def test_episode(data_size, observation_size, action_size, gamma):
observations = np.random.random((data_size, observation_size))
actions = np.random.random((data_size, action_size))
rewards = np.random.random((data_size, 1))
episode = Episode((observation_size, ), action_size, observations, actions,
rewards, gamma)
# check Episode methods
assert np.all(episode.observations == observations)
assert np.all(episode.actions == actions)
assert np.all(episode.rewards == rewards)
assert episode.size() == data_size - 1
assert episode.get_observation_shape() == (observation_size, )
assert episode.get_action_size() == action_size
assert episode.compute_return() == np.sum(rewards[1:])
# check transitions exported from episode
assert len(episode.transitions) == data_size - 1
for i, t in enumerate(episode.transitions):
assert isinstance(t, Transition)
assert t.observation_shape == (observation_size, )
assert t.action_size == action_size
assert np.all(t.observation == observations[i])
assert np.all(t.action == actions[i])
assert t.reward == rewards[i]
assert np.all(t.next_observation == observations[i + 1])
assert np.all(t.next_action == actions[i + 1])
assert t.next_reward == rewards[i + 1]
assert t.terminal == (1.0 if (i == data_size - 2) else 0.0)
assert len(t.returns) == data_size - i - 1
assert len(t.consequent_observations) == data_size - i - 1
# check returns
ref_return = 0.0
for j, ret in enumerate(t.returns):
print(t.returns)
ref_return += (gamma**j) * rewards[i + 1 + j][0]
assert ret == ref_return
# check list-like bahaviors
assert len(episode) == data_size - 1
assert episode[0] is episode.transitions[0]
for i, transition in enumerate(episode):
assert isinstance(transition, Transition)
assert transition is episode.transitions[i]
def test_episode(data_size, observation_size, action_size, create_mask,
mask_size):
observations = np.random.random((data_size, observation_size)).astype("f4")
actions = np.random.random((data_size, action_size)).astype("f4")
rewards = np.random.random(data_size).astype("f4")
episode = Episode(
observation_shape=(observation_size, ),
action_size=action_size,
observations=observations,
actions=actions,
rewards=rewards,
create_mask=create_mask,
mask_size=mask_size,
)
# check Episode methods
assert np.all(episode.observations == observations)
assert np.all(episode.actions == actions)
assert np.all(episode.rewards == rewards)
assert episode.size() == data_size - 1
assert episode.get_observation_shape() == (observation_size, )
assert episode.get_action_size() == action_size
assert episode.compute_return() == np.sum(rewards[1:])
# check transitions exported from episode
assert len(episode.transitions) == data_size - 1
for i, t in enumerate(episode.transitions):
assert isinstance(t, Transition)
assert t.get_observation_shape() == (observation_size, )
assert t.get_action_size() == action_size
assert np.all(t.observation == observations[i])
assert np.all(t.action == actions[i])
assert np.allclose(t.reward, rewards[i])
assert np.all(t.next_observation == observations[i + 1])
assert np.all(t.next_action == actions[i + 1])
assert np.allclose(t.next_reward, rewards[i + 1])
assert t.terminal == (1.0 if (i == data_size - 2) else 0.0)
# check forward pointers
count = 1
transition = episode[0]
while transition.next_transition:
transition = transition.next_transition
count += 1
assert count == data_size - 1
# check backward pointers
count = 1
transition = episode[-1]
while transition.prev_transition:
transition = transition.prev_transition
count += 1
assert count == data_size - 1
# check list-like bahaviors
assert len(episode) == data_size - 1
assert episode[0] is episode.transitions[0]
for i, transition in enumerate(episode):
assert isinstance(transition, Transition)
assert transition is episode.transitions[i]
if create_mask:
assert transition.mask.shape == (mask_size, )
else:
assert transition.mask is None