def _dense_projection(inputs, shape, trainable=True):
shape = list(shape)
flat_shape = array_utils.product(shape)
target_shape = _EXTRA_DIMS + shape
input_shape = inputs.get_shape().as_list()
input_dims = len(input_shape)
expected_dims = len(target_shape)
assert_utils.assert_true(
input_dims == expected_dims, ', '.join([
'`inputs` must have the same number of dims as the number expected dims.'
'expected = {}, actual = {}'.format(input_dims, expected_dims)
]))
if not array_utils.all_equal(input_shape, target_shape):
input_shape_ = array_ops.shape(inputs)
if len(input_shape) > 3:
inputs = gen_array_ops.reshape(
inputs, [input_shape_[0], input_shape_[1], -1])
inputs = core.dense(inputs,
flat_shape,
use_bias=False,
trainable=trainable)
inputs = gen_array_ops.reshape(
inputs, [input_shape_[0], input_shape_[1]] + shape)
return inputs
def step(self, action):
assert_utils.assert_true(
self.action_space.contains(action),
'`action_space` must contain `action`.')
# sample from bernoulli distribution with p = bandit[action]
reward = int(np.random.uniform() < self.bandit[action])
self.time += 1
done = self.time > self.max_time
return [self.time], reward, done, {}
def ssd(x, y, extra_dims=2):
"""`Sum Squared over D`: `l2` over `n`-dimensions (starting at `extra_dims`)
Math:
ssd(x, y) = sum [l2(x-y)] in range (extra_dims, dims(x|y)]
"""
assert_utils.assert_true(
extra_dims >= 0, "extra_dims must be >= 0, got {}".format(extra_dims))
shape = y.get_shape().as_list()[extra_dims:]
return math_ops.reduce_sum(math_ops.square(x - y),
axis=array_utils.ranged_axes(shape))
def __init__(self, max_time=5):
self.time = 0
self.max_time = max_time
assert_utils.assert_true(
len(probs) == 11,
"if `probs` was meant to be a `list`/`tuple`, then it must be of size 2.")
self.bandit = np.array([1.] * 11)
self.informative_action = 0
self.action_space = discrete.Discrete(len(self.bandit))
self.observation_space = discrete.Discrete(1)
self.seed()
def expected_q_value(reward,
action,
action_value,
next_action_value,
weights=1.,
discount=.95):
"""Computes the expected q returns and values.
This covers architectures such as DQN, Double-DQN, Dueling-DQN and Noisy-DQN.
Arguments:
rewards: 1D or 2D `tf.Tensor`, contiguous sequence(s) of rewards.
action: 1D or 2D `tf.Tensor`, contiguous sequence(s) of actions.
next_action_value: `tf.Tensor`, `list` or `tuple` of 2 `tf.Tensor`s, where the first entry is
the `model(next_state) = action_value`, and the second is `target(next_state) = action_value`
weights: `tf.Tensor`, the weights/mask to apply to the result.
discount: 0D scalar, the discount factor (gamma).
Returns:
`tuple` containing the `q_value` `tf.Tensor` and `expected_q_value` `tf.Tensor`.
Reference:
https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf
"""
weights = ops.convert_to_tensor(weights, dtype=reward.dtype)
discount = ops.convert_to_tensor(discount, dtype=reward.dtype)
lda = action_value.get_shape()[-1].value
q_value = gather_along_second_axis(action_value, action)
q_value.set_shape([None, None, lda])
if isinstance(next_action_value, tuple) or isinstance(
next_action_value, list):
assert_utils.assert_true(
len(next_action_value) == 2,
'`next_action_value` must be a `tuple` of length = 2')
next_action_value, target_next_action_value = next_action_value
lda = next_action_value.get_shape()[-1].value
next_q_value = gather_along_second_axis(
next_action_value,
math_ops.argmax(target_next_action_value,
-1,
output_type=dtypes.int32))
next_q_value.set_shape([None, None, lda])
else:
lda = next_action_value.get_shape()[-1].value
next_q_value = gather_along_second_axis(
next_action_value,
math_ops.argmax(next_action_value, -1, output_type=dtypes.int32))
next_q_value.set_shape([None, None, lda])
expected_q_value = reward + discount * next_q_value * weights
return (q_value, expected_q_value)
def step(self, action):
assert_utils.assert_true(
self.action_space.contains(action),
'`action_space` must contain `action`.')
