def test_run_async(self):
counter = mp.Value('l', 0)
def run_func(process_idx):
for _ in range(1000):
with counter.get_lock():
counter.value += 1
async_.run_async(4, run_func)
self.assertEqual(counter.value, 4000)
def test_run_async_exit_code(self):
def run_with_exit_code_0(process_idx):
sys.exit(0)
def run_with_exit_code_11(process_idx):
os.kill(os.getpid(), signal.SIGSEGV)
with warnings.catch_warnings(record=True) as w:
async_.run_async(4, run_with_exit_code_0)
# There should be no warnings
assert len(w) == 0
with warnings.catch_warnings(record=True) as w:
async_.run_async(4, run_with_exit_code_11)
# There should be 4 warnings
assert len(w) == 4
def test_run_async_exit_code(self):
def run_with_exit_code_0(process_idx):
sys.exit(0)
def run_with_exit_code_11(process_idx):
os.kill(os.getpid(), signal.SIGSEGV)
with warnings.catch_warnings(record=True) as ws:
async_.run_async(4, run_with_exit_code_0)
# There should be no AbnormalExitWarning
self.assertEqual(
sum(1 if issubclass(w.category, async_.AbnormalExitWarning
) else 0 for w in ws), 0)
with warnings.catch_warnings(record=True) as ws:
async_.run_async(4, run_with_exit_code_11)
# There should be 4 AbnormalExitWarning
self.assertEqual(
sum(1 if issubclass(w.category, async_.AbnormalExitWarning
) else 0 for w in ws), 4)
def train_agent_async(outdir, processes, make_env,
profile=False,
steps=8 * 10 ** 7,
eval_interval=10 ** 6,
eval_n_runs=10,
max_episode_len=None,
step_offset=0,
successful_score=None,
agent=None,
make_agent=None,
global_step_hooks=[],
save_best_so_far_agent=True,
logger=None,
):
"""Train agent asynchronously using multiprocessing.
Either `agent` or `make_agent` must be specified.
Args:
outdir (str): Path to the directory to output things.
processes (int): Number of processes.
make_env (callable): (process_idx, test) -> Environment.
profile (bool): Profile if set True.
steps (int): Number of global time steps for training.
eval_interval (int): Interval of evaluation. If set to None, the agent
will not be evaluated at all.
eval_n_runs (int): Number of runs for each time of evaluation.
max_episode_len (int): Maximum episode length.
step_offset (int): Time step from which training starts.
successful_score (float): Finish training if the mean score is greater
or equal to this value if not None
agent (Agent): Agent to train.
make_agent (callable): (process_idx) -> Agent
global_step_hooks (list): List of callable objects that accepts
(env, agent, step) as arguments. They are called every global
step. See chainerrl.experiments.hooks.
save_best_so_far_agent (bool): If set to True, after each evaluation,
if the score (= mean return of evaluation episodes) exceeds
the best-so-far score, the current agent is saved.
logger (logging.Logger): Logger used in this function.
Returns:
Trained agent.
"""
logger = logger or logging.getLogger(__name__)
# Prevent numpy from using multiple threads
os.environ['OMP_NUM_THREADS'] = '1'
counter = mp.Value('l', 0)
episodes_counter = mp.Value('l', 0)
training_done = mp.Value('b', False) # bool
if agent is None:
assert make_agent is not None
agent = make_agent(0)
shared_objects = extract_shared_objects_from_agent(agent)
set_shared_objects(agent, shared_objects)
if eval_interval is None:
evaluator = None
else:
evaluator = AsyncEvaluator(
n_runs=eval_n_runs,
eval_interval=eval_interval, outdir=outdir,
max_episode_len=max_episode_len,
step_offset=step_offset,
save_best_so_far_agent=save_best_so_far_agent,
logger=logger,
)
def run_func(process_idx):
random_seed.set_random_seed(process_idx)
env = make_env(process_idx, test=False)
if evaluator is None:
eval_env = env
else:
eval_env = make_env(process_idx, test=True)
if make_agent is not None:
local_agent = make_agent(process_idx)
set_shared_objects(local_agent, shared_objects)
else:
local_agent = agent
local_agent.process_idx = process_idx
def f():
train_loop(
process_idx=process_idx,
counter=counter,
episodes_counter=episodes_counter,
agent=local_agent,
env=env,
steps=steps,
outdir=outdir,
max_episode_len=max_episode_len,
evaluator=evaluator,
successful_score=successful_score,
training_done=training_done,
eval_env=eval_env,
global_step_hooks=global_step_hooks,
logger=logger)
if profile:
import cProfile
cProfile.runctx('f()', globals(), locals(),
'profile-{}.out'.format(os.getpid()))
else:
f()
env.close()
if eval_env is not env:
eval_env.close()
async_.run_async(processes, run_func)
return agent
