class TestScriptedPolicy:
def setup_method(self):
self.sp = ScriptedPolicy(scripted_actions=[1], agent_env_infos={0: 1})
"""
potentially add more tests down the line
"""
def test_pass_codecov(self):
self.sp.get_action(0)
self.sp.get_actions([0])
def setup_method(self):
self.env = TfEnv(GridWorldEnv(desc='4x4'))
self.policy = ScriptedPolicy(
scripted_actions=[2, 2, 1, 0, 3, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1])
self.algo = Mock(env_spec=self.env.spec,
policy=self.policy,
max_path_length=16)
def setup_method(self):
ray.init(local_mode=True, ignore_reinit_error=True)
self.env = TfEnv(GridWorldEnv(desc='4x4'))
self.policy = ScriptedPolicy(
scripted_actions=[2, 2, 1, 0, 3, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1])
self.algo = Mock(
env_spec=self.env.spec, policy=self.policy, max_path_length=16)
def test_obtain_samples(ray_local_session_fixture):
del ray_local_session_fixture
env = GarageEnv(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,
n_workers=8)
sampler1 = RaySampler.from_worker_factory(workers, policy, env)
sampler2 = LocalSampler.from_worker_factory(workers, policy, env)
trajs1 = sampler1.obtain_samples(0, 1000,
tuple(algo.policy.get_param_values()))
trajs2 = sampler2.obtain_samples(0, 1000,
tuple(algo.policy.get_param_values()))
assert trajs1.observations.shape[0] >= 1000
assert trajs1.actions.shape[0] >= 1000
assert (sum(trajs1.rewards[:trajs1.lengths[0]]) == sum(
trajs2.rewards[:trajs2.lengths[0]]) == 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_obtain_samples():
env = GarageEnv(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_episode_length=16)
workers = WorkerFactory(seed=100,
max_episode_length=algo.max_episode_length,
n_workers=8)
sampler1 = MultiprocessingSampler.from_worker_factory(workers, policy, env)
sampler2 = LocalSampler.from_worker_factory(workers, policy, env)
trajs1 = sampler1.obtain_samples(0, 1000,
tuple(algo.policy.get_param_values()))
trajs2 = sampler2.obtain_samples(0, 1000,
tuple(algo.policy.get_param_values()))
# 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.rewards[:trajs2.lengths[0]]) == 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 policy():
return ScriptedPolicy(
scripted_actions=[2, 2, 1, 0, 3, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1])
def setup_method(self):
self.sp = ScriptedPolicy(scripted_actions=[1], agent_env_infos={0: 1})
