from typing import List, Dict, Any, Tuple from collections import namedtuple import copy import torch from ding.torch_utils import Adam, to_device from ding.rl_utils import q_nstep_td_data, q_nstep_td_error, get_nstep_return_data, get_train_sample from ding.model import model_wrap from ding.utils import POLICY_REGISTRY from ding.utils.data import default_collate, default_decollate from .base_policy import Policy from .common_utils import default_preprocess_learn @POLICY_REGISTRY.register('dqn') class DQNPolicy(Policy): r""" Overview: Policy class of DQN algorithm, extended by Double DQN/Dueling DQN/PER/multi-step TD. Config: == ==================== ======== ============== ======================================== ======================= ID Symbol Type Default Value Description Other(Shape) == ==================== ======== ============== ======================================== ======================= 1 ``type`` str dqn | 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 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 9 | ``learn.multi`` bool False | whether to use multi gpu during | ``_gpu`` 10 | ``learn.batch_`` int 64 | 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 100 | 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 [8, 128] | 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 0.95 | start value of exploration rate | [0,1] | ``start`` 18 | ``other.eps.`` float 0.1 | end value of exploration rate | [0,1] | ``end`` 19 | ``other.eps.`` int 10000 | decay length of exploration | greater than 0. set | ``decay`` | decay=10000 means | the exploration rate | decay from start | value to end value | during decay length. == ==================== ======== ============== ======================================== ======================= """ config = dict( type='dqn', # (bool) Whether use cuda in policy cuda=False, # (bool) Whether learning policy is the same as collecting data policy(on-policy) on_policy=False, # (bool) Whether enable priority experience sample priority=False, # (bool) Whether 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, # (int) The number of step for calculating target q_value nstep=1, learn=dict( # (bool) Whether to use multi gpu multi_gpu=False, # 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, # (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_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 _init_learn(self) -> None: """ Overview: Learn mode init method. Called by ``self.__init__``, initialize the optimizer, algorithm arguments, main \ and target model. """ self._priority = self._cfg.priority self._priority_IS_weight = self._cfg.priority_IS_weight # Optimizer self._optimizer = Adam(self._model.parameters(), lr=self._cfg.learn.learning_rate) self._gamma = self._cfg.discount_factor self._nstep = self._cfg.nstep # 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: Dict[str, Any]) -> Dict[str, Any]: """ Overview: Forward computation graph of learn mode(updating policy). Arguments: - data (:obj:`Dict[str, Any]`): Dict type data, a batch of data for training, values are torch.Tensor or \ np.ndarray or dict/list combinations. Returns: - info_dict (:obj:`Dict[str, Any]`): Dict type data, a info dict indicated training result, which will be \ recorded in text log and tensorboard, values are python scalar or a list of scalars. ArgumentsKeys: - necessary: ``obs``, ``action``, ``reward``, ``next_obs``, ``done`` - optional: ``value_gamma``, ``IS`` ReturnsKeys: - necessary: ``cur_lr``, ``total_loss``, ``priority`` - optional: ``action_distribution`` """ 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 = self._target_model.forward(data['next_obs'])['logit'] # Max q value action (main model) target_q_action = self._learn_model.forward(data['next_obs'])['action'] data_n = q_nstep_td_data( q_value, target_q_value, data['action'], target_q_action, data['reward'], data['done'], data['weight'] ) value_gamma = data.get('value_gamma') loss, td_error_per_sample = q_nstep_td_error(data_n, self._gamma, nstep=self._nstep, value_gamma=value_gamma) # ==================== # Q-learning update # ==================== self._optimizer.zero_grad() loss.backward() if self._cfg.learn.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(), '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 _monitor_vars_learn(self) -> List[str]: return ['cur_lr', 'total_loss', 'q_value'] 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(), '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']) def _init_collect(self) -> None: """ Overview: Collect mode init method. Called by ``self.__init__``, initialize algorithm arguments and collect_model, \ enable the eps_greedy_sample for exploration. """ self._unroll_len = self._cfg.collect.unroll_len self._gamma = self._cfg.discount_factor # necessary for parallel self._nstep = self._cfg.nstep # necessary for parallel self._collect_model = model_wrap(self._model, wrapper_name='eps_greedy_sample') self._collect_model.reset() def _forward_collect(self, data: Dict[int, Any], eps: float) -> Dict[int, Any]: """ Overview: Forward computation graph of collect mode(collect training data), with eps_greedy for exploration. Arguments: - data (:obj:`Dict[str, Any]`): Dict type data, stacked env data for predicting policy_output(action), \ values are torch.Tensor or np.ndarray or dict/list combinations, keys are env_id indicated by integer. - eps (:obj:`float`): epsilon value for exploration, which is decayed by collected env step. Returns: - output (:obj:`Dict[int, Any]`): The dict of predicting policy_output(action) for the interaction with \ env and the constructing of transition. ArgumentsKeys: - necessary: ``obs`` ReturnsKeys - necessary: ``logit``, ``action`` """ 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(): output = self._collect_model.forward(data, eps=eps) if self._cuda: output = to_device(output, 'cpu') output = default_decollate(output) return {i: d for i, d in zip(data_id, output)} def _get_train_sample(self, data: 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 continuous transitions(DRQN). Arguments: - data (: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:`dict`): The list of training samples. .. 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. """ data = get_nstep_return_data(data, self._nstep, gamma=self._gamma) return get_train_sample(data, self._unroll_len) def _process_transition(self, obs: Any, policy_output: Dict[str, Any], timestep: namedtuple) -> Dict[str, Any]: """ Overview: Generate a transition(e.g.: ) for this algorithm training. Arguments: - obs (:obj:`Any`): Env observation. - policy_output (:obj:`Dict[str, Any]`): The output of policy collect mode(``self._forward_collect``),\ including at least ``action``. - timestep (:obj:`namedtuple`): The output after env step(execute policy output action), including at \ least ``obs``, ``reward``, ``done``, (here obs indicates obs after env step). Returns: - transition (:obj:`dict`): Dict type transition data. """ transition = { 'obs': obs, 'next_obs': timestep.obs, 'action': policy_output['action'], 'reward': timestep.reward, 'done': timestep.done, } return transition def _init_eval(self) -> None: r""" Overview: Evaluate mode init method. Called by ``self.__init__``, initialize eval_model. """ self._eval_model = model_wrap(self._model, wrapper_name='argmax_sample') self._eval_model.reset() def _forward_eval(self, data: Dict[int, Any]) -> Dict[int, Any]: """ Overview: Forward computation graph of eval mode(evaluate policy performance), at most cases, it is similar to \ ``self._forward_collect``. Arguments: - data (:obj:`Dict[str, Any]`): Dict type data, stacked env data for predicting policy_output(action), \ values are torch.Tensor or np.ndarray or dict/list combinations, keys are env_id indicated by integer. Returns: - output (:obj:`Dict[int, Any]`): The dict of predicting action for the interaction with env. ArgumentsKeys: - necessary: ``obs`` ReturnsKeys - necessary: ``action`` """ 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') output = default_decollate(output) return {i: d for i, d in zip(data_id, output)} def default_model(self) -> Tuple[str, List[str]]: """ Overview: Return this algorithm default model setting for demonstration. Returns: - model_info (:obj:`Tuple[str, List[str]]`): model name and mode 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 DQN, ``ding.model.template.q_learning.DQN`` """ return 'dqn', ['ding.model.template.q_learning']