ray/rllib/agents/impala/vtrace_tf.py

# Copyright 2018 Google LLC
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     https://www.apache.org/licenses/LICENSE-2.0
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"""Functions to compute V-trace off-policy actor critic targets.

For details and theory see:

"IMPALA: Scalable Distributed Deep-RL with
Importance Weighted Actor-Learner Architectures"
by Espeholt, Soyer, Munos et al.

See https://arxiv.org/abs/1802.01561 for the full paper.

In addition to the original paper's code, changes have been made
to support MultiDiscrete action spaces. behaviour_policy_logits,
target_policy_logits and actions parameters in the entry point
multi_from_logits method accepts lists of tensors instead of just
tensors.
"""

import collections

from ray.rllib.models.tf.tf_action_dist import Categorical
from ray.rllib.utils import try_import_tf

tf = try_import_tf()

VTraceFromLogitsReturns = collections.namedtuple("VTraceFromLogitsReturns", [
    "vs", "pg_advantages", "log_rhos", "behaviour_action_log_probs",
    "target_action_log_probs"
])

VTraceReturns = collections.namedtuple("VTraceReturns", "vs pg_advantages")


def log_probs_from_logits_and_actions(policy_logits,
                                      actions,
                                      dist_class=Categorical,
                                      model=None):
    return multi_log_probs_from_logits_and_actions([policy_logits], [actions],
                                                   dist_class, model)[0]


def multi_log_probs_from_logits_and_actions(policy_logits, actions, dist_class,
                                            model):
    """Computes action log-probs from policy logits and actions.

    In the notation used throughout documentation and comments, T refers to the
    time dimension ranging from 0 to T-1. B refers to the batch size and
    ACTION_SPACE refers to the list of numbers each representing a number of
    actions.

    Args:
        policy_logits: A list with length of ACTION_SPACE of float32
            tensors of shapes [T, B, ACTION_SPACE[0]], ...,
            [T, B, ACTION_SPACE[-1]] with un-normalized log-probabilities
            parameterizing a softmax policy.
        actions: A list with length of ACTION_SPACE of tensors of shapes
            [T, B, ...], ..., [T, B, ...]
            with actions.
        dist_class: Python class of the action distribution.

    Returns:
        A list with length of ACTION_SPACE of float32 tensors of shapes
            [T, B], ..., [T, B] corresponding to the sampling log probability
            of the chosen action w.r.t. the policy.
    """
    log_probs = []
    for i in range(len(policy_logits)):
        p_shape = tf.shape(policy_logits[i])
        a_shape = tf.shape(actions[i])
        policy_logits_flat = tf.reshape(policy_logits[i],
                                        tf.concat([[-1], p_shape[2:]], axis=0))
        actions_flat = tf.reshape(actions[i],
                                  tf.concat([[-1], a_shape[2:]], axis=0))
        log_probs.append(
            tf.reshape(
                dist_class(policy_logits_flat, model).logp(actions_flat),
                a_shape[:2]))

    return log_probs


def from_logits(behaviour_policy_logits,
                target_policy_logits,
                actions,
                discounts,
                rewards,
                values,
                bootstrap_value,
                dist_class=Categorical,
                model=None,
                clip_rho_threshold=1.0,
                clip_pg_rho_threshold=1.0,
                name="vtrace_from_logits"):
    """multi_from_logits wrapper used only for tests"""

    res = multi_from_logits(
        [behaviour_policy_logits], [target_policy_logits], [actions],
        discounts,
        rewards,
        values,
        bootstrap_value,
        dist_class,
        model,
        clip_rho_threshold=clip_rho_threshold,
        clip_pg_rho_threshold=clip_pg_rho_threshold,
        name=name)

    return VTraceFromLogitsReturns(
        vs=res.vs,
        pg_advantages=res.pg_advantages,
        log_rhos=res.log_rhos,
        behaviour_action_log_probs=tf.squeeze(
            res.behaviour_action_log_probs, axis=0),
        target_action_log_probs=tf.squeeze(
            res.target_action_log_probs, axis=0),
    )


def multi_from_logits(behaviour_policy_logits,
                      target_policy_logits,
                      actions,
                      discounts,
                      rewards,
                      values,
                      bootstrap_value,
                      dist_class,
                      model,
                      behaviour_action_log_probs=None,
                      clip_rho_threshold=1.0,
                      clip_pg_rho_threshold=1.0,
                      name="vtrace_from_logits"):
    r"""V-trace for softmax policies.

