def test_batch():
batch = Batch(obs=[0], np=np.zeros([3, 4]))
assert batch.obs == batch["obs"]
batch.obs = [1]
assert batch.obs == [1]
batch.cat_(batch)
assert batch.obs == [1, 1]
assert batch.np.shape == (6, 4)
assert batch[0].obs == batch[1].obs
batch.obs = np.arange(5)
for i, b in enumerate(batch.split(1, shuffle=False)):
if i != 5:
assert b.obs == batch[i].obs
else:
with pytest.raises(AttributeError):
batch[i].obs
with pytest.raises(AttributeError):
b.obs
print(batch)
batch_dict = {'b': np.array([1.0]), 'c': 2.0, 'd': torch.Tensor([3.0])}
batch_item = Batch({'a': [batch_dict]})[0]
assert isinstance(batch_item.a.b, np.ndarray)
assert batch_item.a.b == batch_dict['b']
assert isinstance(batch_item.a.c, float)
assert batch_item.a.c == batch_dict['c']
assert isinstance(batch_item.a.d, torch.Tensor)
assert batch_item.a.d == batch_dict['d']
batch2 = Batch(a=[{
'b': np.float64(1.0),
'c': np.zeros(1),
'd': Batch(e=np.array(3.0))}])
assert len(batch2) == 1
with pytest.raises(IndexError):
batch2[-2]
with pytest.raises(IndexError):
batch2[1]
with pytest.raises(TypeError):
batch2[0][0]
assert isinstance(batch2[0].a.c, np.ndarray)
assert isinstance(batch2[0].a.b, np.float64)
assert isinstance(batch2[0].a.d.e, np.float64)
batch2_from_list = Batch(list(batch2))
batch2_from_comp = Batch([e for e in batch2])
assert batch2_from_list.a.b == batch2.a.b
assert batch2_from_list.a.c == batch2.a.c
assert batch2_from_list.a.d.e == batch2.a.d.e
assert batch2_from_comp.a.b == batch2.a.b
assert batch2_from_comp.a.c == batch2.a.c
assert batch2_from_comp.a.d.e == batch2.a.d.e
for batch_slice in [
batch2[slice(0, 1)], batch2[:1], batch2[0:]]:
assert batch_slice.a.b == batch2.a.b
assert batch_slice.a.c == batch2.a.c
assert batch_slice.a.d.e == batch2.a.d.e
batch2_sum = (batch2 + 1.0) * 2
assert batch2_sum.a.b == (batch2.a.b + 1.0) * 2
assert batch2_sum.a.c == (batch2.a.c + 1.0) * 2
assert batch2_sum.a.d.e == (batch2.a.d.e + 1.0) * 2
def test_batch():
batch = Batch(obs=[0], np=np.zeros([3, 4]))
batch.obs = [1]
assert batch.obs == [1]
batch.append(batch)
assert batch.obs == [1, 1]
assert batch.np.shape == (6, 4)
assert batch[0].obs == batch[1].obs
with pytest.raises(IndexError):
batch[2]
batch.obs = np.arange(5)
for i, b in enumerate(batch.split(1, permute=False)):
assert b.obs == batch[i].obs
def test_batch():
batch = Batch(obs=[0], np=np.zeros([3, 4]))
assert batch.obs == batch["obs"]
batch.obs = [1]
assert batch.obs == [1]
batch.append(batch)
assert batch.obs == [1, 1]
assert batch.np.shape == (6, 4)
assert batch[0].obs == batch[1].obs
batch.obs = np.arange(5)
for i, b in enumerate(batch.split(1, shuffle=False)):
if i != 5:
assert b.obs == batch[i].obs
else:
with pytest.raises(AttributeError):
batch[i].obs
with pytest.raises(AttributeError):
b.obs
print(batch)
def add(
self,
batch: Batch,
buffer_ids: Optional[Union[np.ndarray, List[int]]] = None
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Add a batch of data into replay buffer.
:param Batch batch: the input data batch. Its keys must belong to the 7
reserved keys, and "obs", "act", "rew", "done" is required.