if self.time == 0:
if action == self.informative_action:
reward = .55
else:
reward = 1.4
else:
reward = self.bandit[action]
self.time += 1
done = self.time > self.max_time
return [self.time], reward, done, {}
def from_distributions(cls,
state_distribution,
action_distribution,
reward_shape=[],
reward_dtype=dtypes.float32,
with_values=False):
"""Construct a `alchemy.contrib.rl.ReplayStream` from a `gym.Env`.
Arguments:
env: a `gym.Env` instance that has `action_space` and `observation_space` properties.
state_distribution: distribution of the state space.
action_distribution: distribution of the action space.
reward_shape: shape representing the reward for a chosen action.
reward_dtype: dtype representing the reward for a chosen action.
with_values: Python `bool` for recording values.
Returns:
A `ay.contrib.rl.ReplayStream`.
"""
assert_utils.assert_true(
distribution_utils.is_distribution(state_distribution),
'`state_distribution` must be an instance of `tf.distributions.Distribution`'
)
assert_utils.assert_true(
distribution_utils.is_distribution(action_distribution),
'`action_distribution` must be an instance of `tf.distributions.Distribution`'
)
state_shape, state_dtype = distribution_utils.logits_shape_and_dtype(
state_distribution)
action_value_shape, action_value_dtype = distribution_utils.logits_shape_and_dtype(
action_distribution)
action_shape, action_dtype = distribution_utils.sample_shape_and_dtype(
action_distribution)
if isinstance(reward_shape, tensor_shape.TensorShape):
reward_shape = reward_shape.as_list()
return cls(state_shape,
state_dtype,
action_shape,
action_dtype,
action_value_shape,
action_value_dtype,
reward_shape,
type_utils.safe_tf_dtype(reward_dtype),
with_values=with_values)
def __init__(self, probs, max_time=99):
self.time = 0
self.max_time = max_time
# independent case where probs are independent of each other
# where p = p1 + p2, p >= 1 and > 0
if isinstance(probs, list) or isinstance(probs, tuple):
assert_utils.assert_true(
len(probs) == 2,
"if `probs` was meant to be a `list`/`tuple`, then it must be of size 2.")
self.bandit = np.array([probs[0], probs[1]])
# dependent bandits
# where p = p1 + (1 - p1), p = 1
else:
self.bandit = np.array([probs, 1. - probs])
self.action_space = discrete.Discrete(len(self.bandit))
self.observation_space = discrete.Discrete(1)
self.seed()
def create_example(self, k=8, use_modified=False):
"""Creates a single (modified) example of length `k`.
Arguments:
k: an even `int` that defines the length of the sample space. For example, if `k = 8` and the
vocab contains `ATCG`, then a sample would look like this: (A9C5G1T3??C, 5).
use_modified: `bool` that, when `True`, makes samples contiguous alpha-numeric. For example,
when `k = 8` and the vocab contains `ATCG`, then a sample would look like this:
(ACTG9513??C, 5). The label is the same as the unmodified version, but the sequence is no
longer zipped.
Returns:
A tuple containing the one-hot encoded values from the vocab. For example, if `k = 8` and the
vocab contains `ATCG`, then this would return onehot(A9C5G1T3??C, 5), where the
length of the onehot encoding vectors = len('ACTG') + len([0...9]) + len('?')
= `vocab_size`.
"""
q, r = divmod(k, 2)
assert_utils.assert_true(
r == 0 and k > 1 and k < self._alphabet_size,
"k must be even, > 1, and < {}".format(self._alphabet_size))
letters = np.random.choice(range(0, self._chars_size),
q,
replace=False)
numbers = np.random.choice(range(self._chars_size + 1,
self._alphabet_size),
q,
replace=True)
if use_modified:
x = np.concatenate((letters, numbers))
else:
x = np.stack((letters, numbers)).T.ravel()
x = np.append(x, [self._alphabet_size, self._alphabet_size])
index = np.random.choice(range(0, q), 1, replace=False)
x = np.append(x, [letters[index]]).astype('int')
y = numbers[index]
return (self._encoder[x], self._encoder[y][0])
def epsilon_greedy(dist, epsilon, deterministic):
"""Compute the mode of the distribution if epsilon < X ~ U(0, 1), else sample.
