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from collections import namedtuple |
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from typing import List, Dict, Any, Tuple |
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import torch |
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import treetensor.torch as ttorch |
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from ding.model import model_wrap |
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from ding.rl_utils import vtrace_data, vtrace_error_discrete_action, vtrace_error_continuous_action, get_train_sample |
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from ding.torch_utils import Adam, RMSprop, to_device |
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from ding.utils import POLICY_REGISTRY |
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from ding.utils.data import default_collate, default_decollate, ttorch_collate |
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from ding.policy.base_policy import Policy |
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@POLICY_REGISTRY.register('impala') |
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class IMPALAPolicy(Policy): |
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""" |
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Overview: |
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Policy class of IMPALA algorithm. Paper link: https://arxiv.org/abs/1802.01561. |
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Config: |
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== ==================== ======== ============== ======================================== ======================= |
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ID Symbol Type Default Value Description Other(Shape) |
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== ==================== ======== ============== ======================================== ======================= |
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1 ``type`` str impala | RL policy register name, refer to | this arg is optional, |
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| registry ``POLICY_REGISTRY`` | a placeholder |
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2 ``cuda`` bool False | Whether to use cuda for network | this arg can be diff- |
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| erent from modes |
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3 ``on_policy`` bool False | Whether the RL algorithm is on-policy |
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| or off-policy |
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4. ``priority`` bool False | Whether use priority(PER) | priority sample, |
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| update priority |
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5 | ``priority_`` bool False | Whether use Importance Sampling Weight | If True, priority |
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| ``IS_weight`` | | must be True |
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6 ``unroll_len`` int 32 | trajectory length to calculate v-trace |
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| target |
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7 | ``learn.update`` int 4 | How many updates(iterations) to train | this args can be vary |
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| ``per_collect`` | after collector's one collection. Only | from envs. Bigger val |
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| valid in serial training | means more off-policy |
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== ==================== ======== ============== ======================================== ======================= |
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""" |
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config = dict( |
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type='impala', |
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cuda=False, |
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on_policy=False, |
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priority=False, |
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priority_IS_weight=False, |
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action_space='discrete', |
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unroll_len=32, |
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transition_with_policy_data=True, |
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learn=dict( |
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update_per_collect=4, |
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batch_size=16, |
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learning_rate=0.0005, |
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value_weight=0.5, |
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entropy_weight=0.0001, |
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discount_factor=0.99, |
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lambda_=0.95, |
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rho_clip_ratio=1.0, |
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c_clip_ratio=1.0, |
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rho_pg_clip_ratio=1.0, |
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grad_clip_type=None, |
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clip_value=0.5, |
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optim='adam', |
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), |
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collect=dict( |
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), |
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eval=dict(), |
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other=dict( |
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replay_buffer=dict( |
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replay_buffer_size=1000, |
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max_use=16, |
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), |
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), |
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) |
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def default_model(self) -> Tuple[str, List[str]]: |
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""" |
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Overview: |
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Return this algorithm default neural network model setting for demonstration. ``__init__`` method will \ |
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automatically call this method to get the default model setting and create model. |
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Returns: |
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- model_info (:obj:`Tuple[str, List[str]]`): The registered model name and model's import_names. |
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.. note:: |
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The user can define and use customized network model but must obey the same inferface definition indicated \ |
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by import_names path. For example about IMPALA , its registered name is ``vac`` and the import_names is \ |
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``ding.model.template.vac``. |
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""" |
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return 'vac', ['ding.model.template.vac'] |
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def _init_learn(self) -> None: |
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""" |
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Overview: |
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Initialize the learn mode of policy, including related attributes and modules. For IMPALA, it mainly \ |
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contains optimizer, algorithm-specific arguments such as loss weight and gamma, main (learn) model. |
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This method will be called in ``__init__`` method if ``learn`` field is in ``enable_field``. |
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.. note:: |