:param buffer_ids: to make consistent with other buffer's add function; if it
is not None, we assume the input batch's first dimension is always 1.
Return (current_index, episode_reward, episode_length, episode_start_index). If
the episode is not finished, the return value of episode_length and
episode_reward is 0.
"""
# preprocess batch
b = Batch()
for key in set(self._reserved_keys).intersection(batch.keys()):
b.__dict__[key] = batch[key]
batch = b
assert set(["obs", "act", "rew", "done"]).issubset(batch.keys())
stacked_batch = buffer_ids is not None
if stacked_batch:
assert len(batch) == 1
if self._save_only_last_obs:
batch.obs = batch.obs[:, -1] if stacked_batch else batch.obs[-1]
if not self._save_obs_next:
batch.pop("obs_next", None)
elif self._save_only_last_obs:
batch.obs_next = (
batch.obs_next[:, -1] if stacked_batch else batch.obs_next[-1]
)
# get ptr
if stacked_batch:
rew, done = batch.rew[0], batch.done[0]
else:
rew, done = batch.rew, batch.done
ptr, ep_rew, ep_len, ep_idx = list(
map(lambda x: np.array([x]), self._add_index(rew, done))
)
try:
self._meta[ptr] = batch
except ValueError:
stack = not stacked_batch
batch.rew = batch.rew.astype(float)
batch.done = batch.done.astype(bool)
if self._meta.is_empty():
self._meta = _create_value( # type: ignore
batch, self.maxsize, stack)
else: # dynamic key pops up in batch
_alloc_by_keys_diff(self._meta, batch, self.maxsize, stack)
self._meta[ptr] = batch
return ptr, ep_rew, ep_len, ep_idx
def add(
self,
batch: Batch,
buffer_ids: Optional[Union[np.ndarray, List[int]]] = None
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Add a batch of data into ReplayBufferManager.
Each of the data's length (first dimension) must equal to the length of
buffer_ids. By default buffer_ids is [0, 1, ..., buffer_num - 1].
Return (current_index, episode_reward, episode_length, episode_start_index). If
the episode is not finished, the return value of episode_length and
episode_reward is 0.
"""
# preprocess batch
new_batch = Batch()
for key in set(self._reserved_keys).intersection(batch.keys()):
new_batch.__dict__[key] = batch[key]
batch = new_batch
assert set(["obs", "act", "rew", "done"]).issubset(batch.keys())
if self._save_only_last_obs:
batch.obs = batch.obs[:, -1]
if not self._save_obs_next:
batch.pop("obs_next", None)
elif self._save_only_last_obs:
batch.obs_next = batch.obs_next[:, -1]
# get index
if buffer_ids is None:
buffer_ids = np.arange(self.buffer_num)
ptrs, ep_lens, ep_rews, ep_idxs = [], [], [], []
for batch_idx, buffer_id in enumerate(buffer_ids):
ptr, ep_rew, ep_len, ep_idx = self.buffers[buffer_id]._add_index(
batch.rew[batch_idx], batch.done[batch_idx]
)
ptrs.append(ptr + self._offset[buffer_id])
ep_lens.append(ep_len)
ep_rews.append(ep_rew)
ep_idxs.append(ep_idx + self._offset[buffer_id])
self.last_index[buffer_id] = ptr + self._offset[buffer_id]
self._lengths[buffer_id] = len(self.buffers[buffer_id])
ptrs = np.array(ptrs)
try:
self._meta[ptrs] = batch
except ValueError:
batch.rew = batch.rew.astype(float)
batch.done = batch.done.astype(bool)
if self._meta.is_empty():
self._meta = _create_value( # type: ignore
batch, self.maxsize, stack=False)
else: # dynamic key pops up in batch
_alloc_by_keys_diff(self._meta, batch, self.maxsize, False)
