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from typing import Optional, Any, Dict, cast
import numpy as np
from mlagents.tf_utils import tf
from mlagents_envs.timers import timed
from mlagents.trainers.tf.models import ModelUtils, EncoderType
from mlagents.trainers.policy.tf_policy import TFPolicy
from mlagents.trainers.optimizer.tf_optimizer import TFOptimizer
from mlagents.trainers.buffer import AgentBuffer
from mlagents.trainers.settings import TrainerSettings, PPOSettings
class PPOOptimizer(TFOptimizer):
def __init__(self, policy: TFPolicy, trainer_params: TrainerSettings):
"""
Takes a Policy and a Dict of trainer parameters and creates an Optimizer around the policy.
The PPO optimizer has a value estimator and a loss function.
:param policy: A TFPolicy object that will be updated by this PPO Optimizer.
:param trainer_params: Trainer parameters dictionary that specifies the properties of the trainer.
"""
# Create the graph here to give more granular control of the TF graph to the Optimizer.
policy.create_tf_graph()
with policy.graph.as_default():
with tf.variable_scope("optimizer/"):
super().__init__(policy, trainer_params)
hyperparameters: PPOSettings = cast(
PPOSettings, trainer_params.hyperparameters
)
lr = float(hyperparameters.learning_rate)
self._schedule = hyperparameters.learning_rate_schedule
epsilon = float(hyperparameters.epsilon)
beta = float(hyperparameters.beta)
max_step = float(trainer_params.max_steps)
policy_network_settings = policy.network_settings
h_size = int(policy_network_settings.hidden_units)
num_layers = policy_network_settings.num_layers
vis_encode_type = policy_network_settings.vis_encode_type
self.burn_in_ratio = 0.0
self.stream_names = list(self.reward_signals.keys())
self.tf_optimizer: Optional[tf.train.AdamOptimizer] = None
self.grads = None
self.update_batch: Optional[tf.Operation] = None
self.stats_name_to_update_name = {
"Losses/Value Loss": "value_loss",
"Losses/Policy Loss": "policy_loss",
"Policy/Learning Rate": "learning_rate",
"Policy/Epsilon": "decay_epsilon",
"Policy/Beta": "decay_beta",
}
if self.policy.use_recurrent:
self.m_size = self.policy.m_size
self.memory_in = tf.placeholder(
shape=[None, self.m_size],
dtype=tf.float32,
name="recurrent_value_in",
)
if num_layers < 1:
num_layers = 1
if policy.use_continuous_act:
self._create_cc_critic(h_size, num_layers, vis_encode_type)
else:
self._create_dc_critic(h_size, num_layers, vis_encode_type)
self.learning_rate = ModelUtils.create_schedule(
self._schedule,
lr,
self.policy.global_step,
int(max_step),
min_value=1e-10,
)
self._create_losses(
self.policy.total_log_probs,
self.old_log_probs,
self.value_heads,
self.policy.entropy,
beta,
epsilon,
lr,
max_step,
)
self._create_ppo_optimizer_ops()
self.update_dict.update(
{
"value_loss": self.value_loss,
"policy_loss": self.abs_policy_loss,
"update_batch": self.update_batch,
"learning_rate": self.learning_rate,
"decay_epsilon": self.decay_epsilon,
"decay_beta": self.decay_beta,
}
)
self.policy.initialize_or_load()
def _create_cc_critic(
self, h_size: int, num_layers: int, vis_encode_type: EncoderType
) -> None:
"""
Creates Continuous control critic (value) network.
:param h_size: Size of hidden linear layers.
:param num_layers: Number of hidden linear layers.
:param vis_encode_type: The type of visual encoder to use.
