def setUpClass(cls):
env = GymEnv('Pendulum-v0')
random_pol = RandomPol(cls.env.observation_space, cls.env.action_space)
sampler = EpiSampler(cls.env, pol, num_parallel=1)
epis = sampler.sample(pol, max_steps=32)
traj = Traj()
traj.add_epis(epis)
traj.register_epis()
cls.num_step = traj.num_step
make_redis('localhost', '6379')
cls.r = get_redis()
cls.r.set('env', env)
cls.r.set('traj', traj)
pol_net = PolNet(env.observation_space, env.action_space)
gpol = GaussianPol(env.observation_space, env.action_space, pol_net)
pol_net = PolNet(env.observation_space,
env.action_space, deterministic=True)
dpol = DeterministicActionNoisePol(
env.observation_space, env.action_space, pol_net)
model_net = ModelNet(env.observation_space, env.action_space)
mpcpol = MPCPol(env.observation_space,
env.action_space, model_net, rew_func)
q_net = QNet(env.observation_space, env.action_space)
qfunc = DeterministicSAVfunc(
env.observation_space, env.action_space, q_net)
aqpol = ArgmaxQfPol(env.observation_space, env.action_space, qfunc)
v_net = VNet(env.observation_space)
vfunc = DeterministicSVfunc(env.observation_space, v_net)
cls.r.set('gpol', cloudpickle.dumps(gpol))
cls.r.set('dpol', cloudpickle.dumps(dpol))
cls.r.set('mpcpol', cloudpickle.dumps(mpcpol))
cls.r.set('qfunc', cloudpickle.dumps(qfunc))
cls.r.set('aqpol', cloudpickle.dumps(aqpol))
cls.r.set('vfunc', cloudpickle.dumps(vfunc))
c2d = C2DEnv(env)
pol_net = PolNet(c2d.observation_space, c2d.action_space)
mcpol = MultiCategoricalPol(
env.observation_space, env.action_space, pol_net)
cls.r.set('mcpol', cloudpickle.dumps(mcpol))
def test_distributed_epi_sampler(self):
proc_redis = subprocess.Popen(['redis-server'])
proc_slave = subprocess.Popen([
'python', '-m', 'machina.samplers.distributed_epi_sampler',
'--world_size', '1', '--rank', '0', '--redis_host', 'localhost',
'--redis_port', '6379'
])
make_redis('localhost', '6379')
sampler = DistributedEpiSampler(1,
-1,
self.env,
self.pol,
num_parallel=1)
epis = sampler.sample(self.pol, max_epis=2)
assert len(epis) >= 2
children = psutil.Process(os.getpid()).children(recursive=True)
for child in children:
child.send_signal(SIGTERM)
"""
This method should be called in master node.
"""
self.pol = pol
self.max_epis = max_epis // self.world_size if max_epis is not None else None
self.max_steps = max_steps // self.world_size if max_steps is not None else None
self.deterministic = deterministic
self.scatter_from_master('pol')
self.scatter_from_master('max_epis')
self.scatter_from_master('max_steps')
self.scatter_from_master('deterministic')
self.gather_to_master('epis')
return self.epis
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--world_size', type=int)
parser.add_argument('--rank', type=int)
parser.add_argument('--redis_host', type=str, default='localhost')
parser.add_argument('--redis_port', type=str, default='6379')
args = parser.parse_args()
make_redis(args.redis_host, args.redis_port)
sampler = DistributedEpiSampler(
args.world_size, args.rank)