self._set_batch_for_children()
self._meta[ptrs] = batch
return ptrs, np.array(ep_rews), np.array(ep_lens), np.array(ep_idxs)
def test_batch():
assert list(Batch()) == []
assert Batch().is_empty()
assert not Batch(b={'c': {}}).is_empty()
assert Batch(b={'c': {}}).is_empty(recurse=True)
assert not Batch(a=Batch(), b=Batch(c=Batch())).is_empty()
assert Batch(a=Batch(), b=Batch(c=Batch())).is_empty(recurse=True)
assert not Batch(d=1).is_empty()
assert not Batch(a=np.float64(1.0)).is_empty()
assert len(Batch(a=[1, 2, 3], b={'c': {}})) == 3
assert not Batch(a=[1, 2, 3]).is_empty()
b = Batch({'a': [4, 4], 'b': [5, 5]}, c=[None, None])
assert b.c.dtype == object
b = Batch(d=[None], e=[starmap], f=Batch)
assert b.d.dtype == b.e.dtype == object and b.f == Batch
b = Batch()
b.update()
assert b.is_empty()
b.update(c=[3, 5])
assert np.allclose(b.c, [3, 5])
# mimic the behavior of dict.update, where kwargs can overwrite keys
b.update({'a': 2}, a=3)
assert 'a' in b and b.a == 3
assert b.pop('a') == 3
assert 'a' not in b
with pytest.raises(AssertionError):
Batch({1: 2})
assert Batch(a=[np.zeros((2, 3)), np.zeros((3, 3))]).a.dtype == object
with pytest.raises(TypeError):
Batch(a=[np.zeros((3, 2)), np.zeros((3, 3))])
with pytest.raises(TypeError):
Batch(a=[torch.zeros((2, 3)), torch.zeros((3, 3))])
with pytest.raises(TypeError):
Batch(a=[torch.zeros((3, 3)), np.zeros((3, 3))])
with pytest.raises(TypeError):
Batch(a=[1, np.zeros((3, 3)), torch.zeros((3, 3))])
batch = Batch(a=[torch.ones(3), torch.ones(3)])
assert torch.allclose(batch.a, torch.ones(2, 3))
batch.cat_(batch)
assert torch.allclose(batch.a, torch.ones(4, 3))
Batch(a=[])
batch = Batch(obs=[0], np=np.zeros([3, 4]))
assert batch.obs == batch["obs"]
batch.obs = [1]
assert batch.obs == [1]
batch.cat_(batch)
assert np.allclose(batch.obs, [1, 1])
assert batch.np.shape == (6, 4)
assert np.allclose(batch[0].obs, batch[1].obs)
batch.obs = np.arange(5)
for i, b in enumerate(batch.split(1, shuffle=False)):
if i != 5:
assert b.obs == batch[i].obs
else:
with pytest.raises(AttributeError):
batch[i].obs
with pytest.raises(AttributeError):
b.obs
print(batch)
batch = Batch(a=np.arange(10))
with pytest.raises(AssertionError):
list(batch.split(0))
data = [
(1, False, [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]]),
(1, True, [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]]),
(3, False, [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]),
(3, True, [[0, 1, 2], [3, 4, 5], [6, 7, 8, 9]]),
(5, False, [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]),
(5, True, [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]),
(7, False, [[0, 1, 2, 3, 4, 5, 6], [7, 8, 9]]),
(7, True, [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]),
(10, False, [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]),
(10, True, [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]),
(15, False, [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]),
(15, True, [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]),
(100, False, [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]),
(100, True, [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]),
]
for size, merge_last, result in data:
bs = list(batch.split(size, shuffle=False, merge_last=merge_last))
