ray/rllib/models/tf/tf_action_dist.py

import numpy as np
import functools

from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.utils.annotations import override, DeveloperAPI
from ray.rllib.utils import try_import_tf, try_import_tfp, SMALL_NUMBER, \
    MIN_LOG_NN_OUTPUT, MAX_LOG_NN_OUTPUT
from ray.rllib.utils.tuple_actions import TupleActions

tf = try_import_tf()
tfp = try_import_tfp()


@DeveloperAPI
class TFActionDistribution(ActionDistribution):
    """TF-specific extensions for building action distributions."""

    @DeveloperAPI
    def __init__(self, inputs, model):
        super().__init__(inputs, model)
        self.sample_op = self._build_sample_op()
        self.sampled_action_logp_op = self.logp(self.sample_op)

    @DeveloperAPI
    def _build_sample_op(self):
        """Implement this instead of sample(), to enable op reuse.

        This is needed since the sample op is non-deterministic and is shared
        between sample() and sampled_action_logp().
        """
        raise NotImplementedError

    @override(ActionDistribution)
    def sample(self):
        """Draw a sample from the action distribution."""
        return self.sample_op

    @override(ActionDistribution)
    def sampled_action_logp(self):
        """Returns the log probability of the sampled action."""
        return self.sampled_action_logp_op


class Categorical(TFActionDistribution):
    """Categorical distribution for discrete action spaces."""

    @DeveloperAPI
    def __init__(self, inputs, model=None, temperature=1.0):
        assert temperature > 0.0, "Categorical `temperature` must be > 0.0!"
        # Allow softmax formula w/ temperature != 1.0:
        # Divide inputs by temperature.
        super().__init__(inputs / temperature, model)

    @override(ActionDistribution)
    def deterministic_sample(self):
        return tf.math.argmax(self.inputs, axis=1)

    @override(ActionDistribution)
    def logp(self, x):
        return -tf.nn.sparse_softmax_cross_entropy_with_logits(
            logits=self.inputs, labels=tf.cast(x, tf.int32))

    @override(ActionDistribution)
    def entropy(self):
        a0 = self.inputs - tf.reduce_max(self.inputs, axis=1, keep_dims=True)
        ea0 = tf.exp(a0)
        z0 = tf.reduce_sum(ea0, axis=1, keep_dims=True)
        p0 = ea0 / z0
        return tf.reduce_sum(p0 * (tf.log(z0) - a0), axis=1)

    @override(ActionDistribution)
    def kl(self, other):
        a0 = self.inputs - tf.reduce_max(self.inputs, axis=1, keep_dims=True)
        a1 = other.inputs - tf.reduce_max(other.inputs, axis=1, keep_dims=True)
        ea0 = tf.exp(a0)
        ea1 = tf.exp(a1)
        z0 = tf.reduce_sum(ea0, axis=1, keep_dims=True)
        z1 = tf.reduce_sum(ea1, axis=1, keep_dims=True)
        p0 = ea0 / z0
        return tf.reduce_sum(p0 * (a0 - tf.log(z0) - a1 + tf.log(z1)), axis=1)

    @override(TFActionDistribution)
    def _build_sample_op(self):
        return tf.squeeze(tf.multinomial(self.inputs, 1), axis=1)

    @staticmethod
    @override(ActionDistribution)
    def required_model_output_shape(action_space, model_config):
        return action_space.n


class MultiCategorical(TFActionDistribution):
    """MultiCategorical distribution for MultiDiscrete action spaces."""

    def __init__(self, inputs, model, input_lens):
        # skip TFActionDistribution init
        ActionDistribution.__init__(self, inputs, model)
        self.cats = [
            Categorical(input_, model)
            for input_ in tf.split(inputs, input_lens, axis=1)
        ]
        self.sample_op = self._build_sample_op()
        self.sampled_action_logp_op = self.logp(self.sample_op)

    @override(ActionDistribution)
    def deterministic_sample(self):
        return tf.stack(
            [cat.deterministic_sample() for cat in self.cats], axis=1)

