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Source code for ding.policy.bc

import math
import torch
import torch.nn as nn
import copy
from torch.optim import Adam, SGD, AdamW
from torch.optim.lr_scheduler import LambdaLR
import logging
from typing import List, Dict, Any, Tuple, Union, Optional
from collections import namedtuple
from easydict import EasyDict
from ding.policy import Policy
from ding.model import model_wrap
from ding.torch_utils import to_device, to_list
from ding.utils import EasyTimer
from ding.utils.data import default_collate, default_decollate
from ding.rl_utils import get_nstep_return_data, get_train_sample
from ding.utils import POLICY_REGISTRY
from ding.torch_utils.loss.cross_entropy_loss import LabelSmoothCELoss


[docs]@POLICY_REGISTRY.register('bc') class BehaviourCloningPolicy(Policy): """ Overview: Behaviour Cloning (BC) policy class, which supports both discrete and continuous action space. \ The policy is trained by supervised learning, and the data is a offline dataset collected by expert. """ config = dict( type='bc', cuda=False, on_policy=False, continuous=False, action_shape=19, learn=dict( update_per_collect=1, batch_size=32, learning_rate=1e-5, lr_decay=False, decay_epoch=30, decay_rate=0.1, warmup_lr=1e-4, warmup_epoch=3, optimizer='SGD', momentum=0.9, weight_decay=1e-4, ce_label_smooth=False, show_accuracy=False, tanh_mask=False, # if actions always converge to 1 or -1, use this. ), collect=dict( unroll_len=1, noise=False, noise_sigma=0.2, noise_range=dict( min=-0.5, max=0.5, ), ), eval=dict(), # for compatibility )
[docs] 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 discrete BC, its registered name is ``discrete_bc`` and the \ import_names is ``ding.model.template.bc``. """ if self._cfg.continuous: return 'continuous_bc', ['ding.model.template.bc'] else: return 'discrete_bc', ['ding.model.template.bc']
[docs] def _init_learn(self) -> None: """ Overview: Initialize the learn mode of policy, including related attributes and modules. For BC, it mainly contains \ optimizer, algorithm-specific arguments such as lr_scheduler, loss, etc. \ This method will be called in ``__init__`` method if ``learn`` field is in ``enable_field``. .. 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``. """ assert self._cfg.learn.optimizer in ['SGD', 'Adam'], self._cfg.learn.optimizer if self._cfg.learn.optimizer == 'SGD': self._optimizer = SGD( self._model.parameters(), lr=self._cfg.learn.learning_rate, weight_decay=self._cfg.learn.weight_decay, momentum=self._cfg.learn.momentum ) elif self._cfg.learn.optimizer == 'Adam': if self._cfg.learn.weight_decay is None: self._optimizer = Adam( self._model.parameters(), lr=self._cfg.learn.learning_rate, ) else: self._optimizer = AdamW( self._model.parameters(), lr=self._cfg.learn.learning_rate, weight_decay=self._cfg.learn.weight_decay ) if self._cfg.learn.lr_decay: def lr_scheduler_fn(epoch): if epoch <= self._cfg.learn.warmup_epoch: return self._cfg.learn.warmup_lr / self._cfg.learn.learning_rate else: ratio = (epoch - self._cfg.learn.warmup_epoch) // self._cfg.learn.decay_epoch return math.pow(self._cfg.learn.decay_rate, ratio) self._lr_scheduler = LambdaLR(self._optimizer, lr_scheduler_fn) self._timer = EasyTimer(cuda=True) self._learn_model = model_wrap(self._model, 'base') self._learn_model.reset() if self._cfg.continuous: if self._cfg.loss_type == 'l1_loss': self._loss = nn.L1Loss() elif self._cfg.loss_type == 'mse_loss': self._loss = nn.MSELoss() else: raise KeyError("not support loss type: {}".format(self._cfg.loss_type)) else: if not self._cfg.learn.ce_label_smooth: self._loss = nn.CrossEntropyLoss() else: self._loss = LabelSmoothCELoss(0.1)
[docs] 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 and time. 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, the value is torch.Tensor or np.ndarray or there dict/list \ combinations. In the ``_forward_learn`` method, data often need to first be stacked in the batch \ dimension by some utility functions such as ``default_preprocess_learn``. \ For BC, each element in list is a dict containing at least the following keys: ``obs``, ``action``. Returns: - info_dict (:obj:`Dict[str, Any]`): The information dict that indicated training result, which will be \ recorded in text log and tensorboard, values must be python scalar or a list of scalars. For the \ detailed definition of the dict, refer to the code of ``_monitor_vars_learn`` method. .. 