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# ## ML-Agent Learning (SAC)
# Contains an implementation of SAC as described in https://arxiv.org/abs/1801.01290
# and implemented in https://github.com/hill-a/stable-baselines
from collections import defaultdict
from typing import Dict
import os
import numpy as np
from mlagents_envs.logging_util import get_logger
from mlagents_envs.timers import timed
from mlagents.trainers.policy.tf_policy import TFPolicy
from mlagents.trainers.policy.nn_policy import NNPolicy
from mlagents.trainers.sac.optimizer import SACOptimizer
from mlagents.trainers.trainer.rl_trainer import RLTrainer
from mlagents.trainers.trajectory import Trajectory, SplitObservations
from mlagents.trainers.brain import BrainParameters
from mlagents.trainers.exception import UnityTrainerException
from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
logger = get_logger(__name__)
BUFFER_TRUNCATE_PERCENT = 0.8
class SACTrainer(RLTrainer):
"""
The SACTrainer is an implementation of the SAC algorithm, with support
for discrete actions and recurrent networks.
"""
def __init__(
self,
brain_name: str,
reward_buff_cap: int,
trainer_parameters: dict,
training: bool,
load: bool,
seed: int,
run_id: str,
):
"""
Responsible for collecting experiences and training SAC model.
:param brain_name: The name of the brain associated with trainer config
:param reward_buff_cap: Max reward history to track in the reward buffer
:param trainer_parameters: The parameters for the trainer (dictionary).
:param training: Whether the trainer is set for training.
:param load: Whether the model should be loaded.
:param seed: The seed the model will be initialized with
:param run_id: The The identifier of the current run
"""
super().__init__(
brain_name, trainer_parameters, training, run_id, reward_buff_cap
)
self.param_keys = [
"batch_size",
"buffer_size",
"buffer_init_steps",
"hidden_units",
"learning_rate",
"init_entcoef",
"max_steps",
"normalize",
"num_update",
"num_layers",
"time_horizon",
"sequence_length",
"summary_freq",
"tau",
"use_recurrent",
"summary_path",
"memory_size",
"model_path",
"reward_signals",
]
self._check_param_keys()
self.load = load
self.seed = seed
self.policy: NNPolicy = None # type: ignore
self.optimizer: SACOptimizer = None # type: ignore
self.step = 0
self.train_interval = (
trainer_parameters["train_interval"]
if "train_interval" in trainer_parameters
else 1
)
self.reward_signal_updates_per_train = (
trainer_parameters["reward_signals"]["reward_signal_num_update"]
if "reward_signal_num_update" in trainer_parameters["reward_signals"]
else trainer_parameters["num_update"]
)
self.checkpoint_replay_buffer = (
trainer_parameters["save_replay_buffer"]
if "save_replay_buffer" in trainer_parameters
else False
)
def _check_param_keys(self):
super()._check_param_keys()
# Check that batch size is greater than sequence length. Else, throw
# an exception.
if (
self.trainer_parameters["sequence_length"]
> self.trainer_parameters["batch_size"]
and self.trainer_parameters["use_recurrent"]
):
raise UnityTrainerException(
"batch_size must be greater than or equal to sequence_length when use_recurrent is True."
)
def save_model(self, name_behavior_id: str) -> None:
"""
Saves the model. Overrides the default save_model since we want to save
the replay buffer as well.
"""
self.policy.save_model(self.get_step)
if self.checkpoint_replay_buffer:
self.save_replay_buffer()
def save_replay_buffer(self) -> None:
"""
Save the training buffer's update buffer to a pickle file.
"""
filename = os.path.join(
self.trainer_parameters["model_path"], "last_replay_buffer.hdf5"
)
logger.info("Saving Experience Replay Buffer to {}".format(filename))
with open(filename, "wb") as file_object:
self.update_buffer.save_to_file(file_object)
def load_replay_buffer(self) -> None:
"""
Loads the last saved replay buffer from a file.