  Calculates V-trace actor critic targets for softmax polices as described in

  "IMPALA: Scalable Distributed Deep-RL with
  Importance Weighted Actor-Learner Architectures"
  by Espeholt, Soyer, Munos et al.

  Target policy refers to the policy we are interested in improving and
  behaviour policy refers to the policy that generated the given
  rewards and actions.

  In the notation used throughout documentation and comments, T refers to the
  time dimension ranging from 0 to T-1. B refers to the batch size and
  ACTION_SPACE refers to the list of numbers each representing a number of
  actions.

  Args:
    behaviour_policy_logits: A list with length of ACTION_SPACE of float32
      tensors of shapes
      [T, B, ACTION_SPACE[0]],
      ...,
      [T, B, ACTION_SPACE[-1]]
      with un-normalized log-probabilities parameterizing the softmax behaviour
      policy.
    target_policy_logits: A list with length of ACTION_SPACE of float32
      tensors of shapes
      [T, B, ACTION_SPACE[0]],
      ...,
      [T, B, ACTION_SPACE[-1]]
      with un-normalized log-probabilities parameterizing the softmax target
      policy.
    actions: A list with length of ACTION_SPACE of
      tensors of shapes
      [T, B, ...],
      ...,
      [T, B, ...]
      with actions sampled from the behaviour policy.
    discounts: A float32 tensor of shape [T, B] with the discount encountered
      when following the behaviour policy.
    rewards: A float32 tensor of shape [T, B] with the rewards generated by
      following the behaviour policy.
    values: A float32 tensor of shape [T, B] with the value function estimates
      wrt. the target policy.
    bootstrap_value: A float32 of shape [B] with the value function estimate at
      time T.
    dist_class: action distribution class for the logits.
    model: backing ModelV2 instance
    behaviour_action_log_probs: precalculated values of the behaviour actions
    clip_rho_threshold: A scalar float32 tensor with the clipping threshold for
      importance weights (rho) when calculating the baseline targets (vs).
      rho^bar in the paper.
    clip_pg_rho_threshold: A scalar float32 tensor with the clipping threshold
      on rho_s in \rho_s \delta log \pi(a|x) (r + \gamma v_{s+1} - V(x_s)).
    name: The name scope that all V-trace operations will be created in.

  Returns:
    A `VTraceFromLogitsReturns` namedtuple with the following fields:
      vs: A float32 tensor of shape [T, B]. Can be used as target to train a
          baseline (V(x_t) - vs_t)^2.
      pg_advantages: A float 32 tensor of shape [T, B]. Can be used as an
        estimate of the advantage in the calculation of policy gradients.
      log_rhos: A float32 tensor of shape [T, B] containing the log importance
        sampling weights (log rhos).
      behaviour_action_log_probs: A float32 tensor of shape [T, B] containing
        behaviour policy action log probabilities (log \mu(a_t)).
      target_action_log_probs: A float32 tensor of shape [T, B] containing
        target policy action probabilities (log \pi(a_t)).
  """

    for i in range(len(behaviour_policy_logits)):
        behaviour_policy_logits[i] = tf.convert_to_tensor(
            behaviour_policy_logits[i], dtype=tf.float32)
        target_policy_logits[i] = tf.convert_to_tensor(
            target_policy_logits[i], dtype=tf.float32)