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For the member variables that need to be saved and loaded, please refer to the ``_state_dict_learn`` \ |
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and ``_load_state_dict_learn`` methods. |
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.. note:: |
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For the member variables that need to be monitored, please refer to the ``_monitor_vars_learn`` method. |
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.. note:: |
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If you want to set some spacial member variables in ``_init_learn`` method, you'd better name them \ |
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with prefix ``_learn_`` to avoid conflict with other modes, such as ``self._learn_attr1``. |
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""" |
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assert self._cfg.action_space in ["continuous", "discrete"], self._cfg.action_space |
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self._action_space = self._cfg.action_space |
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optim_type = self._cfg.learn.optim |
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if optim_type == 'rmsprop': |
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self._optimizer = RMSprop(self._model.parameters(), lr=self._cfg.learn.learning_rate) |
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elif optim_type == 'adam': |
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self._optimizer = Adam( |
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self._model.parameters(), |
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grad_clip_type=self._cfg.learn.grad_clip_type, |
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clip_value=self._cfg.learn.clip_value, |
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lr=self._cfg.learn.learning_rate |
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) |
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else: |
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raise NotImplementedError("Now only support rmsprop and adam, but input is {}".format(optim_type)) |
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self._learn_model = model_wrap(self._model, wrapper_name='base') |
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self._action_shape = self._cfg.model.action_shape |
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self._unroll_len = self._cfg.unroll_len |
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self._priority = self._cfg.priority |
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self._priority_IS_weight = self._cfg.priority_IS_weight |
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self._value_weight = self._cfg.learn.value_weight |
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self._entropy_weight = self._cfg.learn.entropy_weight |
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self._gamma = self._cfg.learn.discount_factor |
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self._lambda = self._cfg.learn.lambda_ |
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self._rho_clip_ratio = self._cfg.learn.rho_clip_ratio |
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self._c_clip_ratio = self._cfg.learn.c_clip_ratio |
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self._rho_pg_clip_ratio = self._cfg.learn.rho_pg_clip_ratio |
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self._learn_model.reset() |
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def _data_preprocess_learn(self, data: List[Dict[str, Any]]): |
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""" |
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Overview: |
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Data preprocess function of learn mode. |
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Convert list trajectory data to to trajectory data, which is a dict of tensors. |
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Arguments: |
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- data (:obj:`List[Dict[str, Any]]`): List type data, a list of data for training. Each list element is a \ |
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dict, whose values are torch.Tensor or np.ndarray or dict/list combinations, keys include at least \ |
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'obs', 'next_obs', 'logit', 'action', 'reward', 'done' |
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Returns: |
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- data (:obj:`dict`): Dict type data. Values are torch.Tensor or np.ndarray or dict/list combinations. \ |
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ReturnsKeys: |
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- necessary: 'logit', 'action', 'reward', 'done', 'weight', 'obs_plus_1'. |
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- optional and not used in later computation: 'obs', 'next_obs'.'IS', 'collect_iter', 'replay_unique_id', \ |
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'replay_buffer_idx', 'priority', 'staleness', 'use'. |
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ReturnsShapes: |
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- obs_plus_1 (:obj:`torch.FloatTensor`): :math:`(T * B, obs_shape)`, where T is timestep, B is batch size \ |
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and obs_shape is the shape of single env observation |
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- logit (:obj:`torch.FloatTensor`): :math:`(T, B, N)`, where N is action dim |
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- action (:obj:`torch.LongTensor`): :math:`(T, B)` |
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- reward (:obj:`torch.FloatTensor`): :math:`(T+1, B)` |
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- done (:obj:`torch.FloatTensor`): :math:`(T, B)` |
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- weight (:obj:`torch.FloatTensor`): :math:`(T, B)` |
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""" |
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elem = data[0] |
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if isinstance(elem, dict): |
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data = default_collate(data) |
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elif isinstance(elem, list): |
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data = default_collate(default_collate(data)) |
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else: |
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raise TypeError("not support element type ({}) in IMPALA".format(type(elem))) |
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if self._cuda: |
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data = to_device(data, self._device) |
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if self._priority_IS_weight: |
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assert self._priority, "Use IS Weight correction, but Priority is not used." |
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if self._priority and self._priority_IS_weight: |
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data['weight'] = data['IS'] |
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else: |
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data['weight'] = data.get('weight', None) |
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if isinstance(elem, dict): |
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for k in data: |
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if isinstance(data[k], list): |
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data[k] = default_collate(data[k]) |
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data['obs_plus_1'] = torch.cat([data['obs'], data['next_obs'][-1:]], dim=0) |
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return data |
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def _forward_learn(self, data: List[Dict[str, Any]]) -> Dict[str, Any]: |
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""" |
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Overview: |
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Policy forward function of learn mode (training policy and updating parameters). Forward means \ |