assert [bs[i].a.tolist() for i in range(len(bs))] == result
batch_dict = {'b': np.array([1.0]), 'c': 2.0, 'd': torch.Tensor([3.0])}
batch_item = Batch({'a': [batch_dict]})[0]
assert isinstance(batch_item.a.b, np.ndarray)
assert batch_item.a.b == batch_dict['b']
assert isinstance(batch_item.a.c, float)
assert batch_item.a.c == batch_dict['c']
assert isinstance(batch_item.a.d, torch.Tensor)
assert batch_item.a.d == batch_dict['d']
batch2 = Batch(a=[{
'b': np.float64(1.0),
'c': np.zeros(1),
'd': Batch(e=np.array(3.0))}])
assert len(batch2) == 1
assert Batch().shape == []
assert Batch(a=1).shape == []
assert Batch(a=set((1, 2, 1))).shape == []
assert batch2.shape[0] == 1
assert 'a' in batch2 and all([i in batch2.a for i in 'bcd'])
with pytest.raises(IndexError):
batch2[-2]
with pytest.raises(IndexError):
batch2[1]
assert batch2[0].shape == []
with pytest.raises(IndexError):
batch2[0][0]
with pytest.raises(TypeError):
len(batch2[0])
assert isinstance(batch2[0].a.c, np.ndarray)
assert isinstance(batch2[0].a.b, np.float64)
assert isinstance(batch2[0].a.d.e, np.float64)
batch2_from_list = Batch(list(batch2))
batch2_from_comp = Batch([e for e in batch2])
assert batch2_from_list.a.b == batch2.a.b
assert batch2_from_list.a.c == batch2.a.c
assert batch2_from_list.a.d.e == batch2.a.d.e
assert batch2_from_comp.a.b == batch2.a.b
assert batch2_from_comp.a.c == batch2.a.c
assert batch2_from_comp.a.d.e == batch2.a.d.e
for batch_slice in [batch2[slice(0, 1)], batch2[:1], batch2[0:]]:
assert batch_slice.a.b == batch2.a.b
assert batch_slice.a.c == batch2.a.c
assert batch_slice.a.d.e == batch2.a.d.e
batch2.a.d.f = {}
batch2_sum = (batch2 + 1.0) * 2
assert batch2_sum.a.b == (batch2.a.b + 1.0) * 2
assert batch2_sum.a.c == (batch2.a.c + 1.0) * 2
assert batch2_sum.a.d.e == (batch2.a.d.e + 1.0) * 2
assert batch2_sum.a.d.f.is_empty()
with pytest.raises(TypeError):
batch2 += [1]
batch3 = Batch(a={
'c': np.zeros(1),
'd': Batch(e=np.array([0.0]), f=np.array([3.0]))})
batch3.a.d[0] = {'e': 4.0}
assert batch3.a.d.e[0] == 4.0
batch3.a.d[0] = Batch(f=5.0)
assert batch3.a.d.f[0] == 5.0
with pytest.raises(ValueError):
batch3.a.d[0] = Batch(f=5.0, g=0.0)
with pytest.raises(ValueError):
batch3[0] = Batch(a={"c": 2, "e": 1})
# auto convert
batch4 = Batch(a=np.array(['a', 'b']))
assert batch4.a.dtype == object # auto convert to object
batch4.update(a=np.array(['c', 'd']))
assert list(batch4.a) == ['c', 'd']
assert batch4.a.dtype == object # auto convert to object
batch5 = Batch(a=np.array([{'index': 0}]))
assert isinstance(batch5.a, Batch)
assert np.allclose(batch5.a.index, [0])
batch5.b = np.array([{'index': 1}])
assert isinstance(batch5.b, Batch)
assert np.allclose(batch5.b.index, [1])
# None is a valid object and can be stored in Batch
a = Batch.stack([Batch(a=None), Batch(b=None)])
assert a.a[0] is None and a.a[1] is None
assert a.b[0] is None and a.b[1] is None
# nx.Graph corner case
assert Batch(a=np.array([nx.Graph(), nx.Graph()], dtype=object)).a.dtype == object
g1 = nx.Graph()
g1.add_nodes_from(list(range(10)))
g2 = nx.Graph()
g2.add_nodes_from(list(range(20)))
assert Batch(a=np.array([g1, g2])).a.dtype == object
def adversarial_policy(self, batch):
"""Find an adversarial policy (return the standard policy if can't find better policies).