        # Make sure tensor ranks are as expected.
        # The rest will be checked by from_action_log_probs.
        behaviour_policy_logits[i].shape.assert_has_rank(3)
        target_policy_logits[i].shape.assert_has_rank(3)

    with tf.name_scope(
            name,
            values=[
                behaviour_policy_logits, target_policy_logits, actions,
                discounts, rewards, values, bootstrap_value
            ]):
        target_action_log_probs = multi_log_probs_from_logits_and_actions(
            target_policy_logits, actions, dist_class, model)

        if (len(behaviour_policy_logits) > 1
                or behaviour_action_log_probs is None):
            # can't use precalculated values, recompute them. Note that
            # recomputing won't work well for autoregressive action dists
            # which may have variables not captured by 'logits'
            behaviour_action_log_probs = (
                multi_log_probs_from_logits_and_actions(
                    behaviour_policy_logits, actions, dist_class, model))

        log_rhos = get_log_rhos(target_action_log_probs,
                                behaviour_action_log_probs)

        vtrace_returns = from_importance_weights(
            log_rhos=log_rhos,
            discounts=discounts,
            rewards=rewards,
            values=values,
            bootstrap_value=bootstrap_value,
            clip_rho_threshold=clip_rho_threshold,
            clip_pg_rho_threshold=clip_pg_rho_threshold)

        return VTraceFromLogitsReturns(
            log_rhos=log_rhos,
            behaviour_action_log_probs=behaviour_action_log_probs,
            target_action_log_probs=target_action_log_probs,
            **vtrace_returns._asdict())


def from_importance_weights(log_rhos,
                            discounts,
                            rewards,
                            values,
                            bootstrap_value,
                            clip_rho_threshold=1.0,
                            clip_pg_rho_threshold=1.0,
                            name="vtrace_from_importance_weights"):
    r"""V-trace from log importance weights.

  Calculates V-trace actor critic targets as described in

  "IMPALA: Scalable Distributed Deep-RL with
  Importance Weighted Actor-Learner Architectures"
  by Espeholt, Soyer, Munos et al.

  In the notation used throughout documentation and comments, T refers to the
  time dimension ranging from 0 to T-1. B refers to the batch size. This code
  also supports the case where all tensors have the same number of additional
  dimensions, e.g., `rewards` is [T, B, C], `values` is [T, B, C],
  `bootstrap_value` is [B, C].

  Args:
    log_rhos: A float32 tensor of shape [T, B] representing the
      log importance sampling weights, i.e.
      log(target_policy(a) / behaviour_policy(a)). V-trace performs operations
      on rhos in log-space for numerical stability.
    discounts: A float32 tensor of shape [T, B] with discounts encountered when
      following the behaviour policy.
    rewards: A float32 tensor of shape [T, B] containing rewards generated by
      following the behaviour policy.
    values: A float32 tensor of shape [T, B] with the value function estimates
      wrt. the target policy.
    bootstrap_value: A float32 of shape [B] with the value function estimate at
      time T.
    clip_rho_threshold: A scalar float32 tensor with the clipping threshold for
      importance weights (rho) when calculating the baseline targets (vs).
      rho^bar in the paper. If None, no clipping is applied.
    clip_pg_rho_threshold: A scalar float32 tensor with the clipping threshold
      on rho_s in \rho_s \delta log \pi(a|x) (r + \gamma v_{s+1} - V(x_s)). If
      None, no clipping is applied.
    name: The name scope that all V-trace operations will be created in.

  Returns:
    A VTraceReturns namedtuple (vs, pg_advantages) where:
      vs: A float32 tensor of shape [T, B]. Can be used as target to
        train a baseline (V(x_t) - vs_t)^2.
      pg_advantages: A float32 tensor of shape [T, B]. Can be used as the
        advantage in the calculation of policy gradients.
  """
    log_rhos = tf.convert_to_tensor(log_rhos, dtype=tf.float32)
    discounts = tf.convert_to_tensor(discounts, dtype=tf.float32)
    rewards = tf.convert_to_tensor(rewards, dtype=tf.float32)
    values = tf.convert_to_tensor(values, dtype=tf.float32)
    bootstrap_value = tf.convert_to_tensor(bootstrap_value, dtype=tf.float32)
    if clip_rho_threshold is not None:
        clip_rho_threshold = tf.convert_to_tensor(
            clip_rho_threshold, dtype=tf.float32)
    if clip_pg_rho_threshold is not None:
        clip_pg_rho_threshold = tf.convert_to_tensor(
            clip_pg_rho_threshold, dtype=tf.float32)

