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
223 行
8.6 KiB
223 行
8.6 KiB
import logging
|
|
import numpy as np
|
|
from typing import Any, Dict, Optional
|
|
|
|
from mlagents.tf_utils import tf
|
|
|
|
from mlagents_envs.timers import timed
|
|
from mlagents.trainers.brain import BrainParameters
|
|
from mlagents.trainers.models import EncoderType, LearningRateSchedule
|
|
from mlagents.trainers.ppo.models import PPOModel
|
|
from mlagents.trainers.tf_policy import TFPolicy
|
|
from mlagents.trainers.components.reward_signals.reward_signal_factory import (
|
|
create_reward_signal,
|
|
)
|
|
from mlagents.trainers.components.bc.module import BCModule
|
|
|
|
logger = logging.getLogger("mlagents.trainers")
|
|
|
|
|
|
class PPOPolicy(TFPolicy):
|
|
def __init__(
|
|
self,
|
|
seed: int,
|
|
brain: BrainParameters,
|
|
trainer_params: Dict[str, Any],
|
|
is_training: bool,
|
|
load: bool,
|
|
):
|
|
"""
|
|
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 = trainer_params["reward_signals"]
|
|
self.inference_dict: Dict[str, tf.Tensor] = {}
|
|
self.update_dict: Dict[str, tf.Tensor] = {}
|
|
self.stats_name_to_update_name = {
|
|
"Losses/Value Loss": "value_loss",
|
|
"Losses/Policy Loss": "policy_loss",
|
|
}
|
|
|
|
self.create_model(
|
|
brain, trainer_params, reward_signal_configs, is_training, load, seed
|
|
)
|
|
self.create_reward_signals(reward_signal_configs)
|
|
|
|
with self.graph.as_default():
|
|
self.bc_module: Optional[BCModule] = None
|
|
# Create pretrainer if needed
|
|
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=3,
|
|
**trainer_params["behavioral_cloning"],
|
|
)
|
|
|
|
if load:
|
|
self._load_graph()
|
|
else:
|
|
self._initialize_graph()
|
|
|
|
def create_model(
|
|
self, brain, trainer_params, reward_signal_configs, is_training, load, seed
|
|
):
|
|
"""
|
|
Create PPO model
|
|
:param brain: Assigned Brain object.
|
|
:param trainer_params: Defined training parameters.
|
|
:param reward_signal_configs: Reward signal config
|
|
:param seed: Random seed.
|
|
"""
|
|
with self.graph.as_default():
|
|
self.model = PPOModel(
|
|
brain=brain,
|
|
lr=float(trainer_params["learning_rate"]),
|
|
lr_schedule=LearningRateSchedule(
|
|
trainer_params.get("learning_rate_schedule", "linear")
|
|
),
|
|
h_size=int(trainer_params["hidden_units"]),
|
|
epsilon=float(trainer_params["epsilon"]),
|
|
beta=float(trainer_params["beta"]),
|
|
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()),
|
|
vis_encode_type=EncoderType(
|
|
trainer_params.get("vis_encode_type", "simple")
|
|
),
|
|
)
|
|
self.model.create_ppo_optimizer()
|
|
|
|
self.inference_dict.update(
|
|
{
|
|
"action": self.model.output,
|
|
"log_probs": self.model.all_log_probs,
|
|
"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.total_policy_loss = self.model.abs_policy_loss
|
|
self.update_dict.update(
|
|
{
|
|
"value_loss": self.model.value_loss,
|
|
"policy_loss": self.total_policy_loss,
|
|
"update_batch": self.model.update_batch,
|
|
}
|
|
)
|
|
|
|
def create_reward_signals(self, reward_signal_configs):
|
|
"""
|
|
Create reward signals
|
|
:param reward_signal_configs: Reward signal config.
|
|
"""
|
|
self.reward_signals = {}
|
|
with self.graph.as_default():
|
|
# Create reward signals
|
|
for reward_signal, config in reward_signal_configs.items():
|
|
self.reward_signals[reward_signal] = create_reward_signal(
|
|
self, self.model, reward_signal, config
|
|
)
|
|
self.update_dict.update(self.reward_signals[reward_signal].update_dict)
|
|
|
|
@timed
|
|
def evaluate(self, brain_info):
|
|
"""
|
|
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,
|
|
}
|
|
epsilon = None
|
|
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)
|
|
if self.use_continuous_act:
|
|
epsilon = np.random.normal(
|
|
size=(len(brain_info.vector_observations), self.model.act_size[0])
|
|
)
|
|
feed_dict[self.model.epsilon] = epsilon
|
|
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, num_sequences):
|
|
"""
|
|
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(self.model, mini_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.model, mini_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, model, mini_batch, num_sequences):
|
|
feed_dict = {
|
|
model.batch_size: num_sequences,
|
|
model.sequence_length: self.sequence_length,
|
|
model.mask_input: mini_batch["masks"],
|
|
model.advantage: mini_batch["advantages"],
|
|
model.all_old_log_probs: mini_batch["action_probs"],
|
|
}
|
|
for name in self.reward_signals:
|
|
feed_dict[model.returns_holders[name]] = mini_batch[
|
|
"{}_returns".format(name)
|
|
]
|
|
feed_dict[model.old_values[name]] = mini_batch[
|
|
"{}_value_estimates".format(name)
|
|
]
|
|
|
|
if self.use_continuous_act:
|
|
feed_dict[model.output_pre] = mini_batch["actions_pre"]
|
|
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"]
|
|
if self.model.vis_obs_size > 0:
|
|
for i, _ in enumerate(self.model.visual_in):
|
|
feed_dict[model.visual_in[i]] = mini_batch["visual_obs%d" % i]
|
|
if self.use_recurrent:
|
|
mem_in = [
|
|
mini_batch["memory"][i]
|
|
for i in range(0, len(mini_batch["memory"]), self.sequence_length)
|
|
]
|
|
feed_dict[model.memory_in] = mem_in
|
|
return feed_dict
|