Arguments:
dist: a `tf.distribution.Distribution` to sample/mode from.
epsilon: scalar `tf.Tensor`.
deterministic: `Boolean` or `tf.Tensor` boolean, if `True` the mode is always be chosen.
Raises:
`AssertionError` if dist is not a `tf.distribution.Distribution`.
Returns:
`tf.Tensor` shape of `dist.event_shape`.
"""
assert_utils.assert_true(
distribution_utils.is_distribution(dist),
'`dist must be a `tf.distribution.Distribution.`')
deterministic_sample = lambda: dist.mode()
return control_flow_ops.cond(
deterministic, deterministic_sample,
lambda: control_flow_ops.cond(epsilon < random_ops.random_uniform([
]), deterministic_sample, lambda: dist.sample()))
def ReplayDataset(replay_stream, max_sequence_length=200, name=None):
"""Creates a `tf.data.Dataset` from a `ay.contrib.rl.ReplayStream` instance.
Arguments:
replay_stream: `ay.contrib.rl.ReplayStream` instance. Must implement `replay_stream.read`.
The method is called `replay_stream.read(limit=max_sequence_length)` each time an instance
is requested by the dataset. This method should return `None` or raise an
`tf.errors.OutOfRangeError` when the stream is done and execution of the dataset should stop.
`replay_stream.read` should always return a `tf.SequenceExample` proto.
Returns:
A `tf.data.Dataset`.
Raises:
An `tf.errors.OutOfRangeError` when the stream returns a `None` or raises
`tf.errors.OutOfRangeError`.
"""
assert_utils.assert_true(
isinstance(replay_stream, streams.ReplayStream),
'`replay_stream` must be an instance of `ay.contrib.rl.ReplayStream`')
with ops.name_scope(name or 'replay_dataset'):
state_shape = list(replay_stream.state_shape)
state_dtype = replay_stream.state_dtype
action_shape = list(replay_stream.action_shape)
action_dtype = replay_stream.action_dtype
action_value_shape = list(replay_stream.action_value_shape)
action_value_dtype = replay_stream.action_value_dtype
reward_shape = list(replay_stream.reward_shape)
reward_dtype = replay_stream.reward_dtype
replay_dtypes = {
'state': state_dtype,
'next_state': state_dtype,
'action': action_dtype,
'action_value': action_value_dtype,
'reward': reward_dtype,
'terminal': dtypes.bool,
'sequence_length': dtypes.int32,
}
if replay_stream.with_values:
replay_dtypes['value'] = reward_dtype
def convert_to_safe_feature_type(dtype):
return type_utils.safe_tf_dtype(
serialize.type_to_feature[dtype][-1])
replay_features = {
'state':
parsing_ops.FixedLenSequenceFeature(
shape=state_shape,
dtype=convert_to_safe_feature_type(state_dtype)),
'next_state':
parsing_ops.FixedLenSequenceFeature(
shape=state_shape,
dtype=convert_to_safe_feature_type(state_dtype)),
'action':
parsing_ops.FixedLenSequenceFeature(
shape=action_shape,
dtype=convert_to_safe_feature_type(action_dtype)),
'action_value':
parsing_ops.FixedLenSequenceFeature(
shape=action_value_shape,
dtype=convert_to_safe_feature_type(action_value_dtype)),
'reward':
parsing_ops.FixedLenSequenceFeature(
shape=reward_shape,
dtype=convert_to_safe_feature_type(reward_dtype)),
'terminal':
parsing_ops.FixedLenSequenceFeature(
shape=[], dtype=convert_to_safe_feature_type(dtypes.bool)),
'sequence_length':
parsing_ops.FixedLenSequenceFeature(
shape=[], dtype=convert_to_safe_feature_type(dtypes.int32)),
}
if replay_stream.with_values:
replay_features['value'] = parsing_ops.FixedLenSequenceFeature(
shape=reward_shape,
dtype=convert_to_safe_feature_type(reward_dtype))
def convert_and_fix_dtypes(replay):
"""Cast dtypes back to their original types."""
