gomoku / DI-engine /ding /policy /base_policy.py
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init space
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from typing import Optional, List, Dict, Any, Tuple, Union
from abc import ABC, abstractmethod
from collections import namedtuple
from easydict import EasyDict
import copy
import torch
from ding.model import create_model
from ding.utils import import_module, allreduce, broadcast, get_rank, allreduce_async, synchronize, deep_merge_dicts, \
POLICY_REGISTRY
class Policy(ABC):
"""
Overview:
The basic class of Reinforcement Learning (RL) and Imitation Learning (IL) policy in DI-engine.
Property:
``cfg``, ``learn_mode``, ``collect_mode``, ``eval_mode``
"""
@classmethod
def default_config(cls: type) -> EasyDict:
"""
Overview:
Get the default config of policy. This method is used to create the default config of policy.
Returns:
- cfg (:obj:`EasyDict`): The default config of corresponding policy. For the derived policy class, \
it will recursively merge the default config of base class and its own default config.
.. tip::
This method will deepcopy the ``config`` attribute of the class and return the result. So users don't need \
to worry about the modification of the returned config.
"""
if cls == Policy:
raise RuntimeError("Basic class Policy doesn't have completed default_config")
base_cls = cls.__base__
if base_cls == Policy:
base_policy_cfg = EasyDict(copy.deepcopy(Policy.config))
else:
base_policy_cfg = copy.deepcopy(base_cls.default_config())
cfg = EasyDict(copy.deepcopy(cls.config))
cfg = deep_merge_dicts(base_policy_cfg, cfg)
cfg.cfg_type = cls.__name__ + 'Dict'
return cfg
learn_function = namedtuple(
'learn_function', [
'forward',
'reset',
'info',
'monitor_vars',
'get_attribute',
'set_attribute',
'state_dict',
'load_state_dict',
]
)
collect_function = namedtuple(
'collect_function', [
'forward',
'process_transition',
'get_train_sample',
'reset',
'get_attribute',
'set_attribute',
'state_dict',
'load_state_dict',
]
)
eval_function = namedtuple(
'eval_function', [
'forward',
'reset',
'get_attribute',
'set_attribute',
'state_dict',
'load_state_dict',
]
)
total_field = set(['learn', 'collect', 'eval'])
config = dict(
# (bool) Whether the learning policy is the same as the collecting data policy (on-policy).
on_policy=False,
# (bool) Whether to use cuda in policy.
cuda=False,
# (bool) Whether to use data parallel multi-gpu mode in policy.
multi_gpu=False,
# (bool) Whether to synchronize update the model parameters after allreduce the gradients of model parameters.
bp_update_sync=True,
# (bool) Whether to enable infinite trajectory length in data collecting.
traj_len_inf=False,
# neural network model config
model=dict(),
)
def __init__(
self,
cfg: EasyDict,
model: Optional[torch.nn.Module] = None,
enable_field: Optional[List[str]] = None
) -> None:
"""
Overview:
Initialize policy instance according to input configures and model. This method will initialize differnent \
fields in policy, including ``learn``, ``collect``, ``eval``. The ``learn`` field is used to train the \
policy, the ``collect`` field is used to collect data for training, and the ``eval`` field is used to \
evaluate the policy. The ``enable_field`` is used to specify which field to initialize, if it is None, \
then all fields will be initialized.
Arguments:
- cfg (:obj:`EasyDict`): The final merged config used to initialize policy. For the default config, \
see the ``config`` attribute and its comments of policy class.
- model (:obj:`torch.nn.Module`): The neural network model used to initialize policy. If it \
is None, then the model will be created according to ``default_model`` method and ``cfg.model`` field. \
Otherwise, the model will be set to the ``model`` instance created by outside caller.
- enable_field (:obj:`Optional[List[str]]`): The field list to initialize. If it is None, then all fields \
will be initialized. Otherwise, only the fields in ``enable_field`` will be initialized, which is \
beneficial to save resources.
.. note::
For the derived policy class, it should implement the ``_init_learn``, ``_init_collect``, ``_init_eval`` \
method to initialize the corresponding field.