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that the policy inputs some training batch data from the replay buffer and then returns the output \ |
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result, including various training information such as loss and current learning rate. |
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Arguments: |
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- data (:obj:`List[Dict[int, Any]]`): The input data used for policy forward, including a batch of \ |
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training samples. For each element in list, the key of the dict is the name of data items and the \ |
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value is the corresponding data. Usually, the value is torch.Tensor or np.ndarray or there dict/list \ |
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combinations. In the ``_forward_learn`` method, data often need to first be stacked in the batch \ |
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dimension by some utility functions such as ``default_preprocess_learn``. \ |
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For IMPALA, each element in list is a dict containing at least the following keys: ``obs``, \ |
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``action``, ``logit``, ``reward``, ``next_obs``, ``done``. Sometimes, it also contains other keys such \ |
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as ``weight``. |
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Returns: |
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- info_dict (:obj:`Dict[str, Any]`): The information dict that indicated training result, which will be \ |
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recorded in text log and tensorboard, values must be python scalar or a list of scalars. For the \ |
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detailed definition of the dict, refer to the code of ``_monitor_vars_learn`` method. |
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.. note:: |
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The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ |
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For the data type that not supported, the main reason is that the corresponding model does not support it. \ |
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You can implement you own model rather than use the default model. For more information, please raise an \ |
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issue in GitHub repo and we will continue to follow up. |
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.. note:: |
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For more detailed examples, please refer to unittest for IMPALAPolicy: ``ding.policy.tests.test_impala``. |
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""" |
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data = self._data_preprocess_learn(data) |
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self._learn_model.train() |
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output = self._learn_model.forward( |
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data['obs_plus_1'].view((-1, ) + data['obs_plus_1'].shape[2:]), mode='compute_actor_critic' |
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) |
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target_logit, behaviour_logit, actions, values, rewards, weights = self._reshape_data(output, data) |
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data = vtrace_data(target_logit, behaviour_logit, actions, values, rewards, weights) |
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g, l, r, c, rg = self._gamma, self._lambda, self._rho_clip_ratio, self._c_clip_ratio, self._rho_pg_clip_ratio |
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if self._action_space == 'continuous': |
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vtrace_loss = vtrace_error_continuous_action(data, g, l, r, c, rg) |
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elif self._action_space == 'discrete': |
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vtrace_loss = vtrace_error_discrete_action(data, g, l, r, c, rg) |
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wv, we = self._value_weight, self._entropy_weight |
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total_loss = vtrace_loss.policy_loss + wv * vtrace_loss.value_loss - we * vtrace_loss.entropy_loss |
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self._optimizer.zero_grad() |
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total_loss.backward() |
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self._optimizer.step() |
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return { |
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'cur_lr': self._optimizer.defaults['lr'], |
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'total_loss': total_loss.item(), |
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'policy_loss': vtrace_loss.policy_loss.item(), |
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'value_loss': vtrace_loss.value_loss.item(), |
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'entropy_loss': vtrace_loss.entropy_loss.item(), |
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} |
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def _reshape_data(self, output: Dict[str, Any], data: Dict[str, Any]) -> Tuple: |
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""" |
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Overview: |
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Obtain weights for loss calculating, where should be 0 for done positions. Update values and rewards with \ |
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the weight. |
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Arguments: |
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- output (:obj:`Dict[int, Any]`): Dict type data, output of learn_model forward. \ |
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Values are torch.Tensor or np.ndarray or dict/list combinations,keys are value, logit. |
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- data (:obj:`Dict[int, Any]`): Dict type data, input of policy._forward_learn Values are torch.Tensor or \ |
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np.ndarray or dict/list combinations. Keys includes at least ['logit', 'action', 'reward', 'done']. |
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Returns: |
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- data (:obj:`Tuple[Any]`): Tuple of target_logit, behaviour_logit, actions, values, rewards, weights. |
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ReturnsShapes: |
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- target_logit (:obj:`torch.FloatTensor`): :math:`((T+1), B, Obs_Shape)`, where T is timestep,\ |
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B is batch size and Obs_Shape is the shape of single env observation. |
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- behaviour_logit (:obj:`torch.FloatTensor`): :math:`(T, B, N)`, where N is action dim. |
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- actions (:obj:`torch.LongTensor`): :math:`(T, B)` |
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- values (:obj:`torch.FloatTensor`): :math:`(T+1, B)` |
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- rewards (:obj:`torch.FloatTensor`): :math:`(T, B)` |
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- weights (:obj:`torch.FloatTensor`): :math:`(T, B)` |
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""" |
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if self._action_space == 'continuous': |
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target_logit = {} |
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target_logit['mu'] = output['logit']['mu'].reshape(self._unroll_len + 1, -1, |
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self._action_shape)[:-1 |
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] |
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target_logit['sigma'] = output['logit']['sigma'].reshape(self._unroll_len + 1, -1, self._action_shape |
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)[:-1] |
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elif self._action_space == 'discrete': |