:return: list of ints, an array containing the adversarial actions"""
batch = copy.deepcopy(batch)
batch.obs = np.squeeze(batch.obs, axis=0)
action_shape = self.env.action_space.shape or self.env.action_space.n
action_shape = np.prod(action_shape)
if self.acts_mask:
actions = [
a for a in range(int(action_shape)) if a in self.acts_mask
]
else:
actions = [a for a in range(int(action_shape))]
atk_strategies = [
p for p in itertools.product(actions,
repeat=self.n // self.repeat_adv_act)
] # define attack strategies
atk_strategies = np.repeat(atk_strategies,
self.repeat_adv_act,
axis=-1)
if isinstance(self.env, gym.wrappers.Monitor):
env = copy.deepcopy(self.env.env) # copy the environment
else:
env = copy.deepcopy(self.env)
self.store_env_state(self.env) # store the state of the environment
env = self.load_env_state(env) # restore the state of the environment
adv_acts = [] # actions of the best adversarial policy
# test standard policy
std_rew = 0 # cumulative reward
attack = False
for i in range(self.m):
with torch.no_grad():
batch.obs = np.expand_dims(batch.obs, axis=0)
result = self.policy(batch, None)
act = to_numpy(result.act)
obs_next, rew, done, info = env.step(act[0])
std_rew += rew
adv_acts.append(act[0])
if done:
break
batch = Batch(state={},
obs=obs_next,
act={},
rew={},
done={},
info={},
obs_next={},
policy={})
lowest_rew = std_rew # best adversarial reward
# test adversarial policies
for atk in atk_strategies:
self.store_env_state(
self.env) # store the state of the environment
env = self.load_env_state(
env) # restore the state of the environment
acts = list(atk)
atk_rew = 0
for act in acts: # play n steps according to adversarial policy
obs_next, rew, done, info = env.step(act)
atk_rew += rew
if done:
break
if self.m > self.n and not done: # play n-m steps according to standard policy
batch = Batch(state={},
obs=obs_next,
act={},
rew={},
done={},
info={},
obs_next={},
policy={})
for i in range(self.m - self.n):
with torch.no_grad():
batch.obs = np.expand_dims(batch.obs, axis=0)
result = self.policy(batch, None)
act = to_numpy(result.act)
obs_next, rew, done, info = env.step(act[0])
atk_rew += rew
if done:
break
batch = Batch(state={},
obs=obs_next,
act={},
rew={},
done={},
info={},
obs_next={},
policy={})
if abs(atk_rew - std_rew) > self.delta and atk_rew < lowest_rew:
lowest_rew = atk_rew
adv_acts = acts
attack = True
if not self.full_search:
return adv_acts, attack
# print(std_rew, lowest_rew)
return adv_acts, attack
def test_batch():
assert list(Batch()) == []
assert Batch().is_empty()
assert not Batch(b={'c': {}}).is_empty()
assert Batch(b={'c': {}}).is_empty(recurse=True)
assert not Batch(a=Batch(), b=Batch(c=Batch())).is_empty()
assert Batch(a=Batch(), b=Batch(c=Batch())).is_empty(recurse=True)
assert not Batch(d=1).is_empty()
assert not Batch(a=np.float64(1.0)).is_empty()
assert len(Batch(a=[1, 2, 3], b={'c': {}})) == 3
assert not Batch(a=[1, 2, 3]).is_empty()
b = Batch()
b.update()
assert b.is_empty()
b.update(c=[3, 5])
assert np.allclose(b.c, [3, 5])
# mimic the behavior of dict.update, where kwargs can overwrite keys
b.update({'a': 2}, a=3)
assert b.a == 3
with pytest.raises(AssertionError):
Batch({1: 2})
with pytest.raises(TypeError):
Batch(a=[np.zeros((2, 3)), np.zeros((3, 3))])
with pytest.raises(TypeError):
Batch(a=[np.zeros((3, 2)), np.zeros((3, 3))])
with pytest.raises(TypeError):