"""
self._cfg = cfg
self._on_policy = self._cfg.on_policy
if enable_field is None:
self._enable_field = self.total_field
else:
self._enable_field = enable_field
assert set(self._enable_field).issubset(self.total_field), self._enable_field
if len(set(self._enable_field).intersection(set(['learn', 'collect', 'eval']))) > 0:
model = self._create_model(cfg, model)
self._cuda = cfg.cuda and torch.cuda.is_available()
# now only support multi-gpu for only enable learn mode
if len(set(self._enable_field).intersection(set(['learn']))) > 0:
multi_gpu = self._cfg.multi_gpu
self._rank = get_rank() if multi_gpu else 0
if self._cuda:
# model.cuda() is an in-place operation.
model.cuda()
if multi_gpu:
bp_update_sync = self._cfg.bp_update_sync
self._bp_update_sync = bp_update_sync
self._init_multi_gpu_setting(model, bp_update_sync)
else:
self._rank = 0
if self._cuda:
# model.cuda() is an in-place operation.
model.cuda()
self._model = model
self._device = 'cuda:{}'.format(self._rank % torch.cuda.device_count()) if self._cuda else 'cpu'
else:
self._cuda = False
self._rank = 0
self._device = 'cpu'
# call the initialization method of different modes, such as ``_init_learn``, ``_init_collect``, ``_init_eval``
for field in self._enable_field:
getattr(self, '_init_' + field)()
def _init_multi_gpu_setting(self, model: torch.nn.Module, bp_update_sync: bool) -> None:
"""
Overview:
Initialize multi-gpu data parallel training setting, including broadcast model parameters at the beginning \
of the training, and prepare the hook function to allreduce the gradients of model parameters.
Arguments:
- model (:obj:`torch.nn.Module`): The neural network model to be trained.
- bp_update_sync (:obj:`bool`): Whether to synchronize update the model parameters after allreduce the \
gradients of model parameters. Async update can be parallel in different network layers like pipeline \
so that it can save time.
"""
for name, param in model.state_dict().items():
assert isinstance(param.data, torch.Tensor), type(param.data)
broadcast(param.data, 0)
# here we manually set the gradient to zero tensor at the beginning of the training, which is necessary for
# the case that different GPUs have different computation graph.
for name, param in model.named_parameters():
setattr(param, 'grad', torch.zeros_like(param))
if not bp_update_sync:
def make_hook(name, p):
def hook(*ignore):
allreduce_async(name, p.grad.data)
return hook
for i, (name, p) in enumerate(model.named_parameters()):
if p.requires_grad:
p_tmp = p.expand_as(p)
grad_acc = p_tmp.grad_fn.next_functions[0][0]
grad_acc.register_hook(make_hook(name, p))
def _create_model(self, cfg: EasyDict, model: Optional[torch.nn.Module] = None) -> torch.nn.Module:
"""
Overview:
Create or validate the neural network model according to input configures and model. If the input model is \
None, then the model will be created according to ``default_model`` method and ``cfg.model`` field. \
Otherwise, the model will be verified as an instance of ``torch.nn.Module`` and set to the ``model`` \
instance created by outside caller.
Arguments:
- cfg (:obj:`EasyDict`): The final merged config used to initialize policy.
- model (:obj:`torch.nn.Module`): The neural network model used to initialize policy. User can refer to \
the default model defined in corresponding policy to customize its own model.
Returns:
- model (:obj:`torch.nn.Module`): The created neural network model. The different modes of policy will \
add distinct wrappers and plugins to the model, which is used to train, collect and evaluate.
Raises:
- RuntimeError: If the input model is not None and is not an instance of ``torch.nn.Module``.