    @override(ActionDistribution)
    def logp(self, actions):
        # If tensor is provided, unstack it into list.
        if isinstance(actions, tf.Tensor):
            actions = tf.unstack(tf.cast(actions, tf.int32), axis=1)
        logps = tf.stack(
            [cat.logp(act) for cat, act in zip(self.cats, actions)])
        return tf.reduce_sum(logps, axis=0)

    @override(ActionDistribution)
    def multi_entropy(self):
        return tf.stack([cat.entropy() for cat in self.cats], axis=1)

    @override(ActionDistribution)
    def entropy(self):
        return tf.reduce_sum(self.multi_entropy(), axis=1)

    @override(ActionDistribution)
    def multi_kl(self, other):
        return tf.stack(
            [cat.kl(oth_cat) for cat, oth_cat in zip(self.cats, other.cats)],
            axis=1)

    @override(ActionDistribution)
    def kl(self, other):
        return tf.reduce_sum(self.multi_kl(other), axis=1)

    @override(TFActionDistribution)
    def _build_sample_op(self):
        return tf.stack([cat.sample() for cat in self.cats], axis=1)

    @staticmethod
    @override(ActionDistribution)
    def required_model_output_shape(action_space, model_config):
        return np.sum(action_space.nvec)


class GumbelSoftmax(TFActionDistribution):
    """GumbelSoftmax distr. (for differentiable sampling in discr. actions

    The Gumbel Softmax distribution [1] (also known as the Concrete [2]
    distribution) is a close cousin of the relaxed one-hot categorical
    distribution, whose tfp implementation we will use here plus
    adjusted `sample_...` and `log_prob` methods. See discussion at [0].

    [0] https://stackoverflow.com/questions/56226133/
    soft-actor-critic-with-discrete-action-space

    [1] Categorical Reparametrization with Gumbel-Softmax (Jang et al, 2017):
    https://arxiv.org/abs/1611.01144
    [2] The Concrete Distribution: A Continuous Relaxation of Discrete Random
    Variables (Maddison et al, 2017) https://arxiv.org/abs/1611.00712
    """

    @DeveloperAPI
    def __init__(self, inputs, model=None, temperature=1.0):
        """Initializes a GumbelSoftmax distribution.

        Args:
            temperature (float): Temperature parameter. For low temperatures,
                the expected value approaches a categorical random variable.
                For high temperatures, the expected value approaches a uniform
                distribution.
        """
        assert temperature >= 0.0
        self.dist = tfp.distributions.RelaxedOneHotCategorical(
            temperature=temperature, logits=inputs)
        super().__init__(inputs, model)

    @override(ActionDistribution)
    def deterministic_sample(self):
        # Return the dist object's prob values.
        return self.dist._distribution.probs

    @override(ActionDistribution)
    def logp(self, x):
        # Override since the implementation of tfp.RelaxedOneHotCategorical
        # yields positive values.
        if x.shape != self.dist.logits.shape:
            values = tf.one_hot(
                x, self.dist.logits.shape.as_list()[-1], dtype=tf.float32)
            assert values.shape == self.dist.logits.shape, (
                values.shape, self.dist.logits.shape)

        # [0]'s implementation (see line below) seems to be an approximation
        # to the actual Gumbel Softmax density.
        return -tf.reduce_sum(
            -x * tf.nn.log_softmax(self.dist.logits, axis=-1), axis=-1)

    @override(TFActionDistribution)
    def _build_sample_op(self):
        return self.dist.sample()

    @staticmethod
    @override(ActionDistribution)
    def required_model_output_shape(action_space, model_config):
        return action_space.n


class DiagGaussian(TFActionDistribution):
    """Action distribution where each vector element is a gaussian.

