您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
314 行
13 KiB
314 行
13 KiB
import logging
|
|
from typing import Dict, Any, Optional
|
|
import numpy as np
|
|
from mlagents.tf_utils import tf
|
|
|
|
from mlagents.envs.timers import timed
|
|
from mlagents.trainers.brain import BrainInfo, BrainParameters
|
|
from mlagents.trainers.models import EncoderType, LearningRateSchedule
|
|
from mlagents.trainers.sac.models import SACModel
|
|
from mlagents.trainers.tf_policy import TFPolicy
|
|
from mlagents.trainers.components.reward_signals.reward_signal_factory import (
|
|
create_reward_signal,
|
|
)
|
|
from mlagents.trainers.components.reward_signals import RewardSignal
|
|
from mlagents.trainers.components.bc.module import BCModule
|
|
|
|
logger = logging.getLogger("mlagents.trainers")
|
|
|
|
|
|
class SACPolicy(TFPolicy):
|
|
def __init__(
|
|
self,
|
|
seed: int,
|
|
brain: BrainParameters,
|
|
trainer_params: Dict[str, Any],
|
|
is_training: bool,
|
|
load: bool,
|
|
) -> None:
|
|
"""
|
|
Policy for Proximal Policy Optimization Networks.
|
|
:param seed: Random seed.
|
|
:param brain: Assigned Brain object.
|
|
:param trainer_params: Defined training parameters.
|
|
:param is_training: Whether the model should be trained.
|
|
:param load: Whether a pre-trained model will be loaded or a new one created.
|
|
"""
|
|
super().__init__(seed, brain, trainer_params)
|
|
|
|
reward_signal_configs = {}
|
|
for key, rsignal in trainer_params["reward_signals"].items():
|
|
if type(rsignal) is dict:
|
|
reward_signal_configs[key] = rsignal
|
|
|
|
self.inference_dict: Dict[str, tf.Tensor] = {}
|
|
self.update_dict: Dict[str, tf.Tensor] = {}
|
|
self.create_model(
|
|
brain, trainer_params, reward_signal_configs, is_training, load, seed
|
|
)
|
|
self.create_reward_signals(reward_signal_configs)
|
|
|
|
self.stats_name_to_update_name = {
|
|
"Losses/Value Loss": "value_loss",
|
|
"Losses/Policy Loss": "policy_loss",
|
|
"Losses/Q1 Loss": "q1_loss",
|
|
"Losses/Q2 Loss": "q2_loss",
|
|
"Policy/Entropy Coeff": "entropy_coef",
|
|
}
|
|
|
|
with self.graph.as_default():
|
|
# Create pretrainer if needed
|
|
self.bc_module: Optional[BCModule] = None
|
|
if "behavioral_cloning" in trainer_params:
|
|
BCModule.check_config(trainer_params["behavioral_cloning"])
|
|
self.bc_module = BCModule(
|
|
self,
|
|
policy_learning_rate=trainer_params["learning_rate"],
|
|
default_batch_size=trainer_params["batch_size"],
|
|
default_num_epoch=1,
|
|
samples_per_update=trainer_params["batch_size"],
|
|
**trainer_params["behavioral_cloning"],
|
|
)
|
|
# SAC-specific setting - we don't want to do a whole epoch each update!
|
|
if "samples_per_update" in trainer_params["behavioral_cloning"]:
|
|
logger.warning(
|
|
"Pretraining: Samples Per Update is not a valid setting for SAC."