"""
if model is None:
model_cfg = cfg.model
if 'type' not in model_cfg:
m_type, import_names = self.default_model()
model_cfg.type = m_type
model_cfg.import_names = import_names
return create_model(model_cfg)
else:
if isinstance(model, torch.nn.Module):
return model
else:
raise RuntimeError("invalid model: {}".format(type(model)))
@property
def cfg(self) -> EasyDict:
return self._cfg
@abstractmethod
def _init_learn(self) -> None:
"""
Overview:
Initialize the learn mode of policy, including related attributes and modules. This method will be \
called in ``__init__`` method if ``learn`` field is in ``enable_field``. Almost different policies have \
its own learn mode, so this method must be overrided in subclass.
.. note::
For the member variables that need to be saved and loaded, please refer to the ``_state_dict_learn`` \
and ``_load_state_dict_learn`` methods.
.. note::
For the member variables that need to be monitored, please refer to the ``_monitor_vars_learn`` method.
.. note::
If you want to set some spacial member variables in ``_init_learn`` method, you'd better name them \
with prefix ``_learn_`` to avoid conflict with other modes, such as ``self._learn_attr1``.
"""
raise NotImplementedError
@abstractmethod
def _init_collect(self) -> None:
"""
Overview:
Initialize the collect mode of policy, including related attributes and modules. This method will be \
called in ``__init__`` method if ``collect`` field is in ``enable_field``. Almost different policies have \
its own collect mode, so this method must be overrided in subclass.
.. note::
For the member variables that need to be saved and loaded, please refer to the ``_state_dict_collect`` \
and ``_load_state_dict_collect`` methods.
.. note::
If you want to set some spacial member variables in ``_init_collect`` method, you'd better name them \
with prefix ``_collect_`` to avoid conflict with other modes, such as ``self._collect_attr1``.
"""
raise NotImplementedError
@abstractmethod
def _init_eval(self) -> None:
"""
Overview:
Initialize the eval mode of policy, including related attributes and modules. This method will be \
called in ``__init__`` method if ``eval`` field is in ``enable_field``. Almost different policies have \
its own eval mode, so this method must be overrided in subclass.
.. note::
For the member variables that need to be saved and loaded, please refer to the ``_state_dict_eval`` \
and ``_load_state_dict_eval`` methods.
.. note::
If you want to set some spacial member variables in ``_init_eval`` method, you'd better name them \
with prefix ``_eval_`` to avoid conflict with other modes, such as ``self._eval_attr1``.
"""
raise NotImplementedError
@property
def learn_mode(self) -> 'Policy.learn_function': # noqa
"""
Overview:
Return the interfaces of learn mode of policy, which is used to train the model. Here we use namedtuple \
to define immutable interfaces and restrict the usage of policy in different mode. Moreover, derived \
subclass can override the interfaces to customize its own learn mode.
Returns:
- interfaces (:obj:`Policy.learn_function`): The interfaces of learn mode of policy, it is a namedtuple \
whose values of distinct fields are different internal methods.
Examples:
>>> policy = Policy(cfg, model)
>>> policy_learn = policy.learn_mode
>>> train_output = policy_learn.forward(data)
>>> state_dict = policy_learn.state_dict()
"""
return Policy.learn_function(
self._forward_learn,
self._reset_learn,
self.__repr__,
self._monitor_vars_learn,
self._get_attribute,
self._set_attribute,
self._state_dict_learn,
self._load_state_dict_learn,
)
@property
def collect_mode(self) -> 'Policy.collect_function': # noqa
"""
Overview:
Return the interfaces of collect mode of policy, which is used to train the model. Here we use namedtuple \
to define immutable interfaces and restrict the usage of policy in different mode. Moreover, derived \
subclass can override the interfaces to customize its own collect mode.
Returns:
- interfaces (:obj:`Policy.collect_function`): The interfaces of collect mode of policy, it is a \
namedtuple whose values of distinct fields are different internal methods.