    # Make sure tensor ranks are consistent.
    rho_rank = log_rhos.shape.ndims  # Usually 2.
    values.shape.assert_has_rank(rho_rank)
    bootstrap_value.shape.assert_has_rank(rho_rank - 1)
    discounts.shape.assert_has_rank(rho_rank)
    rewards.shape.assert_has_rank(rho_rank)
    if clip_rho_threshold is not None:
        clip_rho_threshold.shape.assert_has_rank(0)
    if clip_pg_rho_threshold is not None:
        clip_pg_rho_threshold.shape.assert_has_rank(0)

    with tf.name_scope(
            name,
            values=[log_rhos, discounts, rewards, values, bootstrap_value]):
        rhos = tf.exp(log_rhos)
        if clip_rho_threshold is not None:
            clipped_rhos = tf.minimum(
                clip_rho_threshold, rhos, name="clipped_rhos")

            tf.summary.histogram("clipped_rhos_1000", tf.minimum(1000.0, rhos))
            tf.summary.scalar(
                "num_of_clipped_rhos",
                tf.reduce_sum(
                    tf.cast(
                        tf.equal(clipped_rhos, clip_rho_threshold), tf.int32)))
            tf.summary.scalar("size_of_clipped_rhos", tf.size(clipped_rhos))
        else:
            clipped_rhos = rhos

        cs = tf.minimum(1.0, rhos, name="cs")
        # Append bootstrapped value to get [v1, ..., v_t+1]
        values_t_plus_1 = tf.concat(
            [values[1:], tf.expand_dims(bootstrap_value, 0)], axis=0)
        deltas = clipped_rhos * (
            rewards + discounts * values_t_plus_1 - values)

        # All sequences are reversed, computation starts from the back.
        sequences = (
            tf.reverse(discounts, axis=[0]),
            tf.reverse(cs, axis=[0]),
            tf.reverse(deltas, axis=[0]),
        )

        # V-trace vs are calculated through a scan from the back to the
        # beginning of the given trajectory.
        def scanfunc(acc, sequence_item):
            discount_t, c_t, delta_t = sequence_item
            return delta_t + discount_t * c_t * acc

        initial_values = tf.zeros_like(bootstrap_value)
        vs_minus_v_xs = tf.scan(
            fn=scanfunc,
            elems=sequences,
            initializer=initial_values,
            parallel_iterations=1,
            back_prop=False,
            name="scan")
        # Reverse the results back to original order.
        vs_minus_v_xs = tf.reverse(vs_minus_v_xs, [0], name="vs_minus_v_xs")

        # Add V(x_s) to get v_s.
        vs = tf.add(vs_minus_v_xs, values, name="vs")

        # Advantage for policy gradient.
        vs_t_plus_1 = tf.concat(
            [vs[1:], tf.expand_dims(bootstrap_value, 0)], axis=0)
        if clip_pg_rho_threshold is not None:
            clipped_pg_rhos = tf.minimum(
                clip_pg_rho_threshold, rhos, name="clipped_pg_rhos")
        else:
            clipped_pg_rhos = rhos
        pg_advantages = (
            clipped_pg_rhos * (rewards + discounts * vs_t_plus_1 - values))

        # Make sure no gradients backpropagated through the returned values.
        return VTraceReturns(
            vs=tf.stop_gradient(vs),
            pg_advantages=tf.stop_gradient(pg_advantages))


def get_log_rhos(target_action_log_probs, behaviour_action_log_probs):
    """With the selected log_probs for multi-discrete actions of behaviour
    and target policies we compute the log_rhos for calculating the vtrace."""
    t = tf.stack(target_action_log_probs)
    b = tf.stack(behaviour_action_log_probs)
    log_rhos = tf.reduce_sum(t - b, axis=0)
    return log_rhos