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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, cast
import os
import numpy as np
from mlagents.trainers.policy.checkpoint_manager import ModelCheckpoint
from mlagents_envs.logging_util import get_logger
from mlagents_envs.timers import timed
from mlagents_envs.base_env import BehaviorSpec
from mlagents.trainers.buffer import BufferKey, RewardSignalUtil
from mlagents.trainers.policy import Policy
from mlagents.trainers.trainer.rl_trainer import RLTrainer
from mlagents.trainers.policy.torch_policy import TorchPolicy
from mlagents.trainers.sac.optimizer_torch import TorchSACOptimizer
from mlagents.trainers.trajectory import Trajectory, ObsUtil
from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
from mlagents.trainers.settings import TrainerSettings, SACSettings
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,
behavior_name: str,
reward_buff_cap: int,
trainer_settings: TrainerSettings,
training: bool,
load: bool,
seed: int,
artifact_path: str,
):
"""
Responsible for collecting experiences and training SAC model.
:param behavior_name: The name of the behavior associated with trainer config
:param reward_buff_cap: Max reward history to track in the reward buffer
:param trainer_settings: The parameters for the trainer.
: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 artifact_path: The directory within which to store artifacts from this trainer.
"""
super().__init__(
behavior_name,
trainer_settings,
training,
load,
artifact_path,
reward_buff_cap,
)
self.seed = seed
self.policy: Policy = None # type: ignore
self.optimizer: TorchSACOptimizer = None # type: ignore
self.hyperparameters: SACSettings = cast(
SACSettings, trainer_settings.hyperparameters
)
self._step = 0
# Don't divide by zero
self.update_steps = 1
self.reward_signal_update_steps = 1
self.steps_per_update = self.hyperparameters.steps_per_update
self.reward_signal_steps_per_update = (
self.hyperparameters.reward_signal_steps_per_update
)
self.checkpoint_replay_buffer = self.hyperparameters.save_replay_buffer
def _checkpoint(self) -> ModelCheckpoint:
"""
Writes a checkpoint model to memory
Overrides the default to save the replay buffer.
"""
ckpt = super()._checkpoint()
if self.checkpoint_replay_buffer:
self.save_replay_buffer()
return ckpt
def save_model(self) -> None:
"""
Saves the final training model to memory
Overrides the default to save the replay buffer.
"""
super().save_model()
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.artifact_path, "last_replay_buffer.hdf5")
logger.info(f"Saving Experience Replay Buffer to {filename}...")
with open(filename, "wb") as file_object:
self.update_buffer.save_to_file(file_object)
logger.info(
f"Saved Experience Replay Buffer ({os.path.getsize(filename)} bytes)."
)
def load_replay_buffer(self) -> None:
"""
Loads the last saved replay buffer from a file.
"""
filename = os.path.join(self.artifact_path, "last_replay_buffer.hdf5")
logger.info(f"Loading Experience Replay Buffer from {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()
# Check if we used group rewards, warn if so.
self._warn_if_group_reward(agent_buffer_trajectory)
# Update the normalization
if self.is_training:
self.policy.update_normalization(agent_buffer_trajectory)
# Evaluate all reward functions for reporting purposes
self.collected_rewards["environment"][agent_id] += np.sum(
agent_buffer_trajectory[BufferKey.ENVIRONMENT_REWARDS]
)
for name, reward_signal in self.optimizer.reward_signals.items():
evaluate_result = (
reward_signal.evaluate(agent_buffer_trajectory) * reward_signal.strength
)
# Report the reward signals
self.collected_rewards[name][agent_id] += np.sum(evaluate_result)
# Get all value estimates for reporting purposes
(
value_estimates,
_,
value_memories,
) = self.optimizer.get_trajectory_value_estimates(
agent_buffer_trajectory, trajectory.next_obs, trajectory.done_reached
)
if value_memories is not None:
agent_buffer_trajectory[BufferKey.CRITIC_MEMORY].set(value_memories)
for name, v in value_estimates.items():
self._stats_reporter.add_stat(
f"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value",
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.interrupted:
last_step_obs = last_step.obs
for i, obs in enumerate(last_step_obs):
agent_buffer_trajectory[ObsUtil.get_name_at_next(i)][-1] = obs
agent_buffer_trajectory[BufferKey.DONE][-1] = False
self._append_to_update_buffer(agent_buffer_trajectory)
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_policy() can be run
"""
return (
self.update_buffer.num_experiences >= self.hyperparameters.batch_size
and self._step >= self.hyperparameters.buffer_init_steps
)
@timed
def _update_policy(self) -> bool:
"""
Update the SAC policy and reward signals. The reward signal generators are updated using different mini batches.