Examples:
>>> policy = Policy(cfg, model)
>>> policy_collect = policy.collect_mode
>>> obs = env_manager.ready_obs
>>> inference_output = policy_collect.forward(obs)
>>> next_obs, rew, done, info = env_manager.step(inference_output.action)
"""
return Policy.collect_function(
self._forward_collect,
self._process_transition,
self._get_train_sample,
self._reset_collect,
self._get_attribute,
self._set_attribute,
self._state_dict_collect,
self._load_state_dict_collect,
)
@property
def eval_mode(self) -> 'Policy.eval_function': # noqa
"""
Overview:
Return the interfaces of eval mode of policy, which is used to train the model. Here we use namedtuple \
to define immutable interfaces and restrict the usage of policy in different mode. Moreover, derived \
subclass can override the interfaces to customize its own eval mode.
Returns:
- interfaces (:obj:`Policy.eval_function`): The interfaces of eval mode of policy, it is a namedtuple \
whose values of distinct fields are different internal methods.
Examples:
>>> policy = Policy(cfg, model)
>>> policy_eval = policy.eval_mode
>>> obs = env_manager.ready_obs
>>> inference_output = policy_eval.forward(obs)
>>> next_obs, rew, done, info = env_manager.step(inference_output.action)
"""
return Policy.eval_function(
self._forward_eval,
self._reset_eval,
self._get_attribute,
self._set_attribute,
self._state_dict_eval,
self._load_state_dict_eval,
)
def _set_attribute(self, name: str, value: Any) -> None:
"""
Overview:
In order to control the access of the policy attributes, we expose different modes to outside rather than \
directly use the policy instance. And we also provide a method to set the attribute of the policy in \
different modes. And the new attribute will named as ``_{name}``.
Arguments:
- name (:obj:`str`): The name of the attribute.
- value (:obj:`Any`): The value of the attribute.
"""
setattr(self, '_' + name, value)
def _get_attribute(self, name: str) -> Any:
"""
Overview:
In order to control the access of the policy attributes, we expose different modes to outside rather than \
directly use the policy instance. And we also provide a method to get the attribute of the policy in \
different modes.
Arguments:
- name (:obj:`str`): The name of the attribute.
Returns:
- value (:obj:`Any`): The value of the attribute.
.. note::
DI-engine's policy will first try to access `_get_{name}` method, and then try to access `_{name}` \
attribute. If both of them are not found, it will raise a ``NotImplementedError``.
"""
if hasattr(self, '_get_' + name):
return getattr(self, '_get_' + name)()
elif hasattr(self, '_' + name):
return getattr(self, '_' + name)
else:
raise NotImplementedError
def __repr__(self) -> str:
"""
Overview:
Get the string representation of the policy.
Returns:
- repr (:obj:`str`): The string representation of the policy.
"""
return "DI-engine DRL Policy\n{}".format(repr(self._model))
def sync_gradients(self, model: torch.nn.Module) -> None:
"""
Overview:
Synchronize (allreduce) gradients of model parameters in data-parallel multi-gpu training.
Arguments:
- model (:obj:`torch.nn.Module`): The model to synchronize gradients.
.. note::
This method is only used in multi-gpu training, and it shoule be called after ``backward`` method and \
before ``step`` method. The user can also use ``bp_update_sync`` config to control whether to synchronize \
gradients allreduce and optimizer updates.
"""
if self._bp_update_sync:
for name, param in model.named_parameters():
if param.requires_grad:
allreduce(param.grad.data)
else:
synchronize()
# don't need to implement default_model method by force
def default_model(self) -> Tuple[str, List[str]]:
"""
Overview:
Return this algorithm default neural network model setting for demonstration. ``__init__`` method will \
automatically call this method to get the default model setting and create model.
Returns:
- model_info (:obj:`Tuple[str, List[str]]`): The registered model name and model's import_names.
.. note::
The user can define and use customized network model but must obey the same inferface definition indicated \
by import_names path. For example about DQN, its registered name is ``dqn`` and the import_names is \
``ding.model.template.q_learning.DQN``
"""
raise NotImplementedError
# *************************************** learn function ************************************
@abstractmethod
def _forward_learn(self, data: List[Dict[str, Any]]) -> Dict[str, Any]:
"""
Overview:
Policy forward function of learn mode (training policy and updating parameters). Forward means \
that the policy inputs some training batch data from the replay buffer and then returns the output \
result, including various training information such as loss value, policy entropy, q value, priority, \
and so on. This method is left to be implemented by the subclass, and more arguments can be added in \
``data`` item if necessary.