    The first half of the input vector defines the gaussian means, and the
    second half the gaussian standard deviations.
    """

    def __init__(self, inputs, model):
        mean, log_std = tf.split(inputs, 2, axis=1)
        self.mean = mean
        self.log_std = log_std
        self.std = tf.exp(log_std)
        super().__init__(inputs, model)

    @override(ActionDistribution)
    def deterministic_sample(self):
        return self.mean

    @override(ActionDistribution)
    def logp(self, x):
        return -0.5 * tf.reduce_sum(
            tf.square((x - self.mean) / self.std), axis=1) - \
               0.5 * np.log(2.0 * np.pi) * tf.to_float(tf.shape(x)[1]) - \
               tf.reduce_sum(self.log_std, axis=1)

    @override(ActionDistribution)
    def kl(self, other):
        assert isinstance(other, DiagGaussian)
        return tf.reduce_sum(
            other.log_std - self.log_std +
            (tf.square(self.std) + tf.square(self.mean - other.mean)) /
            (2.0 * tf.square(other.std)) - 0.5,
            axis=1)

    @override(ActionDistribution)
    def entropy(self):
        return tf.reduce_sum(
            self.log_std + .5 * np.log(2.0 * np.pi * np.e), axis=1)

    @override(TFActionDistribution)
    def _build_sample_op(self):
        return self.mean + self.std * tf.random_normal(tf.shape(self.mean))

    @staticmethod
    @override(ActionDistribution)
    def required_model_output_shape(action_space, model_config):
        return np.prod(action_space.shape) * 2


class SquashedGaussian(TFActionDistribution):
    """A tanh-squashed Gaussian distribution defined by: mean, std, low, high.

    The distribution will never return low or high exactly, but
    `low`+SMALL_NUMBER or `high`-SMALL_NUMBER respectively.
    """

    def __init__(self, inputs, model, low=-1.0, high=1.0):
        """Parameterizes the distribution via `inputs`.

        Args:
            low (float): The lowest possible sampling value
                (excluding this value).
            high (float): The highest possible sampling value
                (excluding this value).
        """
        assert tfp is not None
        mean, log_std = tf.split(inputs, 2, axis=-1)
        # Clip `scale` values (coming from NN) to reasonable values.
        log_std = tf.clip_by_value(log_std, MIN_LOG_NN_OUTPUT,
                                   MAX_LOG_NN_OUTPUT)
        std = tf.exp(log_std)
        self.distr = tfp.distributions.Normal(loc=mean, scale=std)
        assert np.all(np.less(low, high))
        self.low = low
        self.high = high
        super().__init__(inputs, model)

    @override(ActionDistribution)
    def deterministic_sample(self):
        mean = self.distr.mean()
        return self._squash(mean)

    @override(TFActionDistribution)
    def _build_sample_op(self):
        return self._squash(self.distr.sample())

    @override(ActionDistribution)
    def logp(self, x):
        unsquashed_values = self._unsquash(x)
        log_prob = tf.reduce_sum(
            self.distr.log_prob(value=unsquashed_values), axis=-1)
        unsquashed_values_tanhd = tf.math.tanh(unsquashed_values)
        log_prob -= tf.math.reduce_sum(
            tf.math.log(1 - unsquashed_values_tanhd**2 + SMALL_NUMBER),
            axis=-1)
        return log_prob

    def _squash(self, raw_values):
        # Make sure raw_values are not too high/low (such that tanh would
        # return exactly 1.0/-1.0, which would lead to +/-inf log-probs).
        return (tf.clip_by_value(
            tf.math.tanh(raw_values),
            -1.0 + SMALL_NUMBER,
            1.0 - SMALL_NUMBER) + 1.0) / 2.0 * (self.high - self.low) + \
               self.low

    def _unsquash(self, values):
        return tf.math.atanh((values - self.low) /
                             (self.high - self.low) * 2.0 - 1.0)


class Deterministic(TFActionDistribution):
    """Action distribution that returns the input values directly.

    This is similar to DiagGaussian with standard deviation zero (thus only
    requiring the "mean" values as NN output).
    """

    @override(ActionDistribution)
    def deterministic_sample(self):
        return self.inputs

    @override(TFActionDistribution)
    def logp(self, x):
        return tf.zeros_like(self.inputs)

    @override(TFActionDistribution)
    def _build_sample_op(self):
        return self.inputs

    @staticmethod
    @override(ActionDistribution)
    def required_model_output_shape(action_space, model_config):
        return np.prod(action_space.shape)


class MultiActionDistribution(TFActionDistribution):
    """Action distribution that operates for list of actions.