By default we 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.
:return: Whether or not the policy was updated.
"""
policy_was_updated = self._update_sac_policy()
self._update_reward_signals()
return policy_was_updated
def maybe_load_replay_buffer(self):
# 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
)
)
def create_torch_policy(
self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
) -> TorchPolicy:
"""
Creates a policy with a PyTorch backend and SAC hyperparameters
:param parsed_behavior_id:
:param behavior_spec: specifications for policy construction
:return policy
"""
policy = TorchPolicy(
self.seed,
behavior_spec,
self.trainer_settings,
condition_sigma_on_obs=True,
tanh_squash=True,
separate_critic=True,
)
self.maybe_load_replay_buffer()
return policy
def _update_sac_policy(self) -> bool:
"""
Uses update_buffer to update the policy. We sample the update_buffer and update
until the steps_per_update ratio is met.
"""
has_updated = False
self.cumulative_returns_since_policy_update.clear()
n_sequences = max(
int(self.hyperparameters.batch_size / self.policy.sequence_length), 1
)
batch_update_stats: Dict[str, list] = defaultdict(list)
while (
self._step - self.hyperparameters.buffer_init_steps
) / self.update_steps > self.steps_per_update:
logger.debug(f"Updating SAC policy at step {self._step}")
buffer = self.update_buffer
if self.update_buffer.num_experiences >= self.hyperparameters.batch_size:
sampled_minibatch = buffer.sample_mini_batch(
self.hyperparameters.batch_size,
sequence_length=self.policy.sequence_length,
)
# Get rewards for each reward
for name, signal in self.optimizer.reward_signals.items():
sampled_minibatch[RewardSignalUtil.rewards_key(name)] = (
signal.evaluate(sampled_minibatch) * signal.strength
)
update_stats = self.optimizer.update(sampled_minibatch, n_sequences)
for stat_name, value in update_stats.items():
batch_update_stats[stat_name].append(value)
self.update_steps += 1
for stat, stat_list in batch_update_stats.items():
self._stats_reporter.add_stat(stat, np.mean(stat_list))
has_updated = True
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)
# 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.hyperparameters.buffer_size:
self.update_buffer.truncate(
int(self.hyperparameters.buffer_size * BUFFER_TRUNCATE_PERCENT)
)
return has_updated
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
n_sequences = max(
int(self.hyperparameters.batch_size / self.policy.sequence_length), 1
)
batch_update_stats: Dict[str, list] = defaultdict(list)
while (
self._step - self.hyperparameters.buffer_init_steps
) / self.reward_signal_update_steps > self.reward_signal_steps_per_update:
# Get minibatches for reward signal update if needed
reward_signal_minibatches = {}
for name in self.optimizer.reward_signals.keys():
logger.debug(f"Updating {name} at step {self._step}")
if name != "extrinsic":
reward_signal_minibatches[name] = buffer.sample_mini_batch(
self.hyperparameters.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)
self.reward_signal_update_steps += 1
for stat, stat_list in batch_update_stats.items():
self._stats_reporter.add_stat(stat, np.mean(stat_list))
def create_sac_optimizer(self) -> TorchSACOptimizer:
return TorchSACOptimizer( # type: ignore
cast(TorchPolicy, self.policy), self.trainer_settings # type: ignore
) # type: ignore
def add_policy(
self, parsed_behavior_id: BehaviorIdentifiers, policy: Policy
) -> None:
"""
Adds policy to trainer.
"""
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__
)
)
self.policy = policy
self.policies[parsed_behavior_id.behavior_id] = policy
self.optimizer = self.create_sac_optimizer()
for _reward_signal in self.optimizer.reward_signals.keys():
self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
self.model_saver.register(self.policy)
self.model_saver.register(self.optimizer)
self.model_saver.initialize_or_load()
# Needed to resume loads properly
self._step = policy.get_current_step()
# Assume steps were updated at the correct ratio before
self.update_steps = int(max(1, self._step / self.steps_per_update))
self.reward_signal_update_steps = int(
max(1, self._step / self.reward_signal_steps_per_update)
)
def get_policy(self, name_behavior_id: str) -> Policy:
"""
Gets policy from trainer associated with name_behavior_id
:param name_behavior_id: full identifier of policy
"""
return self.policy