"""
hidden_stream = ModelUtils.create_observation_streams(
self.policy.visual_in,
self.policy.processed_vector_in,
1,
h_size,
num_layers,
vis_encode_type,
)[0]
if self.policy.use_recurrent:
hidden_value, memory_value_out = ModelUtils.create_recurrent_encoder(
hidden_stream,
self.memory_in,
self.policy.sequence_length_ph,
name="lstm_value",
)
self.memory_out = memory_value_out
else:
hidden_value = hidden_stream
self.value_heads, self.value = ModelUtils.create_value_heads(
self.stream_names, hidden_value
)
self.all_old_log_probs = tf.placeholder(
shape=[None, sum(self.policy.act_size)],
dtype=tf.float32,
name="old_probabilities",
)
self.old_log_probs = tf.reduce_sum(
(tf.identity(self.all_old_log_probs)), axis=1, keepdims=True
)
def _create_dc_critic(
self, h_size: int, num_layers: int, vis_encode_type: EncoderType
) -> None:
"""
Creates Discrete control critic (value) network.
:param h_size: Size of hidden linear layers.
:param num_layers: Number of hidden linear layers.
:param vis_encode_type: The type of visual encoder to use.
"""
hidden_stream = ModelUtils.create_observation_streams(
self.policy.visual_in,
self.policy.processed_vector_in,
1,
h_size,
num_layers,
vis_encode_type,
)[0]
if self.policy.use_recurrent:
hidden_value, memory_value_out = ModelUtils.create_recurrent_encoder(
hidden_stream,
self.memory_in,
self.policy.sequence_length_ph,
name="lstm_value",
)
self.memory_out = memory_value_out
else:
hidden_value = hidden_stream
self.value_heads, self.value = ModelUtils.create_value_heads(
self.stream_names, hidden_value
)
self.all_old_log_probs = tf.placeholder(
shape=[None, sum(self.policy.act_size)],
dtype=tf.float32,
name="old_probabilities",
)
# Break old log probs into separate branches
old_log_prob_branches = ModelUtils.break_into_branches(
self.all_old_log_probs, self.policy.act_size
)
_, _, old_normalized_logits = ModelUtils.create_discrete_action_masking_layer(
old_log_prob_branches, self.policy.action_masks, self.policy.act_size
)
action_idx = [0] + list(np.cumsum(self.policy.act_size))
self.old_log_probs = tf.reduce_sum(
(
tf.stack(
[
-tf.nn.softmax_cross_entropy_with_logits_v2(
labels=self.policy.selected_actions[
:, action_idx[i] : action_idx[i + 1]
],
logits=old_normalized_logits[
:, action_idx[i] : action_idx[i + 1]
],
)
for i in range(len(self.policy.act_size))
],
axis=1,
)
),
axis=1,
keepdims=True,
)
def _create_losses(
self, probs, old_probs, value_heads, entropy, beta, epsilon, lr, max_step
):
"""
Creates training-specific Tensorflow ops for PPO models.
:param probs: Current policy probabilities
:param old_probs: Past policy probabilities
:param value_heads: Value estimate tensors from each value stream
:param beta: Entropy regularization strength
:param entropy: Current policy entropy
:param epsilon: Value for policy-divergence threshold
:param lr: Learning rate
:param max_step: Total number of training steps.
"""
self.returns_holders = {}
self.old_values = {}
for name in value_heads.keys():
returns_holder = tf.placeholder(
shape=[None], dtype=tf.float32, name=f"{name}_returns"
)
old_value = tf.placeholder(
shape=[None], dtype=tf.float32, name=f"{name}_value_estimate"
)
self.returns_holders[name] = returns_holder
self.old_values[name] = old_value
self.advantage = tf.placeholder(
shape=[None], dtype=tf.float32, name="advantages"
)
advantage = tf.expand_dims(self.advantage, -1)
self.decay_epsilon = ModelUtils.create_schedule(
self._schedule, epsilon, self.policy.global_step, max_step, min_value=0.1
)
self.decay_beta = ModelUtils.create_schedule(
self._schedule, beta, self.policy.global_step, max_step, min_value=1e-5
)
value_losses = []
for name, head in value_heads.items():
