up by RLlib Algorithm and tune.Trainable during the `step()`
# call).
self.assertRaisesRegex(EnvError, "Simulated env crash!",
lambda: algo.train())