def test_example(self, discrete, dim_state, dim_action, kind):
if discrete:
num_states, num_actions = dim_state, dim_action
dim_state, dim_action = (), ()
else:
num_states, num_actions = -1, -1
dim_state, dim_action = (dim_state,), (dim_action,)
if kind == "nan":
o = Observation.nan_example(
dim_state=dim_state,
dim_action=dim_action,
num_states=num_states,
num_actions=num_actions,
)
elif kind == "zero":
o = Observation.zero_example(
dim_state=dim_state,
dim_action=dim_action,
num_states=num_states,
num_actions=num_actions,
)
elif kind == "random":
o = Observation.random_example(
dim_state=dim_state,
dim_action=dim_action,
num_states=num_states,
num_actions=num_actions,
)
else:
with pytest.raises(ValueError):
Observation.get_example(
dim_state=dim_state,
dim_action=dim_action,
num_states=num_states,
num_actions=num_actions,
kind=kind,
)
return
if discrete:
torch.testing.assert_allclose(o.state.shape, torch.Size([]))
torch.testing.assert_allclose(o.action.shape, torch.Size([]))
torch.testing.assert_allclose(o.next_state.shape, torch.Size([]))
torch.testing.assert_allclose(o.log_prob_action, torch.tensor(1.0))
else:
torch.testing.assert_allclose(o.state.shape, torch.Size(dim_state))
torch.testing.assert_allclose(o.action.shape, torch.Size(dim_action))
torch.testing.assert_allclose(o.next_state.shape, torch.Size(dim_state))
torch.testing.assert_allclose(o.log_prob_action, torch.tensor(1.0))
def create_er_from_transitions(discrete, dim_state, dim_action, max_len,
num_steps, num_transitions):
"""Create a memory with `num_transitions' transitions."""
if discrete:
num_states, num_actions = dim_state, dim_action
dim_state, dim_action = (), ()
else:
num_states, num_actions = -1, -1
dim_state, dim_action = (dim_state, ), (dim_action, )
memory = ExperienceReplay(max_len, num_steps=num_steps)
for _ in range(num_transitions):
observation = Observation.random_example(
dim_state=dim_state,
dim_action=dim_action,
num_states=num_states,
num_actions=num_actions,
)
memory.append(observation)
return memory
def test_clone(self, discrete, dim_state, dim_action):
if discrete:
num_states, num_actions = dim_state, dim_action
dim_state, dim_action = (), ()
else:
num_states, num_actions = -1, -1
dim_state, dim_action = (dim_state,), (dim_action,)
o = Observation.random_example(
dim_state=dim_state,
dim_action=dim_action,
num_states=num_states,
num_actions=num_actions,
)
o1 = o.clone()
assert o is not o1
assert o == o1
for x, x1 in zip(o, o1):
assert Observation._is_equal_nan(x, x1)
assert x is not x1
def test_append(self, discrete, dim_state, dim_action, max_len, num_steps):
num_transitions = 200
memory = create_er_from_transitions(discrete, dim_state, dim_action,
max_len, num_steps,
num_transitions)
if discrete:
num_states, num_actions = dim_state, dim_action
dim_state, dim_action = (), ()
else:
num_states, num_actions = -1, -1
dim_state, dim_action = (dim_state, ), (dim_action, )
observation = Observation.random_example(
dim_state=dim_state,
dim_action=dim_action,
num_states=num_states,
num_actions=num_actions,
)
memory.append(observation)
assert memory.valid[(memory.ptr - 1) % max_len] == 1
assert memory.valid[(memory.ptr - 2) % max_len] == 1
for i in range(num_steps):
assert memory.valid[(memory.ptr + i) % max_len] == 0
assert memory.memory[(memory.ptr - 1) % max_len] is not observation