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target_logit = output['logit'].reshape(self._unroll_len + 1, -1, |
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self._action_shape)[:-1] |
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behaviour_logit = data['logit'] |
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actions = data['action'] |
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values = output['value'].reshape(self._unroll_len + 1, -1) |
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rewards = data['reward'] |
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weights_ = 1 - data['done'].float() |
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weights = torch.ones_like(rewards) |
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values[1:] = values[1:] * weights_ |
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weights[1:] = weights_[:-1] |
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rewards = rewards * weights |
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return target_logit, behaviour_logit, actions, values, rewards, weights |
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def _init_collect(self) -> None: |
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""" |
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Overview: |
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Initialize the collect mode of policy, including related attributes and modules. For IMPALA, it contains \ |
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the collect_model to balance the exploration and exploitation (e.g. the multinomial sample mechanism in \ |
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discrete action space), and other algorithm-specific arguments such as unroll_len. |
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This method will be called in ``__init__`` method if ``collect`` field is in ``enable_field``. |
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.. note:: |
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If you want to set some spacial member variables in ``_init_collect`` method, you'd better name them \ |
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with prefix ``_collect_`` to avoid conflict with other modes, such as ``self._collect_attr1``. |
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""" |
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assert self._cfg.action_space in ["continuous", "discrete"] |
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self._action_space = self._cfg.action_space |
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if self._action_space == 'continuous': |
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self._collect_model = model_wrap(self._model, wrapper_name='reparam_sample') |
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elif self._action_space == 'discrete': |
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self._collect_model = model_wrap(self._model, wrapper_name='multinomial_sample') |
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self._collect_model.reset() |
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def _forward_collect(self, data: Dict[int, Any]) -> Dict[int, Any]: |
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""" |
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Overview: |
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Policy forward function of collect mode (collecting training data by interacting with envs). Forward means \ |
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that the policy gets some necessary data (mainly observation) from the envs and then returns the output \ |
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data, such as the action to interact with the envs. |
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Arguments: |
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- data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs. The \ |
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key of the dict is environment id and the value is the corresponding data of the env. |
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Returns: |
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- output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action and \ |
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other necessary data (action logit and value) for learn mode defined in ``self._process_transition`` \ |
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method. The key of the dict is the same as the input data, i.e. environment id. |
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.. tip:: |
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If you want to add more tricks on this policy, like temperature factor in multinomial sample, you can pass \ |
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related data as extra keyword arguments of this method. |
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.. note:: |
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The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ |
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For the data type that not supported, the main reason is that the corresponding model does not support it. \ |
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You can implement you own model rather than use the default model. For more information, please raise an \ |
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issue in GitHub repo and we will continue to follow up. |
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.. note:: |
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For more detailed examples, please refer to unittest for IMPALAPolicy: ``ding.policy.tests.test_impala``. |
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""" |
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data_id = list(data.keys()) |
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data = default_collate(list(data.values())) |
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if self._cuda: |
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data = to_device(data, self._device) |
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self._collect_model.eval() |
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with torch.no_grad(): |
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output = self._collect_model.forward(data, mode='compute_actor') |
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if self._cuda: |
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output = to_device(output, 'cpu') |
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output = default_decollate(output) |
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output = {i: d for i, d in zip(data_id, output)} |
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return output |
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def _get_train_sample(self, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]: |
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""" |
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Overview: |
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For a given trajectory (transitions, a list of transition) data, process it into a list of sample that \ |
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can be used for training. In IMPALA, a train sample is processed transitions with unroll_len length. |
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Arguments: |
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- transitions (:obj:`List[Dict[str, Any]`): The trajectory data (a list of transition), each element is \ |
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the same format as the return value of ``self._process_transition`` method. |
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Returns: |
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- samples (:obj:`List[Dict[str, Any]]`): The processed train samples, each element is the similar format \ |
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as input transitions, but may contain more data for training. |
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""" |
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return get_train_sample(data, self._unroll_len) |
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def _process_transition(self, obs: torch.Tensor, policy_output: Dict[str, torch.Tensor], |