Batch(a=[torch.zeros((2, 3)), torch.zeros((3, 3))])
with pytest.raises(TypeError):
Batch(a=[torch.zeros((3, 3)), np.zeros((3, 3))])
with pytest.raises(TypeError):
Batch(a=[1, np.zeros((3, 3)), torch.zeros((3, 3))])
batch = Batch(a=[torch.ones(3), torch.ones(3)])
assert torch.allclose(batch.a, torch.ones(2, 3))
Batch(a=[])
batch = Batch(obs=[0], np=np.zeros([3, 4]))
assert batch.obs == batch["obs"]
batch.obs = [1]
assert batch.obs == [1]
batch.cat_(batch)
assert np.allclose(batch.obs, [1, 1])
assert batch.np.shape == (6, 4)
assert np.allclose(batch[0].obs, batch[1].obs)
batch.obs = np.arange(5)
for i, b in enumerate(batch.split(1, shuffle=False)):
if i != 5:
assert b.obs == batch[i].obs
else:
with pytest.raises(AttributeError):
batch[i].obs
with pytest.raises(AttributeError):
b.obs
print(batch)
batch_dict = {'b': np.array([1.0]), 'c': 2.0, 'd': torch.Tensor([3.0])}
batch_item = Batch({'a': [batch_dict]})[0]
assert isinstance(batch_item.a.b, np.ndarray)
assert batch_item.a.b == batch_dict['b']
assert isinstance(batch_item.a.c, float)
assert batch_item.a.c == batch_dict['c']
assert isinstance(batch_item.a.d, torch.Tensor)
assert batch_item.a.d == batch_dict['d']
batch2 = Batch(a=[{
'b': np.float64(1.0),
'c': np.zeros(1),
'd': Batch(e=np.array(3.0))
}])
assert len(batch2) == 1
assert Batch().shape == []
assert Batch(a=1).shape == []
assert batch2.shape[0] == 1
with pytest.raises(IndexError):
batch2[-2]
with pytest.raises(IndexError):
batch2[1]
assert batch2[0].shape == []
with pytest.raises(IndexError):
batch2[0][0]
with pytest.raises(TypeError):
len(batch2[0])
assert isinstance(batch2[0].a.c, np.ndarray)
assert isinstance(batch2[0].a.b, np.float64)
assert isinstance(batch2[0].a.d.e, np.float64)
batch2_from_list = Batch(list(batch2))
batch2_from_comp = Batch([e for e in batch2])
assert batch2_from_list.a.b == batch2.a.b
assert batch2_from_list.a.c == batch2.a.c
assert batch2_from_list.a.d.e == batch2.a.d.e
assert batch2_from_comp.a.b == batch2.a.b
assert batch2_from_comp.a.c == batch2.a.c
assert batch2_from_comp.a.d.e == batch2.a.d.e
for batch_slice in [batch2[slice(0, 1)], batch2[:1], batch2[0:]]:
assert batch_slice.a.b == batch2.a.b
assert batch_slice.a.c == batch2.a.c
assert batch_slice.a.d.e == batch2.a.d.e
batch2_sum = (batch2 + 1.0) * 2
assert batch2_sum.a.b == (batch2.a.b + 1.0) * 2
assert batch2_sum.a.c == (batch2.a.c + 1.0) * 2
assert batch2_sum.a.d.e == (batch2.a.d.e + 1.0) * 2
batch3 = Batch(a={
'c': np.zeros(1),
'd': Batch(e=np.array([0.0]), f=np.array([3.0]))
})
batch3.a.d[0] = {'e': 4.0}
assert batch3.a.d.e[0] == 4.0
batch3.a.d[0] = Batch(f=5.0)
assert batch3.a.d.f[0] == 5.0
with pytest.raises(KeyError):
batch3.a.d[0] = Batch(f=5.0, g=0.0)
# auto convert
batch4 = Batch(a=np.array(['a', 'b']))
assert batch4.a.dtype == np.object # auto convert to np.object
batch4.update(a=np.array(['c', 'd']))
assert list(batch4.a) == ['c', 'd']
assert batch4.a.dtype == np.object # auto convert to np.object
batch5 = Batch(a=np.array([{'index': 0}]))
assert isinstance(batch5.a, Batch)
assert np.allclose(batch5.a.index, [0])
batch5.b = np.array([{'index': 1}])
assert isinstance(batch5.b, Batch)
assert np.allclose(batch5.b.index, [1])
# None is a valid object and can be stored in Batch
a = Batch.stack([Batch(a=None), Batch(b=None)])
assert a.a[0] is None and a.a[1] is None
assert a.b[0] is None and a.b[1] is None