"""
filename = os.path.join(
self.trainer_parameters["model_path"], "last_replay_buffer.hdf5"
)
logger.info("Loading Experience Replay Buffer from {}".format(filename))
with open(filename, "rb+") as file_object:
self.update_buffer.load_from_file(file_object)
logger.info(
"Experience replay buffer has {} experiences.".format(
self.update_buffer.num_experiences
)
)
def _process_trajectory(self, trajectory: Trajectory) -> None:
"""
Takes a trajectory and processes it, putting it into the replay buffer.
"""
super()._process_trajectory(trajectory)
last_step = trajectory.steps[-1]
agent_id = trajectory.agent_id # All the agents should have the same ID
agent_buffer_trajectory = trajectory.to_agentbuffer()
# Update the normalization
if self.is_training:
self.policy.update_normalization(agent_buffer_trajectory["vector_obs"])
# Evaluate all reward functions for reporting purposes
self.collected_rewards["environment"][agent_id] += np.sum(
agent_buffer_trajectory["environment_rewards"]
)
for name, reward_signal in self.optimizer.reward_signals.items():
evaluate_result = reward_signal.evaluate_batch(
agent_buffer_trajectory
).scaled_reward
# Report the reward signals
self.collected_rewards[name][agent_id] += np.sum(evaluate_result)
# Get all value estimates for reporting purposes
value_estimates, _ = self.optimizer.get_trajectory_value_estimates(
agent_buffer_trajectory, trajectory.next_obs, trajectory.done_reached
)
for name, v in value_estimates.items():
self._stats_reporter.add_stat(
self.optimizer.reward_signals[name].value_name, np.mean(v)
)
# Bootstrap using the last step rather than the bootstrap step if max step is reached.
# Set last element to duplicate obs and remove dones.
if last_step.max_step:
vec_vis_obs = SplitObservations.from_observations(last_step.obs)
for i, obs in enumerate(vec_vis_obs.visual_observations):
agent_buffer_trajectory["next_visual_obs%d" % i][-1] = obs
if vec_vis_obs.vector_observations.size > 1:
agent_buffer_trajectory["next_vector_in"][
-1
] = vec_vis_obs.vector_observations
agent_buffer_trajectory["done"][-1] = False
# Append to update buffer
agent_buffer_trajectory.resequence_and_append(
self.update_buffer, training_length=self.policy.sequence_length
)
if trajectory.done_reached:
self._update_end_episode_stats(agent_id, self.optimizer)
def _is_ready_update(self) -> bool:
"""
Returns whether or not the trainer has enough elements to run update model
:return: A boolean corresponding to whether or not update_model() can be run
"""
return (
self.update_buffer.num_experiences >= self.trainer_parameters["batch_size"]
and self.step >= self.trainer_parameters["buffer_init_steps"]
)
@timed
def _update_policy(self) -> None:
"""
If train_interval is met, update the SAC policy given the current reward signals.
If reward_signal_train_interval is met, update the reward signals from the buffer.
"""
if self.step % self.train_interval == 0:
self.update_sac_policy()
self.update_reward_signals()
def create_policy(
self, parsed_behavior_id: BehaviorIdentifiers, brain_parameters: BrainParameters
) -> TFPolicy:
policy = NNPolicy(
self.seed,
brain_parameters,
self.trainer_parameters,
self.is_training,
self.load,
tanh_squash=True,
reparameterize=True,
create_tf_graph=False,
)
# Load the replay buffer if load
if self.load and self.checkpoint_replay_buffer:
try:
self.load_replay_buffer()
except (AttributeError, FileNotFoundError):
logger.warning(
"Replay buffer was unable to load, starting from scratch."
)
logger.debug(
"Loaded update buffer with {} sequences".format(
self.update_buffer.num_experiences
)
)
return policy
def update_sac_policy(self) -> None:
"""
Uses demonstration_buffer to update the policy.
The reward signal generators are updated using different mini batches.
If we want to imitate http://arxiv.org/abs/1809.02925 and similar papers, where the policy is updated
N times, then the reward signals are updated N times, then reward_signal_updates_per_train
is greater than 1 and the reward signals are not updated in parallel.