fixed_replay = {}
for k, v in replay.items():
fixed_replay[k] = math_ops.cast(v, dtype=replay_dtypes[k])
return fixed_replay
def generator():
"""Create `tf.Tensor`s from the `ay.contrib.rl.ReplayStream` instance."""
while True:
replay_example = None
try:
replay_example = replay_stream.read(
limit=max_sequence_length)
except:
yield ""
else:
yield replay_example.SerializeToString()
def serialize_map(replay_example_str):
"""Parse each example string to `tf.Tensor`."""
try:
assert_op = control_flow_ops.Assert(replay_example_str != "",
[replay_example_str])
with ops.control_dependencies([assert_op]):
_, replay = parsing_ops.parse_single_sequence_example(
replay_example_str, sequence_features=replay_features)
except errors_impl.InvalidArgumentError:
raise errors_impl.OutOfRangeError()
return convert_and_fix_dtypes(replay)
def pad_or_truncate_map(replay):
"""Truncate or pad replays."""
with_values = 'value' in replay
if with_values:
replay = experience.ReplayWithValues(**replay)
else:
replay = experience.Replay(**replay)
sequence_length = math_ops.minimum(max_sequence_length,
replay.sequence_length)
sequence_length.set_shape([1])
state = sequence_utils.pad_or_truncate(replay.state,
max_sequence_length,
axis=0,
pad_value=0)
state.set_shape([max_sequence_length] + state_shape)
next_state = sequence_utils.pad_or_truncate(replay.next_state,
max_sequence_length,
axis=0,
pad_value=0)
next_state.set_shape([max_sequence_length] + state_shape)
action = sequence_utils.pad_or_truncate(replay.action,
max_sequence_length,
axis=0,
pad_value=0)
action.set_shape([max_sequence_length] + action_shape)
action_value = sequence_utils.pad_or_truncate(replay.action_value,
max_sequence_length,
axis=0,
pad_value=0)
action_value.set_shape([max_sequence_length] + action_value_shape)
reward = sequence_utils.pad_or_truncate(replay.reward,
max_sequence_length,
axis=0,
pad_value=0)
reward.set_shape([max_sequence_length] + reward_shape)
terminal = sequence_utils.pad_or_truncate(
replay.terminal,
max_sequence_length,
axis=0,
pad_value=ops.convert_to_tensor(False))
terminal.set_shape([max_sequence_length])
if with_values:
value = sequence_utils.pad_or_truncate(replay.value,
max_sequence_length,
axis=0,
pad_value=0)
value.set_shape([max_sequence_length] + reward_shape)
return experience.ReplayWithValues(
state=state,
next_state=next_state,
action=action,
action_value=action_value,
value=value,
reward=reward,
terminal=terminal,
sequence_length=sequence_length)
return experience.Replay(state=state,
next_state=next_state,
action=action,
action_value=action_value,
reward=reward,
terminal=terminal,
sequence_length=sequence_length)
dataset = dataset_ops.Dataset.from_generator(generator, dtypes.string)
dataset = dataset.map(serialize_map)
return dataset.map(pad_or_truncate_map)
def serialize_replay(replay,
state_dtype,
action_dtype,
action_value_dtype,
reward_dtype,
with_values=False):
"""Returns a `tf.train.SequenceExample` for the given `ay.contrib.rl.Replay` instance.
Arguments:
replay: `ay.contrib.rl.Replay` instance.
state_dtype: dtype of the state space.
action_dtype: dtype of the action space.
action_value_dtype: dtype of the action-values space.
reward_dtype: dtype of the reward space.
with_values: Python `bool` for recording values.
Returns:
A `tf.train.SequenceExample` containing info from the `ay.contrib.rl.Replay`.
Raises:
`AssertionError` when replay is not an `ay.rl.Replay` instance.