    Args:
        inputs (Tensor list): A list of tensors from which to compute samples.
    """

    def __init__(self, inputs, model, action_space, child_distributions,
                 input_lens):
        # skip TFActionDistribution init
        ActionDistribution.__init__(self, inputs, model)
        self.input_lens = input_lens
        split_inputs = tf.split(inputs, self.input_lens, axis=1)
        child_list = []
        for i, distribution in enumerate(child_distributions):
            child_list.append(distribution(split_inputs[i], model))
        self.child_distributions = child_list

    @override(ActionDistribution)
    def logp(self, x):
        split_indices = []
        for dist in self.child_distributions:
            if isinstance(dist, Categorical):
                split_indices.append(1)
            else:
                split_indices.append(tf.shape(dist.sample())[1])
        split_list = tf.split(x, split_indices, axis=1)
        for i, distribution in enumerate(self.child_distributions):
            # Remove extra categorical dimension
            if isinstance(distribution, Categorical):
                split_list[i] = tf.cast(
                    tf.squeeze(split_list[i], axis=-1), tf.int32)
        log_list = [
            distribution.logp(split_x) for distribution, split_x in zip(
                self.child_distributions, split_list)
        ]
        return functools.reduce(lambda a, b: a + b, log_list)

    @override(ActionDistribution)
    def kl(self, other):
        kl_list = [
            distribution.kl(other_distribution)
            for distribution, other_distribution in zip(
                self.child_distributions, other.child_distributions)
        ]
        return functools.reduce(lambda a, b: a + b, kl_list)

    @override(ActionDistribution)
    def entropy(self):
        entropy_list = [s.entropy() for s in self.child_distributions]
        return functools.reduce(lambda a, b: a + b, entropy_list)

    @override(ActionDistribution)
    def sample(self):
        return TupleActions([s.sample() for s in self.child_distributions])

    @override(ActionDistribution)
    def deterministic_sample(self):
        return TupleActions(
            [s.deterministic_sample() for s in self.child_distributions])

    @override(TFActionDistribution)
    def sampled_action_logp(self):
        p = self.child_distributions[0].sampled_action_logp()
        for c in self.child_distributions[1:]:
            p += c.sampled_action_logp()
        return p


class Dirichlet(TFActionDistribution):
    """Dirichlet distribution for continuous actions that are between
    [0,1] and sum to 1.

    e.g. actions that represent resource allocation."""

    def __init__(self, inputs, model):
        """Input is a tensor of logits. The exponential of logits is used to
        parametrize the Dirichlet distribution as all parameters need to be
        positive. An arbitrary small epsilon is added to the concentration
        parameters to be zero due to numerical error.

        See issue #4440 for more details.
        """
        self.epsilon = 1e-7
        concentration = tf.exp(inputs) + self.epsilon
        self.dist = tf.distributions.Dirichlet(
            concentration=concentration,
            validate_args=True,
            allow_nan_stats=False,
        )
        super().__init__(concentration, model)

    @override(ActionDistribution)
    def logp(self, x):
        # Support of Dirichlet are positive real numbers. x is already
        # an array of positive numbers, but we clip to avoid zeros due to
        # numerical errors.
        x = tf.maximum(x, self.epsilon)
        x = x / tf.reduce_sum(x, axis=-1, keepdims=True)
        return self.dist.log_prob(x)

    @override(ActionDistribution)
    def entropy(self):
        return self.dist.entropy()

    @override(ActionDistribution)
    def kl(self, other):
        return self.dist.kl_divergence(other.dist)

    @override(TFActionDistribution)
    def _build_sample_op(self):
        return self.dist.sample()

    @staticmethod
    @override(ActionDistribution)
    def required_model_output_shape(action_space, model_config):
        return np.prod(action_space.shape)