Arguments:
- data (:obj:`List[Dict[int, Any]]`): The input data used for policy forward, including a batch of \
training samples. For each element in list, the key of the dict is the name of data items and the \
value is the corresponding data. Usually, in the ``_forward_learn`` method, data should be stacked in \
the batch dimension by some utility functions such as ``default_preprocess_learn``.
Returns:
- output (:obj:`Dict[int, Any]`): The training information of policy forward, including some metrics for \
monitoring training such as loss, priority, q value, policy entropy, and some data for next step \
training such as priority. Note the output data item should be Python native scalar rather than \
PyTorch tensor, which is convenient for the outside to use.
"""
raise NotImplementedError
# don't need to implement _reset_learn method by force
def _reset_learn(self, data_id: Optional[List[int]] = None) -> None:
"""
Overview:
Reset some stateful variables for learn mode when necessary, such as the hidden state of RNN or the \
memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \
varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \
different trajectories in ``data_id`` will have different hidden state in RNN.
Arguments:
- data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \
specified by ``data_id``.
.. note::
This method is not mandatory to be implemented. The sub-class can overwrite this method if necessary.
"""
pass
def _monitor_vars_learn(self) -> List[str]:
"""
Overview:
Return the necessary keys for logging the return dict of ``self._forward_learn``. The logger module, such \
as text logger, tensorboard logger, will use these keys to save the corresponding data.
Returns:
- necessary_keys (:obj:`List[str]`): The list of the necessary keys to be logged.
.. tip::
The default implementation is ``['cur_lr', 'total_loss']``. Other derived classes can overwrite this \
method to add their own keys if necessary.
"""
return ['cur_lr', 'total_loss']
def _state_dict_learn(self) -> Dict[str, Any]:
"""
Overview:
Return the state_dict of learn mode, usually including model and optimizer.
Returns:
- state_dict (:obj:`Dict[str, Any]`): The dict of current policy learn state, for saving and restoring.
"""
return {
'model': self._learn_model.state_dict(),
'optimizer': self._optimizer.state_dict(),
}
def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None:
"""
Overview:
Load the state_dict variable into policy learn mode.
Arguments:
- state_dict (:obj:`Dict[str, Any]`): The dict of policy learn state saved before.
.. tip::
If you want to only load some parts of model, you can simply set the ``strict`` argument in \
load_state_dict to ``False``, or refer to ``ding.torch_utils.checkpoint_helper`` for more \
complicated operation.
"""
self._learn_model.load_state_dict(state_dict['model'])
self._optimizer.load_state_dict(state_dict['optimizer'])
def _get_batch_size(self) -> Union[int, Dict[str, int]]:
# some specifial algorithms use different batch size for different optimization parts.
if 'batch_size' in self._cfg:
return self._cfg.batch_size
else: # for compatibility
return self._cfg.learn.batch_size
# *************************************** collect function ************************************
@abstractmethod
def _forward_collect(self, data: Dict[int, Any], **kwargs) -> Dict[int, Any]:
"""
Overview:
Policy forward function of collect mode (collecting training data by interacting with envs). Forward means \
that the policy gets some necessary data (mainly observation) from the envs and then returns the output \
data, such as the action to interact with the envs, or the action logits to calculate the loss in learn \
mode. This method is left to be implemented by the subclass, and more arguments can be added in ``kwargs`` \
part if necessary.
Arguments:
- data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs. The \
key of the dict is environment id and the value is the corresponding data of the env.
Returns:
- output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action and \
other necessary data for learn mode defined in ``self._process_transition`` method. The key of the \
dict is the same as the input data, i.e. environment id.
