def test_ray_batch_sampler(self):
sampler1 = RaySampler(self.algo,
self.env,
seed=100,
num_processors=1,
sampler_worker_cls=SamplerWorker)
sampler1.start_worker()
sampler2 = OnPolicyVectorizedSampler(self.algo, self.env)
sampler2.start_worker()
trajs1 = sampler1.obtain_samples(0, 16)
trajs2 = sampler2.obtain_samples(0, 1)
assert (trajs1[0]['observations'].shape == np.array(
trajs2[0]['observations']).shape == (6, 16))
traj2_action_shape = np.array(trajs2[0]['actions']).shape
assert (trajs1[0]['actions'].shape == traj2_action_shape == (6, 4))
assert (sum(trajs1[0]['rewards']) == sum(trajs2[0]['rewards']) == 1)
true_obs = np.array(
[[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.]])
true_actions = np.array([[0., 0., 1., 0.], [0., 0., 1., 0.],
[0., 1., 0., 0.], [0., 1., 0., 0.],
[0., 1., 0., 0.], [0., 0., 1., 0.]])
true_rewards = np.array([0, 0, 0, 0, 0, 1])
for trajectory in trajs1:
assert (np.array_equal(trajectory['observations'], true_obs))
assert (np.array_equal(trajectory['actions'], true_actions))
assert (np.array_equal(trajectory['rewards'], true_rewards))
sampler1.shutdown_worker()
sampler2.shutdown_worker()
def test_ray_batch_sampler(self):
workers = WorkerFactory(seed=100,
max_path_length=self.algo.max_path_length)
sampler1 = RaySampler(workers, self.policy, self.env, num_processors=1)
sampler1.start_worker()
sampler2 = OnPolicyVectorizedSampler(self.algo, self.env)
sampler2.start_worker()
trajs1 = sampler1.obtain_samples(
0, 1000, tuple(self.algo.policy.get_param_values()))
trajs2 = sampler2.obtain_samples(0, 1000)
# pylint: disable=superfluous-parens
assert (trajs1[0]['observations'].shape == np.array(
trajs2[0]['observations']).shape == (6, ))
traj2_action_shape = np.array(trajs2[0]['actions']).shape
assert trajs1[0]['actions'].shape == traj2_action_shape == (6, )
assert sum(trajs1[0]['rewards']) == sum(trajs2[0]['rewards']) == 1
true_obs = np.array([0, 1, 2, 6, 10, 14])
true_actions = np.array([2, 2, 1, 1, 1, 2])
true_rewards = np.array([0, 0, 0, 0, 0, 1])
for trajectory in trajs1:
assert np.array_equal(trajectory['observations'], true_obs)
assert np.array_equal(trajectory['actions'], true_actions)
assert np.array_equal(trajectory['rewards'], true_rewards)
sampler1.shutdown_worker()
sampler2.shutdown_worker()
def test_ray_batch_sampler(self):
workers = WorkerFactory(seed=100,
max_path_length=self.algo.max_path_length)
sampler1 = RaySampler(workers, self.policy, self.env)
sampler1.start_worker()
sampler2 = OnPolicyVectorizedSampler(self.algo, self.env)
sampler2.start_worker()
trajs1 = sampler1.obtain_samples(
0, 1000, tuple(self.algo.policy.get_param_values()))
trajs2 = sampler2.obtain_samples(0, 1000)
# pylint: disable=superfluous-parens
assert trajs1.observations.shape[0] >= 1000
assert trajs1.actions.shape[0] >= 1000
assert (sum(trajs1.rewards[:trajs1.lengths[0]]) == sum(
trajs2[0]['rewards']) == 1)
true_obs = np.array([0, 1, 2, 6, 10, 14])
true_actions = np.array([2, 2, 1, 1, 1, 2])
true_rewards = np.array([0, 0, 0, 0, 0, 1])
start = 0
for length in trajs1.lengths:
observations = trajs1.observations[start:start + length]
actions = trajs1.actions[start:start + length]
rewards = trajs1.rewards[start:start + length]
assert np.array_equal(observations, true_obs)
assert np.array_equal(actions, true_actions)
assert np.array_equal(rewards, true_rewards)
start += length
sampler1.shutdown_worker()
sampler2.shutdown_worker()
def test_ray_batch_sampler(self, ray_local_session_fixture):
del ray_local_session_fixture
assert ray.is_initialized()
workers = WorkerFactory(
seed=100, max_episode_length=self.algo.max_episode_length)
sampler1 = RaySampler(workers, self.policy, self.env)
sampler1.start_worker()
sampler1.shutdown_worker()
def test_ray_batch_sampler(ray_local_session_fixture):
del ray_local_session_fixture
env = TfEnv(GridWorldEnv(desc='4x4'))
policy = ScriptedPolicy(
scripted_actions=[2, 2, 1, 0, 3, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1])
algo = Mock(env_spec=env.spec, policy=policy, max_path_length=16)
assert ray.is_initialized()
workers = WorkerFactory(seed=100, max_path_length=algo.max_path_length)
sampler1 = RaySampler(workers, policy, env)
sampler1.start_worker()
sampler2 = OnPolicyVectorizedSampler(algo, env)
sampler2.start_worker()
trajs1 = sampler1.obtain_samples(0, 1000,
tuple(algo.policy.get_param_values()))
trajs2 = sampler2.obtain_samples(0, 1000)
# pylint: disable=superfluous-parens
assert trajs1.observations.shape[0] >= 1000
assert trajs1.actions.shape[0] >= 1000
assert (sum(trajs1.rewards[:trajs1.lengths[0]]) == sum(
trajs2[0]['rewards']) == 1)
true_obs = np.array([0, 1, 2, 6, 10, 14])
true_actions = np.array([2, 2, 1, 1, 1, 2])
true_rewards = np.array([0, 0, 0, 0, 0, 1])
start = 0
for length in trajs1.lengths:
observations = trajs1.observations[start:start + length]
actions = trajs1.actions[start:start + length]
rewards = trajs1.rewards[start:start + length]
assert np.array_equal(observations, true_obs)
assert np.array_equal(actions, true_actions)
assert np.array_equal(rewards, true_rewards)
start += length
sampler1.shutdown_worker()
sampler2.shutdown_worker()
env.close()
def test_ray_batch_sampler(self):
workers = WorkerFactory(seed=100,
max_path_length=self.algo.max_path_length)
sampler1 = RaySampler(workers, self.policy, self.env)
sampler1.start_worker()
sampler1.shutdown_worker()
