"""Adapted from A3CTFPolicy to add V-trace. Keep in sync with changes to A3CTFPolicy and VtraceSurrogatePolicy.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import logging import gym import ray from ray.rllib.agents.impala import vtrace from ray.rllib.models.tf.tf_action_dist import Categorical from ray.rllib.policy.sample_batch import SampleBatch from ray.rllib.policy.tf_policy_template import build_tf_policy from ray.rllib.policy.tf_policy import LearningRateSchedule, \ EntropyCoeffSchedule from ray.rllib.utils.explained_variance import explained_variance from ray.rllib.utils import try_import_tf tf = try_import_tf() logger = logging.getLogger(__name__) BEHAVIOUR_LOGITS = "behaviour_logits" class VTraceLoss(object): def __init__(self, actions, actions_logp, actions_entropy, dones, behaviour_logits, target_logits, discount, rewards, values, bootstrap_value, dist_class, valid_mask, config, vf_loss_coeff=0.5, entropy_coeff=0.01, clip_rho_threshold=1.0, clip_pg_rho_threshold=1.0): """Policy gradient loss with vtrace importance weighting. VTraceLoss takes tensors of shape [T, B, ...], where `B` is the batch_size. The reason we need to know `B` is for V-trace to properly handle episode cut boundaries. Args: actions: An int|float32 tensor of shape [T, B, ACTION_SPACE]. actions_logp: A float32 tensor of shape [T, B]. actions_entropy: A float32 tensor of shape [T, B]. dones: A bool tensor of shape [T, B]. behaviour_logits: A list with length of ACTION_SPACE of float32 tensors of shapes [T, B, ACTION_SPACE[0]], ..., [T, B, ACTION_SPACE[-1]] target_logits: A list with length of ACTION_SPACE of float32 tensors of shapes [T, B, ACTION_SPACE[0]], ..., [T, B, ACTION_SPACE[-1]] discount: A float32 scalar. rewards: A float32 tensor of shape [T, B]. values: A float32 tensor of shape [T, B]. bootstrap_value: A float32 tensor of shape [B]. dist_class: action distribution class for logits. valid_mask: A bool tensor of valid RNN input elements (#2992). config: Trainer config dict. """ # Compute vtrace on the CPU for better perf. with tf.device("/cpu:0"): self.vtrace_returns = vtrace.multi_from_logits( behaviour_policy_logits=behaviour_logits, target_policy_logits=target_logits, actions=tf.unstack(actions, axis=2), discounts=tf.to_float(~dones) * discount, rewards=rewards, values=values, bootstrap_value=bootstrap_value, dist_class=dist_class, clip_rho_threshold=tf.cast(clip_rho_threshold, tf.float32), clip_pg_rho_threshold=tf.cast(clip_pg_rho_threshold, tf.float32), config=config) self.value_targets = self.vtrace_returns.vs # The policy gradients loss self.pi_loss = -tf.reduce_sum( tf.boolean_mask(actions_logp * self.vtrace_returns.pg_advantages, valid_mask)) # The baseline loss delta = tf.boolean_mask(values - self.vtrace_returns.vs, valid_mask) self.vf_loss = 0.5 * tf.reduce_sum(tf.square(delta)) # The entropy loss self.entropy = tf.reduce_sum( tf.boolean_mask(actions_entropy, valid_mask)) # The summed weighted loss self.total_loss = (self.pi_loss + self.vf_loss * vf_loss_coeff - self.entropy * entropy_coeff) def _make_time_major(policy, tensor, drop_last=False): """Swaps batch and trajectory axis. Arguments: policy: Policy reference tensor: A tensor or list of tensors to reshape. drop_last: A bool indicating whether to drop the last trajectory item. Returns: res: A tensor with swapped axes or a list of tensors with swapped axes. """ if isinstance(tensor, list): return [_make_time_major(policy, t, drop_last) for t in tensor] if policy.state_in: B = tf.shape(policy.seq_lens)[0] T = tf.shape(tensor)[0] // B else: # Important: chop the tensor into batches at known episode cut # boundaries. TODO(ekl) this is kind of a hack T = policy.config["sample_batch_size"] B = tf.shape(tensor)[0] // T rs = tf.reshape(tensor, tf.concat([[B, T], tf.shape(tensor)[1:]], axis=0)) # swap B and T axes res = tf.transpose( rs, [1, 0] + list(range(2, 1 + int(tf.shape(tensor).shape[0])))) if drop_last: return res[:-1] return res def build_vtrace_loss(policy, batch_tensors): if isinstance(policy.action_space, gym.spaces.Discrete): is_multidiscrete = False output_hidden_shape = [policy.action_space.n] elif isinstance(policy.action_space, gym.spaces.multi_discrete.MultiDiscrete): is_multidiscrete = True output_hidden_shape = policy.action_space.nvec.astype(np.int32) else: is_multidiscrete = False output_hidden_shape = 1 def make_time_major(*args, **kw): return _make_time_major(policy, *args, **kw) actions = batch_tensors[SampleBatch.ACTIONS] dones = batch_tensors[SampleBatch.DONES] rewards = batch_tensors[SampleBatch.REWARDS] behaviour_logits = batch_tensors[BEHAVIOUR_LOGITS] unpacked_behaviour_logits = tf.split( behaviour_logits, output_hidden_shape, axis=1) unpacked_outputs = tf.split(policy.model_out, output_hidden_shape, axis=1) action_dist = policy.action_dist values = policy.value_function if policy.state_in: max_seq_len = tf.reduce_max(policy.seq_lens) - 1 mask = tf.sequence_mask(policy.seq_lens, max_seq_len) mask = tf.reshape(mask, [-1]) else: mask = tf.ones_like(rewards) # Prepare actions for loss loss_actions = actions if is_multidiscrete else tf.expand_dims( actions, axis=1) # Inputs are reshaped from [B * T] => [T - 1, B] for V-trace calc. policy.loss = VTraceLoss( actions=make_time_major(loss_actions, drop_last=True), actions_logp=make_time_major( action_dist.logp(actions), drop_last=True), actions_entropy=make_time_major( action_dist.multi_entropy(), drop_last=True), dones=make_time_major(dones, drop_last=True), behaviour_logits=make_time_major( unpacked_behaviour_logits, drop_last=True), target_logits=make_time_major(unpacked_outputs, drop_last=True), discount=policy.config["gamma"], rewards=make_time_major(rewards, drop_last=True), values=make_time_major(values, drop_last=True), bootstrap_value=make_time_major(values)[-1], dist_class=Categorical if is_multidiscrete else policy.dist_class, valid_mask=make_time_major(mask, drop_last=True), config=policy.config, vf_loss_coeff=policy.config["vf_loss_coeff"], entropy_coeff=policy.entropy_coeff, clip_rho_threshold=policy.config["vtrace_clip_rho_threshold"], clip_pg_rho_threshold=policy.config["vtrace_clip_pg_rho_threshold"]) return policy.loss.total_loss def stats(policy, batch_tensors): values_batched = _make_time_major( policy, policy.value_function, drop_last=policy.config["vtrace"]) return { "cur_lr": tf.cast(policy.cur_lr, tf.float64), "policy_loss": policy.loss.pi_loss, "entropy": policy.loss.entropy, "entropy_coeff": tf.cast(policy.entropy_coeff, tf.float64), "var_gnorm": tf.global_norm(policy.var_list), "vf_loss": policy.loss.vf_loss, "vf_explained_var": explained_variance( tf.reshape(policy.loss.value_targets, [-1]), tf.reshape(values_batched, [-1])), } def grad_stats(policy, grads): return { "grad_gnorm": tf.global_norm(grads), } def postprocess_trajectory(policy, sample_batch, other_agent_batches=None, episode=None): # not used, so save some bandwidth del sample_batch.data[SampleBatch.NEXT_OBS] return sample_batch def add_behaviour_logits(policy): return {BEHAVIOUR_LOGITS: policy.model_out} def validate_config(policy, obs_space, action_space, config): if config["vtrace"]: assert config["batch_mode"] == "truncate_episodes", \ "Must use `truncate_episodes` batch mode with V-trace." def choose_optimizer(policy, config): if policy.config["opt_type"] == "adam": return tf.train.AdamOptimizer(policy.cur_lr) else: return tf.train.RMSPropOptimizer(policy.cur_lr, config["decay"], config["momentum"], config["epsilon"]) def clip_gradients(policy, optimizer, loss): grads = tf.gradients(loss, policy.var_list) policy.grads, _ = tf.clip_by_global_norm(grads, policy.config["grad_clip"]) clipped_grads = list(zip(policy.grads, policy.var_list)) return clipped_grads class ValueNetworkMixin(object): def __init__(self): self.value_function = self.model.value_function() self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, tf.get_variable_scope().name) def value(self, ob, *args): feed_dict = { self.get_placeholder(SampleBatch.CUR_OBS): [ob], self.seq_lens: [1] } assert len(args) == len(self.state_in), \ (args, self.state_in) for k, v in zip(self.state_in, args): feed_dict[k] = v vf = self.get_session().run(self.value_function, feed_dict) return vf[0] def setup_mixins(policy, obs_space, action_space, config): LearningRateSchedule.__init__(policy, config["lr"], config["lr_schedule"]) EntropyCoeffSchedule.__init__(policy, config["entropy_coeff"], config["entropy_coeff_schedule"]) ValueNetworkMixin.__init__(policy) VTraceTFPolicy = build_tf_policy( name="VTraceTFPolicy", get_default_config=lambda: ray.rllib.agents.impala.impala.DEFAULT_CONFIG, loss_fn=build_vtrace_loss, stats_fn=stats, grad_stats_fn=grad_stats, postprocess_fn=postprocess_trajectory, optimizer_fn=choose_optimizer, gradients_fn=clip_gradients, extra_action_fetches_fn=add_behaviour_logits, before_init=validate_config, before_loss_init=setup_mixins, mixins=[LearningRateSchedule, EntropyCoeffSchedule, ValueNetworkMixin], get_batch_divisibility_req=lambda p: p.config["sample_batch_size"])