"""
raise NotImplementedError
@abstractmethod
def _process_transition(
self, obs: Union[torch.Tensor, Dict[str, torch.Tensor]], policy_output: Dict[str, torch.Tensor],
timestep: namedtuple
) -> Dict[str, torch.Tensor]:
"""
Overview:
Process and pack one timestep transition data into a dict, such as <s, a, r, s', done>. Some policies \
need to do some special process and pack its own necessary attributes (e.g. hidden state and logit), \
so this method is left to be implemented by the subclass.
Arguments:
- obs (:obj:`Union[torch.Tensor, Dict[str, torch.Tensor]]`): The observation of the current timestep.
- policy_output (:obj:`Dict[str, torch.Tensor]`): The output of the policy network with the observation \
as input. Usually, it contains the action and the logit of the action.
- timestep (:obj:`namedtuple`): The execution result namedtuple returned by the environment step method, \
except all the elements have been transformed into tensor data. Usually, it contains the next obs, \
reward, done, info, etc.
Returns:
- transition (:obj:`Dict[str, torch.Tensor]`): The processed transition data of the current timestep.
"""
raise NotImplementedError
@abstractmethod
def _get_train_sample(self, transitions: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""
Overview:
For a given trajectory (transitions, a list of transition) data, process it into a list of sample that \
can be used for training directly. A train sample can be a processed transition (DQN with nstep TD) \
or some multi-timestep transitions (DRQN). This method is usually used in collectors to execute necessary \
RL data preprocessing before training, which can help learner amortize revelant time consumption. \
In addition, you can also implement this method as an identity function and do the data processing \
in ``self._forward_learn`` method.
Arguments:
- transitions (:obj:`List[Dict[str, Any]`): The trajectory data (a list of transition), each element is \
the same format as the return value of ``self._process_transition`` method.
Returns:
- samples (:obj:`List[Dict[str, Any]]`): The processed train samples, each element is the similar format \
as input transitions, but may contain more data for training, such as nstep reward, advantage, etc.
.. note::
We will vectorize ``process_transition`` and ``get_train_sample`` method in the following release version. \
And the user can customize the this data processing procecure by overriding this two methods and collector \
itself
"""
raise NotImplementedError
# don't need to implement _reset_collect method by force
def _reset_collect(self, data_id: Optional[List[int]] = None) -> None:
"""
Overview:
Reset some stateful variables for collect mode when necessary, such as the hidden state of RNN or the \
memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \
varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \
different environments/episodes in collecting in ``data_id`` will have different hidden state in RNN.
Arguments:
- data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \
specified by ``data_id``.
.. note::
This method is not mandatory to be implemented. The sub-class can overwrite this method if necessary.
"""
pass
def _state_dict_collect(self) -> Dict[str, Any]:
"""
Overview:
Return the state_dict of collect mode, only including model in usual, which is necessary for distributed \
training scenarios to auto-recover collectors.
Returns:
- state_dict (:obj:`Dict[str, Any]`): The dict of current policy collect state, for saving and restoring.
.. tip::
Not all the scenarios need to auto-recover collectors, sometimes, we can directly shutdown the crashed \
collector and renew a new one.
"""
return {'model': self._collect_model.state_dict()}
def _load_state_dict_collect(self, state_dict: Dict[str, Any]) -> None:
"""
Overview:
Load the state_dict variable into policy collect mode, such as load pretrained state_dict, auto-recover \
checkpoint, or model replica from learner in distributed training scenarios.
Arguments:
- state_dict (:obj:`Dict[str, Any]`): The dict of policy collect state saved before.
.. tip::
If you want to only load some parts of model, you can simply set the ``strict`` argument in \
load_state_dict to ``False``, or refer to ``ding.torch_utils.checkpoint_helper`` for more \
complicated operation.
