from abc import ABC, abstractmethod
from collections.abc import Collection, Iterable
from typing import Any, Generic, Optional, TypeVar, Union
import numpy as np
import numpy.typing as npt
from gymnasium import Env
from gymnasium.spaces import Box
from ...core.callbacks import LearningAgentCallbackMixin
from ...core.experience import ExperienceReplay
from ...core.parameters import LearnableParametersDict
from ...core.update import UpdateStrategy
from ...util.seeding import RngType, mk_seed
from .agent import ActType, Agent, ObsType, SymType, _update_dicts
ExpType = TypeVar("ExpType")
[docs]
class LearningAgent(
Agent[SymType], LearningAgentCallbackMixin, ABC, Generic[SymType, ExpType]
):
r"""Base abstract class for a learning agent with MPC as policy provider. The main
method :meth:`update`, which is called to update the learnable parameters of the MPC
according to the underlying learning methodology (e.g., Bayesian Optimization, RL,
etc.), is abstract and must be implemented by inheriting classes based on their
learning algorithm.
Aside of :meth:`update`, this class also provides also the basic skeleton for both
on-policy and off-policy learning, which require further ad-hoc implementations. For
on-policy learning, the :meth:`train` method should be run, which requires the
implementation of :meth:`train_one_episode`. For off-policy learning, run
:meth:`train_offpolicy` and implement :meth:`train_one_rollout` instead. At least
one of the two implementations is required in the inheriting class, depending on the
learning algorithm. Some algorithms might support both.
Parameters
----------
update_strategy : UpdateStrategy or int
The strategy used to decide which frequency to update the mpc parameters with.
If an ``int`` is passed, then the default strategy that updates every ``n``
env's steps is used (where ``n`` is the argument passed); otherwise, an instance
of :class:`core.update.UpdateStrategy` can be passed to specify the
desired strategy in more details.
learnable_parameters : :class:`core.parameters.LearnableParametersDict`
A special dict containing the learnable parameters of the MPC (usually referred
to as :math:`\theta`), together with their bounds and values. This dict is
complementary to :attr:`fixed_parameters`, which contains the MPC parameters
that are not learnt by the agent.
experience : int or ExperienceReplay, optional
The container for experience replay memory. If ``None`` is passed, then a memory
with unitary length is created, i.e., it keeps only the latest memory
transition. If an integer ``n`` is passed, then a memory with the length ``n``
is created and with sample size ``n``. Otherwise, pass an instance of
:class:`core.experience.ExperienceReplay` to specify the requirements in more
details.
kwargs
Additional arguments to be passed to :class:`Agent`.
Notes
-----
This class makes no assumptions on the learning methodology used to update the MPC's
learnable parameters. This could be either gradient-based or gradient-free, but the
logic implemented in this class should largely remain untouched.
"""
def __init__(
self,
update_strategy: Union[int, UpdateStrategy],
learnable_parameters: LearnableParametersDict,
experience: Union[None, int, ExperienceReplay[ExpType]] = None,
**kwargs: Any,
) -> None:
Agent.__init__(self, **kwargs)
LearningAgentCallbackMixin.__init__(self)
self._learnable_pars = self._reorder_learnable_parameters(learnable_parameters)
if experience is None:
experience = ExperienceReplay(maxlen=1)
elif isinstance(experience, int):
experience = ExperienceReplay(maxlen=experience, sample_size=experience)
self._experience = experience
if not isinstance(update_strategy, UpdateStrategy):
update_strategy = UpdateStrategy(update_strategy, "on_timestep_end")
self._update_strategy = update_strategy
self._raises: bool
self._is_training = False
self._establish_callback_hooks()
@property
def experience(self) -> ExperienceReplay[ExpType]:
"""Gets the experience replay memory of the agent."""
return self._experience
@property
def update_strategy(self) -> UpdateStrategy:
"""Gets the update strategy of the agent."""
return self._update_strategy
@property
def learnable_parameters(self) -> LearnableParametersDict:
"""Gets the parameters of the MPC that can be learnt by the agent."""
return self._learnable_pars
[docs]
def reset(self, seed: RngType = None) -> None:
super().reset(seed)
self.experience.reset(seed)
[docs]
def store_experience(self, item: ExpType) -> None:
"""Stores the given item in the agent's :attr:`experience` for later usage in
updating the parametrization.