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. """ if isinstance(data, list): data = default_collate(data) if self._cuda: data = to_device(data, self._device) self._learn_model.train() with self._timer: obs, action = data['obs'], data['action'].squeeze() if self._cfg.continuous: if self._cfg.learn.tanh_mask: """tanh_mask We mask the action out of range of [tanh(-1),tanh(1)], model will learn information and produce action in [-1,1]. So the action won't always converge to -1 or 1. """ mu = self._eval_model.forward(data['obs'])['action'] bound = 1 - 2 / (math.exp(2) + 1) # tanh(1): (e-e**(-1))/(e+e**(-1)) mask = mu.ge(-bound) & mu.le(bound) mask_percent = 1 - mask.sum().item() / mu.numel() if mask_percent > 0.8: # if there is too little data to learn(<80%). So we use all data. loss = self._loss(mu, action.detach()) else: loss = self._loss(mu.masked_select(mask), action.masked_select(mask).detach()) else: mu = self._learn_model.forward(data['obs'])['action'] # When we use bco, action is predicted by idm, gradient is not expected. loss = self._loss(mu, action.detach()) else: a_logit = self._learn_model.forward(obs) # When we use bco, action is predicted by idm, gradient is not expected. loss = self._loss(a_logit['logit'], action.detach()) if self._cfg.learn.show_accuracy: # Calculate the overall accuracy and the accuracy of each class total_accuracy = (a_logit['action'] == action.view(-1)).float().mean() self.total_accuracy_in_dataset.append(total_accuracy) logging.info(f'the total accuracy in current train mini-batch is: {total_accuracy.item()}') for action_unique in to_list(torch.unique(action)): action_index = (action == action_unique).nonzero(as_tuple=True)[0] action_accuracy = (a_logit['action'][action_index] == action.view(-1)[action_index] ).float().mean() if math.isnan(action_accuracy): action_accuracy = 0.0 self.action_accuracy_in_dataset[action_unique].append(action_accuracy) logging.info( f'the accuracy of action {action_unique} in current train mini-batch is: ' f'{action_accuracy.item()}, ' f'(nan means the action does not appear in the mini-batch)' ) forward_time = self._timer.value with self._timer: self._optimizer.zero_grad() loss.backward() backward_time = self._timer.value with self._timer: if self._cfg.multi_gpu: self.sync_gradients(self._learn_model) sync_time = self._timer.value self._optimizer.step() cur_lr = [param_group['lr'] for param_group in self._optimizer.param_groups] cur_lr = sum(cur_lr) / len(cur_lr) return { 'cur_lr': cur_lr, 'total_loss': loss.item(), 'forward_time': forward_time, 'backward_time': backward_time, 'sync_time': sync_time, }
[docs] 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. """ return ['cur_lr', 'total_loss', 'forward_time', 'backward_time', 'sync_time']
[docs] def _init_eval(self): """ Overview: Initialize the eval mode of policy, including related attributes and modules. For BC, it contains the \ eval model to greedily select action with argmax q_value mechanism for discrete action space. This method will be called in ``__init__`` method if ``eval`` field is in ``enable_field``. .. 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``. """ if self._cfg.continuous: self._eval_model = model_wrap(self._model, wrapper_name='base') else: self._eval_model = model_wrap(self._model, wrapper_name='argmax_sample') self._eval_model.reset()
[docs] def _forward_eval(self, data: Dict[int, Any]) -> Dict[int, Any]: """ Overview: Policy forward function of eval mode (evaluation policy performance by interacting with envs). Forward \ means that the policy gets some necessary data (mainly observation) from the envs and then returns the \ action to interact with the envs. 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. .. 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. """ tensor_input = isinstance(data, torch.Tensor) if tensor_input: data = default_collate(list(data)) else: data_id = list(data.keys()) data = default_collate(list(data.values())) if self._cuda: data = to_device(data, self._device) self._eval_model.eval() with torch.no_grad(): output = self._eval_model.forward(data) if self._cuda: output = to_device(output, 'cpu') if tensor_input: return output else: output = default_decollate(output) return {i: d for i, d in zip(data_id, output)}
[docs] def _init_collect(self) -> None: """ Overview: BC policy uses offline dataset so it does not need to collect data. However, sometimes we need to use the \ trained BC policy to collect data for other purposes. """ self._unroll_len = self._cfg.collect.unroll_len if self._cfg.continuous: self._collect_model = model_wrap( self._model, wrapper_name='action_noise', noise_type='gauss', noise_kwargs={ 'mu': 0.0, 'sigma': self._cfg.collect.noise_sigma.start }, noise_range=self._cfg.collect.noise_range ) else: self._collect_model = model_wrap(self._model, wrapper_name='eps_greedy_sample') self._collect_model.reset()
def _forward_collect(self, data: Dict[int, Any], **kwargs) -> Dict[int, Any]: data_id = list(data.keys()) data = default_collate(list(data.values())) if self._cuda: data = to_device(data, self._device) self._collect_model.eval() with torch.no_grad(): if self._cfg.continuous: # output = self._collect_model.forward(data) output = self._collect_model.forward(data, **kwargs) else: output = self._collect_model.forward(data, **kwargs) if self._cuda: output = to_device(output, 'cpu') output = default_decollate(output) return {i: d for i, d in zip(data_id, output)} def _process_transition(self, obs: Any, policy_output: dict, timestep: namedtuple) -> dict: transition = { 'obs': obs, 'next_obs': timestep.obs, 'action': policy_output['action'], 'reward': timestep.reward, 'done': timestep.done, } return EasyDict(transition) def _get_train_sample(self, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]: data = get_nstep_return_data(data, 1, 1) return get_train_sample(data, self._unroll_len)