"""
if with_values:
assert_utils.assert_true(
isinstance(replay, experience.ReplayWithValues),
'`replay` must be an instance of `ay.contrib.rl.ReplayWithValues`')
else:
assert_utils.assert_true(
isinstance(replay, experience.Replay),
'`replay` must be an instance of `ay.contrib.rl.Replay`')
feature_list = {
'state':
tf.train.FeatureList(
feature=serialize_replay_feature(replay.state, dtype=state_dtype)),
'next_state':
tf.train.FeatureList(feature=serialize_replay_feature(
replay.next_state, dtype=state_dtype)),
'action':
tf.train.FeatureList(feature=serialize_replay_feature(
replay.action, dtype=action_dtype)),
'action_value':
tf.train.FeatureList(feature=serialize_replay_feature(
replay.action_value, dtype=action_value_dtype)),
'reward':
tf.train.FeatureList(feature=serialize_replay_feature(
replay.reward, dtype=reward_dtype)),
'terminal':
tf.train.FeatureList(feature=serialize_replay_feature(
replay.terminal, dtype=dtypes.bool)),
'sequence_length':
tf.train.FeatureList(feature=serialize_replay_feature(
[replay.sequence_length], dtype=dtypes.int32)),
}
if with_values:
feature_list['value'] = tf.train.FeatureList(
feature=serialize_replay_feature(replay.value, dtype=reward_dtype))
feature_lists = tf.train.FeatureLists(feature_list=feature_list)
return tf.train.SequenceExample(feature_lists=feature_lists)
def distribution_from_gym_space(space, logits=None, name='SpaceDistribution'):
"""Determines a parameterized `tf.distribution.Distribution` from the `gym.Space`.
Arguments:
space: a `gym.Space` instance (i.e. `env.action_space`)
logits: optional `list` of `tf.Tensor` to be used instead of creating them.
name: Python `str` name prefixed to Ops created.
Raises:
`TypeError` when space is not a `gym.Space` instance.
Returns:
Either one of the following: , `tuple` or `dict` of `DistributionWithLogits`, or
just `DistributionWithLogits`.
"""
assert_utils.assert_true(isinstance(space, Space),
'`space` must be an instance of `gym.Space`')
with ops.name_scope(name):
if isinstance(space, discrete.Discrete):
if logits and isinstance(logits[0], ops.Tensor):
logits = _dense_projection(logits[0], [space.n])
else:
logits = _placeholder_factory_map[discrete.Discrete](space)
distribution = categorical.Categorical(
logits=math_ops.cast(logits, dtypes.float32))
return DistributionWithLogits(distribution=distribution,
logits=logits)
elif isinstance(space, multi_discrete.MultiDiscrete):
if logits and isinstance(logits[0], ops.Tensor):
logits = _dense_projection(logits[0], space.shape)
else:
logits = _placeholder_factory_map[
multi_discrete.MultiDiscrete](space)
distribution = categorical.Categorical(
logits=math_ops.cast(logits, dtypes.float32))
return DistributionWithLogits(distribution=distribution,
logits=logits)
elif isinstance(space, multi_binary.MultiBinary):
if logits and isinstance(logits[0], ops.Tensor):
logits = _dense_projection(logits[0], space.shape)
else:
logits = _placeholder_factory_map[multi_binary.MultiBinary](
space)
distribution = bernoulli.Bernoulli(logits=logits)
return DistributionWithLogits(distribution=distribution,
logits=logits)
elif isinstance(space, box.Box):
if logits and isinstance(logits[0], ops.Tensor):
logits = _dense_projection(logits[0], space.shape)
else:
logits = _placeholder_factory_map[box.Box](space)
flat_shape = array_utils.product(space.shape)
shape = array_ops.shape(logits)
logits = gen_array_ops.reshape(logits,
[shape[0], shape[1], flat_shape])
log_eps = math.log(distribution_utils.epsilon)
alpha = core.dense(logits, flat_shape, use_bias=False)
alpha = clip_ops.clip_by_value(alpha, log_eps, -log_eps)
alpha = math_ops.log(math_ops.exp(alpha) + 1.0) + 1.0
alpha = gen_array_ops.reshape(alpha, shape)
beta = core.dense(logits, flat_shape, use_bias=False)
beta = clip_ops.clip_by_value(beta, log_eps, -log_eps)
beta = math_ops.log(math_ops.exp(beta) + 1.0) + 1.0
beta = gen_array_ops.reshape(beta, shape)
distribution = beta_min_max.BetaMinMax(concentration1=alpha,
concentration0=beta,
min_value=space.low,
max_value=space.high)
return DistributionWithLogits(distribution=distribution,
logits=logits)
elif isinstance(space, tuple_space.Tuple):
if not logits:
logits = [None] * len(space.spaces)
return tuple(
distribution_from_gym_space(
val, logits=[logit], name='tuple_{}'.format(idx))
for idx, (val, logit) in enumerate(zip(space.spaces, logits)))
elif isinstance(space, dict_space.Dict):
if not logits:
logits = [None] * len(space.spaces)
return {
key: distribution_from_gym_space(val,
logits=[logit],
name='{}'.format(key))
for (key, val), logit in zip(space.spaces.items(), logits)
}
raise TypeError('`space` not supported: {}'.format(type(space)))
def expected_q_value(reward,
action,
action_value,
next_action_value,
sequence_length,
max_sequence_length,
weights=1.,
discount=.95,
n_step=False):
"""Computes the expected q returns and values.