Parameters
----------
item : ExpType
Item to be stored in memory.
"""
self._experience.append(item)
[docs]
def evaluate(self, *args: Any, **kwargs: Any) -> npt.NDArray[np.floating]:
self._is_training = False
return super().evaluate(*args, **kwargs)
[docs]
def train(
self,
env: Env[ObsType, ActType],
episodes: int,
seed: RngType = None,
raises: bool = True,
env_reset_options: Optional[dict[str, Any]] = None,
) -> npt.NDArray[np.floating]:
"""On-policy training of the agent on an environment.
Parameters
----------
env : Env[ObsType, ActType]
The gym environment where to train the agent on.
episodes : int
Number of training episodes.
seed : None, int, array_like of ints, SeedSequence, BitGenerator, Generator
Seed for the agent's and env's random number generator. By default ``None``.
raises : bool, optional
If ``True``, when any of the MPC solver runs fails, or when an update fails,
the corresponding error is raised; otherwise, only a warning is raised.
env_reset_options : dict, optional
Additional information to specify how the environment is reset at each
evalution episode (optional, depending on the specific environment).
Returns
-------
array of doubles
The cumulative returns for each training episode.
Raises
------
MpcSolverError or MpcSolverWarning
Raises the error or the warning (depending on ``raises``) if any of the MPC
solvers fail.
UpdateError or UpdateWarning
Raises the error or the warning (depending on ``raises``) if the update
fails.
"""
if hasattr(env, "action_space"):
assert isinstance(env.action_space, Box), "Env action space must be a Box,"
rng = np.random.default_rng(seed)
self.reset(rng)
self._is_training = True
self._raises = raises
returns = np.zeros(episodes, float)
self.on_training_start(env)
for episode in range(episodes):
state, _ = env.reset(seed=mk_seed(rng), options=env_reset_options)
self.on_episode_start(env, episode, state)
r = self.train_one_episode(env, episode, state, raises)
self.on_episode_end(env, episode, r)
returns[episode] = r
self.on_training_end(env, returns)
return returns
[docs]
def train_one_episode(
self,
env: Env[ObsType, ActType],
episode: int,
init_state: ObsType,
raises: bool = True,
) -> float:
"""On-policy training of the agent on an environment for one single episode.
Parameters
----------
env : Env[ObsType, ActType]
The gym environment where to train the agent on.
episode : int
Number of the current training episode.
init_state : observation type
Initial state/observation of the environment.
raises : bool, optional
If ``True``, when any of the MPC solver runs fails, or when an update fails,
the corresponding error is raised; otherwise, only a warning is raised.
Returns
-------
float
The cumulative rewards for this training episode.
Raises
------
MpcSolverError or MpcSolverWarning
Raises the error or the warning (depending on ``raises``) if any of the MPC
solvers fail.
UpdateError or UpdateWarning
Raises the error or the warning (depending on ``raises``) if the update
fails.
"""
raise NotImplementedError(
f"{self.__class__.__name__} does not implement `train_one_episode` for "
"on-policy learning."
)
[docs]
def train_offpolicy(
self,
episode_rollouts: Iterable[Iterable[Any]],
seed: RngType = None,
raises: bool = True,
) -> None:
"""Off-policy training of the agent on an environment.
Parameters
----------
episode_rollouts : iterable of iterables of any
An iterable of episodical rollouts generated in an
off-policy fashion. Each rollout is itself a sequence of transitions, e.g.,
SARSA tuples. In other words, `episode_rollouts` is a sequence of sequences
of tuples. However, in general, its nature and the tuples' can widely differ
from learning algorithm to learning algorithm.
seed : None, int, array_like of ints, SeedSequence, BitGenerator, Generator
Seed for the agent's random number generator. By default ``None``.
raises : bool, optional
If ``True``, when any of the MPC solver runs fails, or when an update fails,
the corresponding error is raised; otherwise, only a warning is raised.
Raises
------
MpcSolverError or MpcSolverWarning
Raises the error or the warning (depending on ``raises``) if any of the MPC
solvers fail.