"""
self._collect_model.load_state_dict(state_dict['model'], strict=True)
def _get_n_sample(self) -> Union[int, None]:
if 'n_sample' in self._cfg:
return self._cfg.n_sample
else: # for compatibility
return self._cfg.collect.get('n_sample', None) # for some adpative collecting data case
def _get_n_episode(self) -> Union[int, None]:
if 'n_episode' in self._cfg:
return self._cfg.n_episode
else: # for compatibility
return self._cfg.collect.get('n_episode', None) # for some adpative collecting data case
# *************************************** eval function ************************************
@abstractmethod
def _forward_eval(self, data: Dict[int, Any]) -> Dict[int, Any]:
"""
Overview:
Policy forward function of eval mode (evaluation policy performance, such as interacting with envs or \
computing metrics on validation dataset). Forward means that the policy gets some necessary data (mainly \
observation) from the envs and then returns the output data, such as the action to interact with the envs. \
This method is left to be implemented by the subclass.
Arguments:
- data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs. The \
key of the dict is environment id and the value is the corresponding data of the env.
Returns:
- output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action. The \
key of the dict is the same as the input data, i.e. environment id.
"""
raise NotImplementedError
# don't need to implement _reset_eval method by force
def _reset_eval(self, data_id: Optional[List[int]] = None) -> None:
"""
Overview:
Reset some stateful variables for eval mode when necessary, such as the hidden state of RNN or the \
memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \
varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \
different environments/episodes in evaluation in ``data_id`` will have different hidden state in RNN.
Arguments:
- data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \
specified by ``data_id``.
.. note::
This method is not mandatory to be implemented. The sub-class can overwrite this method if necessary.
"""
pass
def _state_dict_eval(self) -> Dict[str, Any]:
"""
Overview:
Return the state_dict of eval mode, only including model in usual, which is necessary for distributed \
training scenarios to auto-recover evaluators.
Returns:
- state_dict (:obj:`Dict[str, Any]`): The dict of current policy eval state, for saving and restoring.
.. tip::
Not all the scenarios need to auto-recover evaluators, sometimes, we can directly shutdown the crashed \
evaluator and renew a new one.
"""
return {'model': self._eval_model.state_dict()}
def _load_state_dict_eval(self, state_dict: Dict[str, Any]) -> None:
"""
Overview:
Load the state_dict variable into policy eval mode, such as load auto-recover \
checkpoint, or model replica from learner in distributed training scenarios.
Arguments:
- state_dict (:obj:`Dict[str, Any]`): The dict of policy eval state saved before.
.. tip::
If you want to only load some parts of model, you can simply set the ``strict`` argument in \
load_state_dict to ``False``, or refer to ``ding.torch_utils.checkpoint_helper`` for more \
complicated operation.
"""
self._eval_model.load_state_dict(state_dict['model'], strict=True)
class CommandModePolicy(Policy):
"""
Overview:
Policy with command mode, which can be used in old version of DI-engine pipeline: ``serial_pipeline``. \
``CommandModePolicy`` uses ``_get_setting_learn``, ``_get_setting_collect``, ``_get_setting_eval`` methods \
to exchange information between different workers.
Interface:
``_init_command``, ``_get_setting_learn``, ``_get_setting_collect``, ``_get_setting_eval``
Property:
``command_mode``
"""
command_function = namedtuple('command_function', ['get_setting_learn', 'get_setting_collect', 'get_setting_eval'])
total_field = set(['learn', 'collect', 'eval', 'command'])
@property
def command_mode(self) -> 'Policy.command_function': # noqa
"""
Overview:
Return the interfaces of command mode of policy, which is used to train the model. Here we use namedtuple \
to define immutable interfaces and restrict the usage of policy in different mode. Moreover, derived \
subclass can override the interfaces to customize its own command mode.
Returns:
- interfaces (:obj:`Policy.command_function`): The interfaces of command mode, it is a namedtuple \
whose values of distinct fields are different internal methods.
Examples:
>>> policy = CommandModePolicy(cfg, model)
>>> policy_command = policy.command_mode
>>> settings = policy_command.get_setting_learn(command_info)
"""
return CommandModePolicy.command_function(
self._get_setting_learn, self._get_setting_collect, self._get_setting_eval
)
@abstractmethod
def _init_command(self) -> None:
"""
Overview:
Initialize the command mode of policy, including related attributes and modules. This method will be \
called in ``__init__`` method if ``command`` field is in ``enable_field``. Almost different policies have \
its own command mode, so this method must be overrided in subclass.