|
|
)
|
|
self.bc_module.samples_per_update = 1
|
|
|
|
if load:
|
|
self._load_graph()
|
|
else:
|
|
self._initialize_graph()
|
|
self.sess.run(self.model.target_init_op)
|
|
|
|
# Disable terminal states for certain reward signals to avoid survivor bias
|
|
for name, reward_signal in self.reward_signals.items():
|
|
if not reward_signal.use_terminal_states:
|
|
self.sess.run(self.model.disable_use_dones[name])
|
|
|
|
def create_model(
|
|
self,
|
|
brain: BrainParameters,
|
|
trainer_params: Dict[str, Any],
|
|
reward_signal_configs: Dict[str, Any],
|
|
is_training: bool,
|
|
load: bool,
|
|
seed: int,
|
|
) -> None:
|
|
with self.graph.as_default():
|
|
self.model = SACModel(
|
|
brain,
|
|
lr=float(trainer_params["learning_rate"]),
|
|
lr_schedule=LearningRateSchedule(
|
|
trainer_params.get("learning_rate_schedule", "constant")
|
|
),
|
|
h_size=int(trainer_params["hidden_units"]),
|
|
init_entcoef=float(trainer_params["init_entcoef"]),
|
|
max_step=float(trainer_params["max_steps"]),
|
|
normalize=trainer_params["normalize"],
|
|
use_recurrent=trainer_params["use_recurrent"],
|
|
num_layers=int(trainer_params["num_layers"]),
|
|
m_size=self.m_size,
|
|
seed=seed,
|
|
stream_names=list(reward_signal_configs.keys()),
|
|
tau=float(trainer_params["tau"]),
|
|
gammas=[_val["gamma"] for _val in reward_signal_configs.values()],
|
|
vis_encode_type=EncoderType(
|
|
trainer_params.get("vis_encode_type", "simple")
|
|
),
|
|
)
|
|
self.model.create_sac_optimizers()
|
|
|
|
self.inference_dict.update(
|
|
{
|
|
"action": self.model.output,
|
|
"log_probs": self.model.all_log_probs,
|
|
"value_heads": self.model.value_heads,
|
|
"value": self.model.value,
|
|
"entropy": self.model.entropy,
|
|
"learning_rate": self.model.learning_rate,
|
|
}
|
|
)
|
|
if self.use_continuous_act:
|
|
self.inference_dict["pre_action"] = self.model.output_pre
|
|
if self.use_recurrent:
|
|
self.inference_dict["memory_out"] = self.model.memory_out
|
|
|
|
self.update_dict.update(
|
|
{
|
|
"value_loss": self.model.total_value_loss,
|
|
"policy_loss": self.model.policy_loss,
|
|
"q1_loss": self.model.q1_loss,
|
|
"q2_loss": self.model.q2_loss,
|
|
"entropy_coef": self.model.ent_coef,
|
|
"entropy": self.model.entropy,
|
|
"update_batch": self.model.update_batch_policy,
|
|
"update_value": self.model.update_batch_value,
|
|
"update_entropy": self.model.update_batch_entropy,
|
|
}
|
|
)
|
|
|
|
def create_reward_signals(self, reward_signal_configs: Dict[str, Any]) -> None:
|
|
"""
|
|
Create reward signals
|
|
:param reward_signal_configs: Reward signal config.
|
|
"""
|
|
self.reward_signals: Dict[str, RewardSignal] = {}
|
|
with self.graph.as_default():
|
|
# Create reward signals
|
|
for reward_signal, config in reward_signal_configs.items():
|
|
if type(config) is dict:
|
|
self.reward_signals[reward_signal] = create_reward_signal(
|
|
self, self.model, reward_signal, config
|
|
)
|
|
|
|
def evaluate(self, brain_info: BrainInfo) -> Dict[str, np.ndarray]:
|
|
"""
|
|
Evaluates policy for the agent experiences provided.
|
|
:param brain_info: BrainInfo object containing inputs.
|
|
:return: Outputs from network as defined by self.inference_dict.
|
|
"""
|
|
feed_dict = {
|
|
self.model.batch_size: len(brain_info.vector_observations),
|
|
self.model.sequence_length: 1,
|
|
}
|
|
if self.use_recurrent:
|
|
if not self.use_continuous_act:
|
|
feed_dict[self.model.prev_action] = self.retrieve_previous_action(
|
|
brain_info.agents
|
|
)
|
|
feed_dict[self.model.memory_in] = self.retrieve_memories(brain_info.agents)
|
|
|
|
feed_dict = self.fill_eval_dict(feed_dict, brain_info)
|
|
run_out = self._execute_model(feed_dict, self.inference_dict)
|
|
return run_out
|
|
|
|
@timed
|
|
def update(
|
|
self, mini_batch: Dict[str, Any], num_sequences: int
|
|
) -> Dict[str, float]:
|
|
"""
|
|
Updates model using buffer.
|
|
:param num_sequences: Number of trajectories in batch.
|
|
:param mini_batch: Experience batch.
|
|
:param update_target: Whether or not to update target value network
|
|
:param reward_signal_mini_batches: Minibatches to use for updating the reward signals,
|
|
indexed by name. If none, don't update the reward signals.
|
|
:return: Output from update process.
|
|
"""
|
|
feed_dict = self.construct_feed_dict(self.model, mini_batch, num_sequences)
|
|
stats_needed = self.stats_name_to_update_name
|
|
update_stats: Dict[str, float] = {}
|
|
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]
|
|
# Update target network. By default, target update happens at every policy update.
|
|
self.sess.run(self.model.target_update_op)
|
|
return update_stats
|
|
|
|
def update_reward_signals(
|
|
self, reward_signal_minibatches: Dict[str, Dict], num_sequences: int
|
|
) -> Dict[str, float]:
|
|
"""
|
|
Only update the reward signals.
|
|
:param reward_signal_mini_batches: Minibatches to use for updating the reward signals,
|
|
indexed by name. If none, don't update the reward signals.