clipped_value_estimate = self.old_values[name] + tf.clip_by_value(
tf.reduce_sum(head, axis=1) - self.old_values[name],
-self.decay_epsilon,
self.decay_epsilon,
)
v_opt_a = tf.squared_difference(
self.returns_holders[name], tf.reduce_sum(head, axis=1)
)
v_opt_b = tf.squared_difference(
self.returns_holders[name], clipped_value_estimate
)
value_loss = tf.reduce_mean(
tf.dynamic_partition(tf.maximum(v_opt_a, v_opt_b), self.policy.mask, 2)[
1
]
)
value_losses.append(value_loss)
self.value_loss = tf.reduce_mean(value_losses)
r_theta = tf.exp(probs - old_probs)
p_opt_a = r_theta * advantage
p_opt_b = (
tf.clip_by_value(
r_theta, 1.0 - self.decay_epsilon, 1.0 + self.decay_epsilon
)
* advantage
)
self.policy_loss = -tf.reduce_mean(
tf.dynamic_partition(tf.minimum(p_opt_a, p_opt_b), self.policy.mask, 2)[1]
)
# For cleaner stats reporting
self.abs_policy_loss = tf.abs(self.policy_loss)
self.loss = (
self.policy_loss
+ 0.5 * self.value_loss
- self.decay_beta
* tf.reduce_mean(tf.dynamic_partition(entropy, self.policy.mask, 2)[1])
)
def _create_ppo_optimizer_ops(self):
self.tf_optimizer = self.create_optimizer_op(self.learning_rate)
self.grads = self.tf_optimizer.compute_gradients(self.loss)
self.update_batch = self.tf_optimizer.minimize(self.loss)
@timed
def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
"""
Performs update on model.
:param mini_batch: Batch of experiences.
:param num_sequences: Number of sequences to process.
:return: Results of update.
"""
feed_dict = self._construct_feed_dict(batch, num_sequences)
stats_needed = self.stats_name_to_update_name
update_stats = {}
# Collect feed dicts for all reward signals.
for _, reward_signal in self.reward_signals.items():
feed_dict.update(
reward_signal.prepare_update(self.policy, batch, num_sequences)
)
stats_needed.update(reward_signal.stats_name_to_update_name)
update_vals = self._execute_model(feed_dict, self.update_dict)
for stat_name, update_name in stats_needed.items():
update_stats[stat_name] = update_vals[update_name]
return update_stats
def _construct_feed_dict(
self, mini_batch: AgentBuffer, num_sequences: int
) -> Dict[tf.Tensor, Any]:
# Do an optional burn-in for memories
num_burn_in = int(self.burn_in_ratio * self.policy.sequence_length)
burn_in_mask = np.ones((self.policy.sequence_length), dtype=np.float32)
burn_in_mask[range(0, num_burn_in)] = 0
burn_in_mask = np.tile(burn_in_mask, num_sequences)
feed_dict = {
self.policy.batch_size_ph: num_sequences,
self.policy.sequence_length_ph: self.policy.sequence_length,
self.policy.mask_input: mini_batch["masks"] * burn_in_mask,
self.advantage: mini_batch["advantages"],
self.all_old_log_probs: mini_batch["action_probs"],
}
for name in self.reward_signals:
feed_dict[self.returns_holders[name]] = mini_batch[f"{name}_returns"]
feed_dict[self.old_values[name]] = mini_batch[f"{name}_value_estimates"]
if self.policy.output_pre is not None and "actions_pre" in mini_batch:
feed_dict[self.policy.output_pre] = mini_batch["actions_pre"]
else:
feed_dict[self.policy.output] = mini_batch["actions"]
if self.policy.use_recurrent:
feed_dict[self.policy.prev_action] = mini_batch["prev_action"]
feed_dict[self.policy.action_masks] = mini_batch["action_mask"]
if "vector_obs" in mini_batch:
feed_dict[self.policy.vector_in] = mini_batch["vector_obs"]
if self.policy.vis_obs_size > 0:
for i, _ in enumerate(self.policy.visual_in):
feed_dict[self.policy.visual_in[i]] = mini_batch["visual_obs%d" % i]
if self.policy.use_recurrent:
feed_dict[self.policy.memory_in] = [
mini_batch["memory"][i]
for i in range(
0, len(mini_batch["memory"]), self.policy.sequence_length
)
]
feed_dict[self.memory_in] = self._make_zero_mem(
self.m_size, mini_batch.num_experiences
)
return feed_dict