"""
self.cumulative_returns_since_policy_update.clear()
n_sequences = max(
int(self.trainer_parameters["batch_size"] / self.policy.sequence_length), 1
)
num_updates = self.trainer_parameters["num_update"]
batch_update_stats: Dict[str, list] = defaultdict(list)
for _ in range(num_updates):
logger.debug("Updating SAC policy at step {}".format(self.step))
buffer = self.update_buffer
if (
self.update_buffer.num_experiences
>= self.trainer_parameters["batch_size"]
):
sampled_minibatch = buffer.sample_mini_batch(
self.trainer_parameters["batch_size"],
sequence_length=self.policy.sequence_length,
)
# Get rewards for each reward
for name, signal in self.optimizer.reward_signals.items():
sampled_minibatch[
"{}_rewards".format(name)
] = signal.evaluate_batch(sampled_minibatch).scaled_reward
update_stats = self.optimizer.update(sampled_minibatch, n_sequences)
for stat_name, value in update_stats.items():
batch_update_stats[stat_name].append(value)
# Truncate update buffer if neccessary. Truncate more than we need to to avoid truncating
# a large buffer at each update.
if self.update_buffer.num_experiences > self.trainer_parameters["buffer_size"]:
self.update_buffer.truncate(
int(self.trainer_parameters["buffer_size"] * BUFFER_TRUNCATE_PERCENT)
)
for stat, stat_list in batch_update_stats.items():
self._stats_reporter.add_stat(stat, np.mean(stat_list))
if self.optimizer.bc_module:
update_stats = self.optimizer.bc_module.update()
for stat, val in update_stats.items():
self._stats_reporter.add_stat(stat, val)
def update_reward_signals(self) -> None:
"""
Iterate through the reward signals and update them. Unlike in PPO,
do it separate from the policy so that it can be done at a different
interval.
This function should only be used to simulate
http://arxiv.org/abs/1809.02925 and similar papers, where the policy is updated
N times, then the reward signals are updated N times. Normally, the reward signal
and policy are updated in parallel.
"""
buffer = self.update_buffer
num_updates = self.reward_signal_updates_per_train
n_sequences = max(
int(self.trainer_parameters["batch_size"] / self.policy.sequence_length), 1
)
batch_update_stats: Dict[str, list] = defaultdict(list)
for _ in range(num_updates):
# Get minibatches for reward signal update if needed
reward_signal_minibatches = {}
for name, signal in self.optimizer.reward_signals.items():
logger.debug("Updating {} at step {}".format(name, self.step))
# Some signals don't need a minibatch to be sampled - so we don't!
if signal.update_dict:
reward_signal_minibatches[name] = buffer.sample_mini_batch(
self.trainer_parameters["batch_size"],
sequence_length=self.policy.sequence_length,
)
update_stats = self.optimizer.update_reward_signals(
reward_signal_minibatches, n_sequences
)
for stat_name, value in update_stats.items():
batch_update_stats[stat_name].append(value)
for stat, stat_list in batch_update_stats.items():
self._stats_reporter.add_stat(stat, np.mean(stat_list))
def add_policy(
self, parsed_behavior_id: BehaviorIdentifiers, policy: TFPolicy
) -> None:
"""
Adds policy to trainer.
:param brain_parameters: specifications for policy construction
"""
if self.policy:
logger.warning(
"Your environment contains multiple teams, but {} doesn't support adversarial games. Enable self-play to \
train adversarial games.".format(
self.__class__.__name__
)
)
if not isinstance(policy, NNPolicy):
raise RuntimeError("Non-SACPolicy passed to SACTrainer.add_policy()")
self.policy = policy
self.optimizer = SACOptimizer(self.policy, self.trainer_parameters)
for _reward_signal in self.optimizer.reward_signals.keys():
self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
# Needed to resume loads properly
self.step = policy.get_current_step()
self.next_summary_step = self._get_next_summary_step()
def get_policy(self, name_behavior_id: str) -> TFPolicy:
"""
Gets policy from trainer associated with name_behavior_id
:param name_behavior_id: full identifier of policy
"""
return self.policy