|
|
"""
|
|
# Collect feed dicts for all reward signals.
|
|
feed_dict: Dict[tf.Tensor, Any] = {}
|
|
update_dict: Dict[str, tf.Tensor] = {}
|
|
update_stats: Dict[str, float] = {}
|
|
stats_needed: Dict[str, str] = {}
|
|
if reward_signal_minibatches:
|
|
self.add_reward_signal_dicts(
|
|
feed_dict,
|
|
update_dict,
|
|
stats_needed,
|
|
reward_signal_minibatches,
|
|
num_sequences,
|
|
)
|
|
update_vals = self._execute_model(feed_dict, update_dict)
|
|
for stat_name, update_name in stats_needed.items():
|
|
update_stats[stat_name] = update_vals[update_name]
|
|
return update_stats
|
|
|
|
def add_reward_signal_dicts(
|
|
self,
|
|
feed_dict: Dict[tf.Tensor, Any],
|
|
update_dict: Dict[str, tf.Tensor],
|
|
stats_needed: Dict[str, str],
|
|
reward_signal_minibatches: Dict[str, Dict],
|
|
num_sequences: int,
|
|
) -> None:
|
|
"""
|
|
Adds the items needed for reward signal updates to the feed_dict and stats_needed dict.
|
|
:param feed_dict: Feed dict needed update
|
|
:param update_dit: Update dict that needs update
|
|
:param stats_needed: Stats needed to get from the update.
|
|
:param reward_signal_minibatches: Minibatches to use for updating the reward signals,
|
|
indexed by name.
|
|
"""
|
|
for name, r_mini_batch in reward_signal_minibatches.items():
|
|
feed_dict.update(
|
|
self.reward_signals[name].prepare_update(
|
|
self.model, r_mini_batch, num_sequences
|
|
)
|
|
)
|
|
update_dict.update(self.reward_signals[name].update_dict)
|
|
stats_needed.update(self.reward_signals[name].stats_name_to_update_name)
|
|
|
|
def construct_feed_dict(
|
|
self, model: SACModel, mini_batch: Dict[str, Any], num_sequences: int
|
|
) -> Dict[tf.Tensor, Any]:
|
|
"""
|
|
Builds the feed dict for updating the SAC model.
|
|
:param model: The model to update. May be different when, e.g. using multi-GPU.
|
|
:param mini_batch: Mini-batch to use to update.
|
|
:param num_sequences: Number of LSTM sequences in mini_batch.
|
|
"""
|
|
feed_dict = {
|
|
self.model.batch_size: num_sequences,
|
|
self.model.sequence_length: self.sequence_length,
|
|
self.model.next_sequence_length: self.sequence_length,
|
|
self.model.mask_input: mini_batch["masks"],
|
|
}
|
|
for name in self.reward_signals:
|
|
feed_dict[model.rewards_holders[name]] = mini_batch[
|
|
"{}_rewards".format(name)
|
|
]
|
|
|
|
if self.use_continuous_act:
|
|
feed_dict[model.action_holder] = mini_batch["actions"]
|
|
else:
|
|
feed_dict[model.action_holder] = mini_batch["actions"]
|
|
if self.use_recurrent:
|
|
feed_dict[model.prev_action] = mini_batch["prev_action"]
|
|
feed_dict[model.action_masks] = mini_batch["action_mask"]
|
|
if self.use_vec_obs:
|
|
feed_dict[model.vector_in] = mini_batch["vector_obs"]
|
|
feed_dict[model.next_vector_in] = mini_batch["next_vector_in"]
|
|
if self.model.vis_obs_size > 0:
|
|
for i, _ in enumerate(model.visual_in):
|
|
_obs = mini_batch["visual_obs%d" % i]
|
|
feed_dict[model.visual_in[i]] = _obs
|
|
for i, _ in enumerate(model.next_visual_in):
|
|
_obs = mini_batch["next_visual_obs%d" % i]
|
|
feed_dict[model.next_visual_in[i]] = _obs
|
|
if self.use_recurrent:
|
|
mem_in = [
|
|
mini_batch["memory"][i]
|
|
for i in range(0, len(mini_batch["memory"]), self.sequence_length)
|
|
]
|
|
# LSTM shouldn't have sequence length <1, but stop it from going out of the index if true.
|
|
offset = 1 if self.sequence_length > 1 else 0
|
|
next_mem_in = [
|
|
mini_batch["memory"][i][
|
|
: self.m_size // 4
|
|
] # only pass value part of memory to target network
|
|
for i in range(offset, len(mini_batch["memory"]), self.sequence_length)
|
|
]
|
|
feed_dict[model.memory_in] = mem_in
|
|
feed_dict[model.next_memory_in] = next_mem_in
|
|
feed_dict[model.dones_holder] = mini_batch["done"]
|
|
return feed_dict
|