This covers architectures such as DQN, Double-DQN, Dueling-DQN and Noisy-DQN.
Arguments:
reward: 1D or 2D `tf.Tensor`, contiguous sequence(s) of rewards.
action: 1D or 2D `tf.Tensor`, contiguous sequence(s) of actions.
next_action_value: `tf.Tensor`, `list` or `tuple` of 2 `tf.Tensor`s, where the first entry is
the `model(next_state) = action_value`, and the second is `target(next_state) = action_value`
sequence_length: 1D `tf.Tensor`, tensor containing lengths of rewards, action_values, etc..
max_sequence_length: `int` or `list`, maximum length(s) of rewards.
weights: `tf.Tensor`, the weights/mask to apply to the result.
discount: 0D scalar, the discount factor (gamma).
n_step: 0D bool, if n-step algorithm should be used, MC sampling with discounts.
Returns:
`tuple` containing the `q_value` `tf.Tensor` and `expected_q_value` `tf.Tensor`.
Reference:
https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf
"""
weights = ops.convert_to_tensor(weights, dtype=reward.dtype)
discount = ops.convert_to_tensor(discount, dtype=reward.dtype)
n_step = ops.convert_to_tensor(n_step, dtype=dtypes.bool)
ndim = len(action_value.shape)
q_value = sequence_utils.gather_along_second_axis(action_value, action)
q_value.set_shape([None, max_sequence_length])
if isinstance(next_action_value, tuple) or isinstance(
next_action_value, list):
assert_utils.assert_true(
len(next_action_value) == 2,
'`next_action_value` must be a `tuple` of length = 2')
next_action_value, target_next_action_value = next_action_value
next_q_value = sequence_utils.gather_along_second_axis(
next_action_value,
math_ops.argmax(target_next_action_value,
-1,
output_type=dtypes.int32))
else:
next_q_value = sequence_utils.gather_along_second_axis(
next_action_value,
math_ops.argmax(next_action_value, -1, output_type=dtypes.int32))
next_q_value.set_shape([None, max_sequence_length])
def n_step_return():
rest_of_rewards = reward[:, 1:]
initial_reward = reward[:, 0]
initial_rewards = array_ops.concat([
array_ops.expand_dims(initial_reward, -1),
array_ops.zeros_like(rest_of_rewards)
], -1)
reward_t = initial_rewards + array_ops.concat([
array_ops.zeros_like(array_ops.expand_dims(initial_reward, -1)),
math_ops.cumsum(discount * rest_of_rewards, axis=-1, reverse=False)
], -1)
discount_t = array_ops.expand_dims(
array_ops.tile(array_ops.expand_dims(discount, -1),
[array_ops.shape(sequence_length)[0]
])**math_ops.cast(sequence_length, dtypes.float32),
-1)
return reward_t + discount_t * next_q_value
def single_step_return():
reward_t = reward
return reward_t + discount * next_q_value
expected_q_value = control_flow_ops.cond(n_step, n_step_return,
single_step_return)
expected_q_value.set_shape([None, max_sequence_length])
return (q_value * weights, expected_q_value * weights)