`ding.policy.pdqn`¶

`ding.policy.pdqn` ¶

`PDQNPolicy` ¶

Bases: Policy
Overview
Policy class of PDQN algorithm, which extends the DQN algorithm on discrete-continuous hybrid action spaces. Paper link: https://arxiv.org/abs/1810.06394.
Config
`default_model()` ¶

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 PDQN, its registered name is pdqn and the import_names is ding.model.template.pdqn.
Full Source Code

../ding/policy/pdqn.py
from typing import List, Dict, Any, Tuplefrom collections import namedtupleimport copyimport torchfrom ding.torch_utils import Adam, to_devicefrom ding.rl_utils import q_nstep_td_data, q_nstep_td_error, get_nstep_return_data, get_train_samplefrom ding.model import model_wrapfrom ding.utils import POLICY_REGISTRYfrom ding.utils.data import default_collate, default_decollatefrom .base_policy import Policyfrom .common_utils import default_preprocess_learn@POLICY_REGISTRY.register('pdqn')class PDQNPolicy(Policy):    """    Overview:        Policy class of PDQN algorithm, which extends the DQN algorithm on discrete-continuous hybrid action spaces.        Paper link: https://arxiv.org/abs/1810.06394.    Config:        == ==================== ======== ============== ======================================== =======================        ID Symbol               Type     Default Value  Description                              Other(Shape)        == ==================== ======== ============== ======================================== =======================        1  ``type``             str      pdqn           | 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  | This value is always                                                        | or off-policy                          | False for PDQN        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.batch_``   int      64             | The number of samples of an iteration           | ``size``           | ``_gpu``        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 | ``collect.noise``  float    0.1            | add noise to continuous args           | ``_sigma``                                 | during collection        17 | ``other.eps.type`` str      exp            | exploration rate decay type            | Support ['exp',                                                                                                 | 'linear'].        18 | ``other.eps.``     float    0.95           | start value of exploration rate        | [0,1]           | ``start``        19 | ``other.eps.``     float    0.05           | end value of exploration rate          | [0,1]           | ``end``        20 | ``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(        # (str) RL policy register name (refer to function "POLICY_REGISTRY").        type='pdqn',        # (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,        # (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_theta=0.005,            # (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=8,            # (int) Split episodes or trajectories into pieces with length `unroll_len`.            unroll_len=1,            # (float) It is a must to add noise during collection. So here omits noise and only set ``noise_sigma``.            noise_sigma=0.1,        ),        eval=dict(),  # for compatibility        # 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,            ),        ),    )    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 PDQN, its registered name is ``pdqn`` and the import_names is \            ``ding.model.template.pdqn``.        """        return 'pdqn', ['ding.model.template.pdqn']    def _init_learn(self) -> None:        """        Overview:            Initialize the learn mode of policy, including related attributes and modules. For PDQN, it mainly \            contains two optimizers, algorithm-specific arguments such as nstep 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        self._priority_IS_weight = self._cfg.priority_IS_weight        # Optimizer        self._dis_optimizer = Adam(            list(self._model.dis_head.parameters()) + list(self._model.cont_encoder.parameters()),            # this is very important to put cont_encoder.parameters in here.            lr=self._cfg.learn.learning_rate_dis        )        self._cont_optimizer = Adam(list(self._model.cont_head.parameters()), lr=self._cfg.learn.learning_rate_cont)        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='momentum',            update_kwargs={'theta': self._cfg.learn.target_theta}        )        self._learn_model = model_wrap(self._model, wrapper_name='hybrid_argmax_sample')        self._learn_model.reset()        self._target_model.reset()        self.cont_train_cnt = 0        self.disc_train_cnt = 0        self.train_cnt = 0    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, q value, target_q_value, 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 PDQN, 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 PDQNPolicy: ``ding.policy.tests.test_pdqn``.        """        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)        self.train_cnt += 1        # ================================        # Continuous args network forward        # ================================        if self.train_cnt == 1 or self.train_cnt % self._cfg.learn.update_circle in range(5, 10):            dis_loss = torch.Tensor([0])            td_error_per_sample = torch.Tensor([0])            target_q_value = torch.Tensor([0])            action_args = self._learn_model.forward(data['obs'], mode='compute_continuous')['action_args']            # Current q value (main model) for cont loss            discrete_inputs = {'state': data['obs'], 'action_args': action_args}            # with torch.no_grad():            q_pi_action_value = self._learn_model.forward(discrete_inputs, mode='compute_discrete')['logit']            cont_loss = -q_pi_action_value.sum(dim=-1).mean()            # ================================            # Continuous args network update            # ================================            self._cont_optimizer.zero_grad()            cont_loss.backward()            self._cont_optimizer.step()        # ====================        # Q-learning forward        # ====================        if self.train_cnt == 1 or self.train_cnt % self._cfg.learn.update_circle in range(0, 5):            cont_loss = torch.Tensor([0])            q_pi_action_value = torch.Tensor([0])            self._learn_model.train()            self._target_model.train()            # Current q value (main model)            discrete_inputs = {'state': data['obs'], 'action_args': data['action']['action_args']}            q_data_action_args_value = self._learn_model.forward(discrete_inputs, mode='compute_discrete')['logit']            # Target q value            with torch.no_grad():                next_action_args = self._learn_model.forward(data['next_obs'], mode='compute_continuous')['action_args']                next_action_args_cp = next_action_args.clone().detach()                next_discrete_inputs = {'state': data['next_obs'], 'action_args': next_action_args_cp}                target_q_value = self._target_model.forward(next_discrete_inputs, mode='compute_discrete')['logit']                # Max q value action (main model)                target_q_discrete_action = self._learn_model.forward(                    next_discrete_inputs, mode='compute_discrete'                )['action']['action_type']            data_n = q_nstep_td_data(                q_data_action_args_value, target_q_value, data['action']['action_type'], target_q_discrete_action,                data['reward'], data['done'], data['weight']            )            value_gamma = data.get('value_gamma')            dis_loss, td_error_per_sample = q_nstep_td_error(                data_n, self._gamma, nstep=self._nstep, value_gamma=value_gamma            )            # ====================            # Q-learning update            # ====================            self._dis_optimizer.zero_grad()            dis_loss.backward()            self._dis_optimizer.step()            # =============            # after update            # =============            self._target_model.update(self._learn_model.state_dict())        return {            'cur_lr': self._dis_optimizer.defaults['lr'],            'q_loss': dis_loss.item(),            'total_loss': cont_loss.item() + dis_loss.item(),            'continuous_loss': cont_loss.item(),            'q_value': q_pi_action_value.mean().item(),            'priority': td_error_per_sample.abs().tolist(),            'reward': data['reward'].mean().item(),            'target_q_value': target_q_value.mean().item(),        }    def _state_dict_learn(self) -> Dict[str, Any]:        """        Overview:            Return the state_dict of learn mode, usually including model, target model, discrete part optimizer, and \            continuous part 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(),            'dis_optimizer': self._dis_optimizer.state_dict(),            'cont_optimizer': self._cont_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._dis_optimizer.load_state_dict(state_dict['dis_optimizer'])        self._cont_optimizer.load_state_dict(state_dict['cont_optimizer'])    def _init_collect(self) -> None:        """        Overview:            Initialize the collect mode of policy, including related attributes and modules. For PDQN, it contains the \            collect_model to balance the exploration and exploitation with epsilon-greedy sample mechanism and \            continuous action mechanism, besides, other algorithm-specific arguments such as unroll_len and nstep are \            also initialized here.            This method will be called in ``__init__`` method if ``collect`` field is in ``enable_field``.        .. note::            If you want to set some spacial member variables in ``_init_collect`` method, you'd better name them \            with prefix ``_collect_`` to avoid conflict with other modes, such as ``self._collect_attr1``.        .. tip::            Some variables need to initialize independently in different modes, such as gamma and nstep in PDQN. This \            design is for the convenience of parallel execution of different policy modes.        """        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='action_noise',            noise_type='gauss',            noise_kwargs={                'mu': 0.0,                'sigma': self._cfg.collect.noise_sigma            },            noise_range=None        )        self._collect_model = model_wrap(self._collect_model, wrapper_name='hybrid_eps_greedy_multinomial_sample')        self._collect_model.reset()    def _forward_collect(self, data: Dict[int, Any], eps: float) -> Dict[int, Any]:        """        Overview:            Policy forward function of collect mode (collecting training data by interacting with envs). Forward means \            that the policy gets some necessary data (mainly observation) from the envs and then returns the output \            data, such as the action to interact with the envs. Besides, this policy also needs ``eps`` argument for \            exploration, i.e., classic epsilon-greedy exploration strategy.        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.            - eps (:obj:`float`): The epsilon value for exploration.        Returns:            - output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action and \                other necessary data for learn mode defined in ``self._process_transition`` method. 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.        .. note::            For more detailed examples, please refer to our unittest for PDQNPolicy: ``ding.policy.tests.test_pdqn``.        """        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():            action_args = self._collect_model.forward(data, 'compute_continuous', eps=eps)['action_args']            inputs = {'state': data, 'action_args': action_args.clone().detach()}            output = self._collect_model.forward(inputs, 'compute_discrete', 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, transitions: 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. In PDQN, a train sample is a processed transition. \            This method is usually used in collectors to execute necessary \            RL data preprocessing before training, which can help learner amortize revelant time consumption. \            In addition, you can also implement this method as an identity function and do the data processing \            in ``self._forward_learn`` method.        Arguments:            - transitions (: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:`List[Dict[str, Any]]`): The processed train samples, each element is the similar format \                as input transitions, but may contain more data for training, such as nstep reward and target obs.        """        transitions = get_nstep_return_data(transitions, self._nstep, gamma=self._gamma)        return get_train_sample(transitions, self._unroll_len)    def _process_transition(self, obs: torch.Tensor, policy_output: Dict[str, torch.Tensor],                            timestep: namedtuple) -> Dict[str, torch.Tensor]:        """        Overview:            Process and pack one timestep transition data into a dict, which can be directly used for training and \            saved in replay buffer. For PDQN, it contains obs, next_obs, action, reward, done and logit.        Arguments:            - obs (:obj:`torch.Tensor`): The env observation of current timestep, such as stacked 2D image in Atari.            - policy_output (:obj:`Dict[str, torch.Tensor]`): The output of the policy network with the observation \                as input. For PDQN, it contains the hybrid action and the logit (discrete part q_value) of the action.            - timestep (:obj:`namedtuple`): The execution result namedtuple returned by the environment step method, \                except all the elements have been transformed into tensor data. Usually, it contains the next obs, \                reward, done, info, etc.        Returns:            - transition (:obj:`Dict[str, torch.Tensor]`): The processed transition data of the current timestep.        """        transition = {            'obs': obs,            'next_obs': timestep.obs,            'action': policy_output['action'],            'logit': policy_output['logit'],            'reward': timestep.reward,            'done': timestep.done,        }        return transition    def _init_eval(self) -> None:        """        Overview:            Initialize the eval mode of policy, including related attributes and modules. For PDQN, it contains the \            eval model to greedily select action with argmax q_value mechanism.            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``.        """        self._eval_model = model_wrap(self._model, wrapper_name='hybrid_argmax_sample')        self._eval_model.reset()    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.        .. note::            For more detailed examples, please refer to our unittest for PDQNPolicy: ``ding.policy.tests.test_pdqn``.        """        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():            action_args = self._eval_model.forward(data, mode='compute_continuous')['action_args']            inputs = {'state': data, 'action_args': action_args.clone().detach()}            output = self._eval_model.forward(inputs, mode='compute_discrete')        if self._cuda:            output = to_device(output, 'cpu')        output = default_decollate(output)        return {i: d for i, d in zip(data_id, output)}    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_loss', 'continuous_loss', 'q_value', 'reward', 'target_q_value']
ding.policy.pdqn¶

ding.policy.pdqn ¶

PDQNPolicy ¶

default_model() ¶

Full Source Code

`ding.policy.pdqn`¶

`ding.policy.pdqn` ¶

`PDQNPolicy` ¶

`default_model()` ¶
