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Source code for ding.policy.iqn

from typing import List, Dict, Any, Tuple, Union
import copy
import torch

from ding.torch_utils import Adam, to_device
from ding.rl_utils import iqn_nstep_td_data, iqn_nstep_td_error, get_train_sample, get_nstep_return_data
from ding.model import model_wrap
from ding.utils import POLICY_REGISTRY
from ding.utils.data import default_collate, default_decollate
from .dqn import DQNPolicy
from .common_utils import default_preprocess_learn


[docs]@POLICY_REGISTRY.register('iqn') class IQNPolicy(DQNPolicy): """ Overview: Policy class of IQN algorithm. Paper link: https://arxiv.org/pdf/1806.06923.pdf. \ Distrbutional RL is a new direction of RL, which is more stable than the traditional RL algorithm. \ The core idea of distributional RL is to estimate the distribution of action value instead of the \ expectation. The difference between IQN and DQN is that IQN uses quantile regression to estimate the \ quantile value of the action distribution, while DQN uses the expectation of the action distribution. \ Config: == ==================== ======== ============== ======================================== ======================= ID Symbol Type Default Value Description Other(Shape) == ==================== ======== ============== ======================================== ======================= 1 ``type`` str qrdqn | RL policy register name, refer to | this arg is optional, | registry ``POLICY_REGISTRY`` | a placeholder 2 ``cuda`` bool False | Whether to use cuda for network | this arg can be diff- | erent from modes 3 ``on_policy`` bool False | Whether the RL algorithm is on-policy | or off-policy 4 ``priority`` bool True | Whether use priority(PER) | priority sample, | update priority 6 | ``other.eps`` float 0.05 | Start value for epsilon decay. It's | ``.start`` | small because rainbow use noisy net. 7 | ``other.eps`` float 0.05 | End value for epsilon decay. | ``.end`` 8 | ``discount_`` float 0.97, | Reward's future discount factor, aka. | may be 1 when sparse | ``factor`` [0.95, 0.999] | gamma | reward env 9 ``nstep`` int 3, | N-step reward discount sum for target [3, 5] | q_value estimation 10 | ``learn.update`` int 3 | How many updates(iterations) to train | this args can be vary | ``per_collect`` | after collector's one collection. Only | from envs. Bigger val | valid in serial training | means more off-policy 11 ``learn.kappa`` float / | Threshold of Huber loss == ==================== ======== ============== ======================================== ======================= """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='iqn', # (bool) Whether to use cuda for network. cuda=False, # (bool) Whether the RL algorithm is on-policy or off-policy. on_policy=False, # (bool) Whether use priority(priority sample, IS weight, update priority) priority=False, # (float) Reward's future discount factor, aka. gamma. discount_factor=0.97, # (int) N-step reward for target q_value estimation nstep=1, learn=dict( # How many updates(iterations) to train after collector's one collection. # Bigger "update_per_collect" means bigger off-policy. # collect data -> update policy-> collect data -> ... update_per_collect=3, batch_size=64, learning_rate=0.001, # ============================================================== # The following configs are algorithm-specific # ============================================================== # (int) Frequence of target network update. target_update_freq=100, # (float) Threshold of Huber loss. In the IQN paper, this is denoted by kappa. Default to 1.0. kappa=1.0, # (bool) Whether ignore done(usually for max step termination env) ignore_done=False, ), # collect_mode config collect=dict( # (int) Only one of [n_sample, n_step, n_episode] shoule be set # n_sample=8, # (int) Cut trajectories into pieces with length "unroll_len". unroll_len=1, ), eval=dict(), # other config other=dict( # Epsilon greedy with decay. eps=dict( # (str) Decay type. Support ['exp', 'linear']. type='exp', start=0.95, end=0.1, # (int) Decay length(env step) decay=10000, ), replay_buffer=dict(replay_buffer_size=10000, ) ), ) 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 IQN, its registered name is ``iqn`` and the import_names is \ ``ding.model.template.q_learning``. """ return 'iqn', ['ding.model.template.q_learning'] def _init_learn(self) -> None: """ Overview: Initialize the learn mode of policy, including related attributes and modules. For IQN, it mainly contains \ optimizer, algorithm-specific arguments such as nstep, kappa and gamma, main and target model. 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``. """ self._priority = self._cfg.priority # Optimizer self._optimizer = Adam(self._model.parameters(), lr=self._cfg.learn.learning_rate) self._gamma = self._cfg.discount_factor self._nstep = self._cfg.nstep self._kappa = self._cfg.learn.kappa # use model_wrapper for specialized demands of different modes self._target_model = copy.deepcopy(self._model) self._target_model = model_wrap( self._target_model, wrapper_name='target', update_type='assign', update_kwargs={'freq': self._cfg.learn.target_update_freq} ) self._learn_model = model_wrap(self._model, wrapper_name='argmax_sample') self._learn_model.reset() self._target_model.reset() def _forward_learn(self, data: List[Dict[int, 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, priority. 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 IQN, each element in list is a dict containing at least the following keys: ``obs``, ``action``, \ ``reward``, ``next_obs``, ``done``. Sometimes, it also contains other keys such as ``weight`` \ and ``value_gamma``. 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. .. note:: For more detailed examples, please refer to our unittest for IQNPolicy: ``ding.policy.tests.test_iqn``. """ data = default_preprocess_learn( data, use_priority=self._priority, ignore_done=self._cfg.learn.ignore_done, use_nstep=True ) if self._cuda: data = to_device(data, self._device) # ==================== # Q-learning forward # ==================== self._learn_model.train() self._target_model.train() # Current q value (main model) ret = self._learn_model.forward(data['obs']) q_value = ret['q'] replay_quantiles = ret['quantiles'] # Target q value with torch.no_grad(): target_q_value = self._target_model.forward(data['next_obs'])['q'] # Max q value action (main model) target_q_action = self._learn_model.forward(data['next_obs'])['action'] data_n = iqn_nstep_td_data( q_value, target_q_value, data['action'], target_q_action, data['reward'], data['done'], replay_quantiles, data['weight'] ) value_gamma = data.get('value_gamma') loss, td_error_per_sample = iqn_nstep_td_error( data_n, self._gamma, nstep=self._nstep, kappa=self._kappa, value_gamma=value_gamma ) # ==================== # Q-learning update # ==================== self._optimizer.zero_grad() loss.backward() if self._cfg.multi_gpu: self.sync_gradients(self._learn_model) self._optimizer.step() # ============= # after update # ============= self._target_model.update(self._learn_model.state_dict()) return { 'cur_lr': self._optimizer.defaults['lr'], 'total_loss': loss.item(), 'priority': td_error_per_sample.abs().tolist(), # Only discrete action satisfying len(data['action'])==1 can return this and draw histogram on tensorboard. # '[histogram]action_distribution': data['action'], } def _state_dict_learn(self) -> Dict[str, Any]: """ Overview: Return the state_dict of learn mode, usually including model, target_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(), 'target_model': self._target_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._target_model.load_state_dict(state_dict['target_model']) self._optimizer.load_state_dict(state_dict['optimizer'])