.. note::
If you want to set some spacial member variables in ``_init_command`` method, you'd better name them \
with prefix ``_command_`` to avoid conflict with other modes, such as ``self._command_attr1``.
"""
raise NotImplementedError
# *************************************** command function ************************************
@abstractmethod
def _get_setting_learn(self, command_info: Dict[str, Any]) -> Dict[str, Any]:
"""
Overview:
Accoding to ``command_info``, i.e., global training information (e.g. training iteration, collected env \
step, evaluation results, etc.), return the setting of learn mode, which contains dynamically changed \
hyperparameters for learn mode, such as ``batch_size``, ``learning_rate``, etc.
Arguments:
- command_info (:obj:`Dict[str, Any]`): The global training information, which is defined in ``commander``.
Returns:
- setting (:obj:`Dict[str, Any]`): The latest setting of learn mode, which is usually used as extra \
arguments of the ``policy._forward_learn`` method.
"""
raise NotImplementedError
@abstractmethod
def _get_setting_collect(self, command_info: Dict[str, Any]) -> Dict[str, Any]:
"""
Overview:
Accoding to ``command_info``, i.e., global training information (e.g. training iteration, collected env \
step, evaluation results, etc.), return the setting of collect mode, which contains dynamically changed \
hyperparameters for collect mode, such as ``eps``, ``temperature``, etc.
Arguments:
- command_info (:obj:`Dict[str, Any]`): The global training information, which is defined in ``commander``.
Returns:
- setting (:obj:`Dict[str, Any]`): The latest setting of collect mode, which is usually used as extra \
arguments of the ``policy._forward_collect`` method.
"""
raise NotImplementedError
@abstractmethod
def _get_setting_eval(self, command_info: Dict[str, Any]) -> Dict[str, Any]:
"""
Overview:
Accoding to ``command_info``, i.e., global training information (e.g. training iteration, collected env \
step, evaluation results, etc.), return the setting of eval mode, which contains dynamically changed \
hyperparameters for eval mode, such as ``temperature``, etc.
Arguments:
- command_info (:obj:`Dict[str, Any]`): The global training information, which is defined in ``commander``.
Returns:
- setting (:obj:`Dict[str, Any]`): The latest setting of eval mode, which is usually used as extra \
arguments of the ``policy._forward_eval`` method.
"""
raise NotImplementedError
def create_policy(cfg: EasyDict, **kwargs) -> Policy:
"""
Overview:
Create a policy instance according to ``cfg`` and other kwargs.
Arguments:
- cfg (:obj:`EasyDict`): Final merged policy config.
ArgumentsKeys:
- type (:obj:`str`): Policy type set in ``POLICY_REGISTRY.register`` method , such as ``dqn`` .
- import_names (:obj:`List[str]`): A list of module names (paths) to import before creating policy, such \
as ``ding.policy.dqn`` .
Returns:
- policy (:obj:`Policy`): The created policy instance.
.. tip::
``kwargs`` contains other arguments that need to be passed to the policy constructor. You can refer to \
the ``__init__`` method of the corresponding policy class for details.
.. note::
For more details about how to merge config, please refer to the system document of DI-engine \
(`en link <../03_system/config.html>`_).
"""
import_module(cfg.get('import_names', []))
return POLICY_REGISTRY.build(cfg.type, cfg=cfg, **kwargs)
def get_policy_cls(cfg: EasyDict) -> type:
"""
Overview:
Get policy class according to ``cfg``, which is used to access related class variables/methods.
Arguments:
- cfg (:obj:`EasyDict`): Final merged policy config.
ArgumentsKeys:
- type (:obj:`str`): Policy type set in ``POLICY_REGISTRY.register`` method , such as ``dqn`` .
- import_names (:obj:`List[str]`): A list of module names (paths) to import before creating policy, such \
as ``ding.policy.dqn`` .
Returns:
- policy (:obj:`type`): The policy class.
"""
import_module(cfg.get('import_names', []))
return POLICY_REGISTRY.get(cfg.type)