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Source code for ding.policy.mdqn

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

from ding.torch_utils import Adam, to_device
from ding.rl_utils import m_q_1step_td_data, m_q_1step_td_error
from ding.model import model_wrap
from ding.utils import POLICY_REGISTRY

from .dqn import DQNPolicy
from .common_utils import default_preprocess_learn


[docs]@POLICY_REGISTRY.register('mdqn') class MDQNPolicy(DQNPolicy): """ Overview: Policy class of Munchausen DQN algorithm, extended by auxiliary objectives. Paper link: https://arxiv.org/abs/2007.14430. Config: == ==================== ======== ============== ======================================== ======================= ID Symbol Type Default Value Description Other(Shape) == ==================== ======== ============== ======================================== ======================= 1 ``type`` str mdqn | 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 False | Whether use priority(PER) | Priority sample, | update priority 5 | ``priority_IS`` bool False | Whether use Importance Sampling Weight | ``_weight`` | to correct biased update. If True, | priority must be True. 6 | ``discount_`` float 0.97, | Reward's future discount factor, aka. | May be 1 when sparse | ``factor`` [0.95, 0.999] | gamma | reward env 7 ``nstep`` int 1, | N-step reward discount sum for target [3, 5] | q_value estimation 8 | ``learn.update`` int 1 | 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 | ``_gpu`` 10 | ``learn.batch_`` int 32 | The number of samples of an iteration | ``size`` 11 | ``learn.learning`` float 0.001 | Gradient step length of an iteration. | ``_rate`` 12 | ``learn.target_`` int 2000 | Frequence of target network update. | Hard(assign) update | ``update_freq`` 13 | ``learn.ignore_`` bool False | Whether ignore done for target value | Enable it for some | ``done`` | calculation. | fake termination env 14 ``collect.n_sample`` int 4 | The number of training samples of a | It varies from | call of collector. | different envs 15 | ``collect.unroll`` int 1 | unroll length of an iteration | In RNN, unroll_len>1 | ``_len`` 16 | ``other.eps.type`` str exp | exploration rate decay type | Support ['exp', | 'linear']. 17 | ``other.eps.`` float 0.01 | start value of exploration rate | [0,1] | ``start`` 18 | ``other.eps.`` float 0.001 | end value of exploration rate | [0,1] | ``end`` 19 | ``other.eps.`` int 250000 | decay length of exploration | greater than 0. set | ``decay`` | decay=250000 means | the exploration rate | decay from start | value to end value | during decay length. 20 | ``entropy_tau`` float 0.003 | the ration of entropy in TD loss 21 | ``alpha`` float 0.9 | the ration of Munchausen term to the | TD loss == ==================== ======== ============== ======================================== ======================= """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='mdqn', # (bool) Whether to use cuda in policy. cuda=False, # (bool) Whether learning policy is the same as collecting data policy(on-policy). on_policy=False, # (bool) Whether to enable priority experience sample. priority=False, # (bool) Whether to use Importance Sampling Weight to correct biased update. If True, priority must be True. priority_IS_weight=False, # (float) Discount factor(gamma) for returns. discount_factor=0.97, # (float) Entropy factor (tau) for Munchausen DQN. entropy_tau=0.03, # (float) Discount factor (alpha) for Munchausen term. m_alpha=0.9, # (int) The number of step for calculating target q_value. nstep=1, # learn_mode config learn=dict( # (int) 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, # (int) How many samples in a training batch batch_size=64, # (float) The step size of gradient descent learning_rate=0.001, # (int) Frequence of target network update. target_update_freq=100, # (bool) Whether ignore done(usually for max step termination env). # Note: Gym wraps the MuJoCo envs by default with TimeLimit environment wrappers. # These limit HalfCheetah, and several other MuJoCo envs, to max length of 1000. # However, interaction with HalfCheetah always gets done with done is False, # Since we inplace done==True with done==False to keep # TD-error accurate computation(``gamma * (1 - done) * next_v + reward``), # when the episode step is greater than max episode step. ignore_done=False, ), # collect_mode config collect=dict( # (int) How many training samples collected in one collection procedure. # Only one of [n_sample, n_episode] shoule be set. n_sample=4, # (int) Split episodes or trajectories into pieces with length `unroll_len`. unroll_len=1, ), eval=dict(), # for compability # other config other=dict( # Epsilon greedy with decay. eps=dict( # (str) Decay type. Support ['exp', 'linear']. type='exp', # (float) Epsilon start value. start=0.95, # (float) Epsilon end value. end=0.1, # (int) Decay length(env step). decay=10000, ), replay_buffer=dict( # (int) Maximum size of replay buffer. Usually, larger buffer size is better. replay_buffer_size=10000, ), ), )
[docs] def _init_learn(self) -> None: """ Overview: Initialize the learn mode of policy, including related attributes and modules. For MDQN, it contains \ optimizer, algorithm-specific arguments such as entropy_tau, m_alpha and nstep, 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 self._priority_IS_weight = self._cfg.priority_IS_weight # Optimizer # set eps in order to consistent with the original paper implementation self._optimizer = Adam(self._model.parameters(), lr=self._cfg.learn.learning_rate, eps=0.0003125) self._gamma = self._cfg.discount_factor self._nstep = self._cfg.nstep self._entropy_tau = self._cfg.entropy_tau self._m_alpha = self._cfg.m_alpha # use model_wrapper for specialized demands of different modes self._target_model = copy.deepcopy(self._model) if 'target_update_freq' in self._cfg.learn: self._target_model = model_wrap( self._target_model, wrapper_name='target', update_type='assign', update_kwargs={'freq': self._cfg.learn.target_update_freq} ) elif 'target_theta' in self._cfg.learn: self._target_model = model_wrap( self._target_model, wrapper_name='target', update_type='momentum', update_kwargs={'theta': self._cfg.learn.target_theta} ) else: raise RuntimeError("DQN needs target network, please either indicate target_update_freq or target_theta") self._learn_model = model_wrap(self._model, wrapper_name='argmax_sample') self._learn_model.reset() self._target_model.reset()
[docs] def _forward_learn(self, data: 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, action_gap, clip_frac, 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 MDQN, 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 MDQNPolicy: ``ding.policy.tests.test_mdqn``. """ data = default_preprocess_learn( data, use_priority=self._priority, use_priority_IS_weight=self._cfg.priority_IS_weight, 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) q_value = self._learn_model.forward(data['obs'])['logit'] # Target q value with torch.no_grad(): target_q_value_current = self._target_model.forward(data['obs'])['logit'] target_q_value = self._target_model.forward(data['next_obs'])['logit'] data_m = m_q_1step_td_data( q_value, target_q_value_current, target_q_value, data['action'], data['reward'].squeeze(0), data['done'], data['weight'] ) loss, td_error_per_sample, action_gap, clipfrac = m_q_1step_td_error( data_m, self._gamma, self._entropy_tau, self._m_alpha ) # ==================== # 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(), 'q_value': q_value.mean().item(), 'target_q_value': target_q_value.mean().item(), 'priority': td_error_per_sample.abs().tolist(), 'action_gap': action_gap.item(), 'clip_frac': clipfrac.mean().item(), }
[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', 'q_value', 'action_gap', 'clip_frac']