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timestep: namedtuple) -> Dict[str, torch.Tensor]: |
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""" |
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Overview: |
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Process and pack one timestep transition data into a dict, which can be directly used for training and \ |
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saved in replay buffer. For IMPALA, it contains obs, next_obs, action, reward, done, logit. |
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Arguments: |
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- obs (:obj:`torch.Tensor`): The env observation of current timestep, such as stacked 2D image in Atari. |
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- policy_output (:obj:`Dict[str, torch.Tensor]`): The output of the policy network with the observation \ |
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as input. For IMPALA, it contains the action and the logit of the action. |
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- timestep (:obj:`namedtuple`): The execution result namedtuple returned by the environment step method, \ |
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except all the elements have been transformed into tensor data. Usually, it contains the next obs, \ |
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reward, done, info, etc. |
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Returns: |
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- transition (:obj:`Dict[str, torch.Tensor]`): The processed transition data of the current timestep. |
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""" |
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transition = { |
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'obs': obs, |
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'next_obs': timestep.obs, |
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'logit': policy_output['logit'], |
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'action': policy_output['action'], |
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'reward': timestep.reward, |
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'done': timestep.done, |
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} |
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return transition |
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|
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def _init_eval(self) -> None: |
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""" |
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Overview: |
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Initialize the eval mode of policy, including related attributes and modules. For IMPALA, it contains the \ |
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eval model to select optimial action (e.g. greedily select action with argmax mechanism in discrete action). |
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This method will be called in ``__init__`` method if ``eval`` field is in ``enable_field``. |
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|
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.. note:: |
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If you want to set some spacial member variables in ``_init_eval`` method, you'd better name them \ |
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with prefix ``_eval_`` to avoid conflict with other modes, such as ``self._eval_attr1``. |
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""" |
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assert self._cfg.action_space in ["continuous", "discrete"], self._cfg.action_space |
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self._action_space = self._cfg.action_space |
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if self._action_space == 'continuous': |
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self._eval_model = model_wrap(self._model, wrapper_name='deterministic_sample') |
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elif self._action_space == 'discrete': |
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self._eval_model = model_wrap(self._model, wrapper_name='argmax_sample') |
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|
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self._eval_model.reset() |
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|
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def _forward_eval(self, data: Dict[int, Any]) -> Dict[int, Any]: |
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""" |
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Overview: |
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Policy forward function of eval mode (evaluation policy performance by interacting with envs). Forward \ |
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means that the policy gets some necessary data (mainly observation) from the envs and then returns the \ |
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action to interact with the envs. ``_forward_eval`` in IMPALA often uses deterministic sample to get \ |
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actions while ``_forward_collect`` usually uses stochastic sample method for balance exploration and \ |
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exploitation. |
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Arguments: |
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- data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs. The \ |
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key of the dict is environment id and the value is the corresponding data of the env. |
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Returns: |
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- output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action. The \ |
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key of the dict is the same as the input data, i.e. environment id. |
|
|
|
.. note:: |
|
The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ |
|
For the data type that not supported, the main reason is that the corresponding model does not support it. \ |
|
You can implement you own model rather than use the default model. For more information, please raise an \ |
|
issue in GitHub repo and we will continue to follow up. |
|
|
|
.. note:: |
|
For more detailed examples, please refer to unittest for IMPALAPolicy: ``ding.policy.tests.test_impala``. |
|
""" |
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data_id = list(data.keys()) |
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data = default_collate(list(data.values())) |
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if self._cuda: |
|
data = to_device(data, self._device) |
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self._eval_model.eval() |
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with torch.no_grad(): |
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output = self._eval_model.forward(data, mode='compute_actor') |
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if self._cuda: |
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output = to_device(output, 'cpu') |
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output = default_decollate(output) |
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output = {i: d for i, d in zip(data_id, output)} |
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return output |
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|
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def _monitor_vars_learn(self) -> List[str]: |
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""" |
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Overview: |
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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. |
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Returns: |
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- necessary_keys (:obj:`List[str]`): The list of the necessary keys to be logged. |
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""" |
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return super()._monitor_vars_learn() + ['policy_loss', 'value_loss', 'entropy_loss'] |
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|