UpdateError or UpdateWarning
Raises the error or the warning (depending on ``raises``) if the update
fails.
"""
rng = np.random.default_rng(seed)
self.reset(rng)
self._raises = raises
env_proxy = "off-policy"
self._is_training = True
self.on_training_start(env_proxy)
for episode, rollout in enumerate(episode_rollouts):
self.on_episode_start(env_proxy, episode, float("nan"))
self.train_one_rollout(rollout, episode, raises)
self.on_episode_end(env_proxy, episode, float("nan"))
self.on_training_end(env_proxy, np.empty(0))
[docs]
def train_one_rollout(
self, rollout: Iterable[Any], episode: int, raises: bool = True
) -> None:
"""Train the agent in an off-policy manner on the given rollout.
Parameters
----------
rollout : iterable of any
Rollout, i.e., a sequence of transitions generated off-policy, e.g., SARSA
tuples. However, in general, these tuples can be of different nature,
depending on the specific learning algorithm.
raises : bool, optional
If ``True``, when any of the MPC solver runs fails, or when an update fails,
the corresponding error is raised; otherwise, only a warning is raised.
Raises
------
MpcSolverError or MpcSolverWarning
Raises the error or the warning (depending on ``raises``) if any of the MPC
solvers fail.
UpdateError or UpdateWarning
Raises the error or the warning (depending on ``raises``) if the update
fails.
"""
raise NotImplementedError(
f"{self.__class__.__name__} does not implement `train_offpolicy` for "
"off-policy learning."
)
[docs]
@abstractmethod
def update(self) -> Optional[str]:
r"""Updates the learnable parameters (usually referred to as :math:`\theta`) of
the MPC according to the agent's learning algorithm.
Returns
-------
errormsg : str or None
In case the update fails, an error message is returned to be raised as error
or warning; otherwise, ``None`` is returned.
"""
def _reorder_learnable_parameters(
self, dict_: LearnableParametersDict
) -> LearnableParametersDict:
"""Reorders the learnable parameters of the MPC according to their creation
order."""
reordered = [dict_.pop(name) for name in self.V.parameters if name in dict_]
assert not dict_, (
"Not all learnable parameters could be reordered. "
"Please check for spurious learnable parameters in `learnable_parameters`."
)
dict_.update(reordered)
return dict_
def _establish_callback_hooks(self) -> None:
super()._establish_callback_hooks()
# hook exploration (only if necessary)
exploration_hook = self._exploration.hook
if exploration_hook is not None:
self._hook_callback(
repr(self._exploration), exploration_hook, self._exploration.step
)
# hook updates (always necessary)
update_hook = self._update_strategy.hook
self._hook_callback(
repr(self._update_strategy), update_hook, self._check_and_perform_update
)
def _check_and_perform_update(
self, _: Env[ObsType, ActType], episode: int, timestep_or_return: float
) -> None:
"""Internal utility to check if an update is due and perform it."""
if not self._is_training or not self._update_strategy.can_update():
return
update_msg = self.update()
if update_msg is not None:
timestep = (
timestep_or_return if isinstance(timestep_or_return, int) else None
)
self.on_update_failure(episode, timestep, update_msg, self._raises)
self.on_update()
def _get_parameters(
self,
overwrite_fixed_pars: Union[
None, dict[str, npt.ArrayLike], Collection[dict[str, npt.ArrayLike]]
] = None,
) -> Union[None, dict[str, npt.ArrayLike], Collection[dict[str, npt.ArrayLike]]]:
"""Internal utility to retrieve parameters of the MPC in order to solve it.
:class:`LearningAgent` returns both fixed and learnable parameters.
Parameters
----------
overwrite_fixed_pars : dict of (str, array_like), or collection of, optional
If not ``None``, this argument is used instead of :attr:`fixed_parameters`
to retrieve the fixed parameters of the MPC.
"""
learnable_pars = self._learnable_pars.value_as_dict
fixed_pars = (
self.fixed_parameters
if overwrite_fixed_pars is None
else overwrite_fixed_pars
)
if fixed_pars is None:
return learnable_pars
if isinstance(fixed_pars, dict):
fixed_pars.update(learnable_pars)
return fixed_pars
return tuple(_update_dicts(fixed_pars, learnable_pars))
