`ding.policy.ddpg`¶

`ding.policy.ddpg` ¶

`DDPGPolicy` ¶

Bases: Policy
Overview
Policy class of DDPG algorithm. Paper link: https://arxiv.org/abs/1509.02971.
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.
Full Source Code

../ding/policy/ddpg.py
from typing import List, Dict, Any, Tuple, Unionfrom collections import namedtupleimport torchimport copyfrom ding.torch_utils import Adam, to_devicefrom ding.rl_utils import v_1step_td_data, v_1step_td_error, 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('ddpg')class DDPGPolicy(Policy):    """    Overview:        Policy class of DDPG algorithm. Paper link: https://arxiv.org/abs/1509.02971.    Config:        == ====================  ========    =============  =================================   =======================        ID Symbol                Type        Default Value  Description                         Other(Shape)        == ====================  ========    =============  =================================   =======================        1  | ``type``            str         ddpg           | RL policy register name, refer    | this arg is optional,           |                                                | to registry ``POLICY_REGISTRY``   | a placeholder        2  | ``cuda``            bool        False          | Whether to use cuda for network   |        3  | ``random_``         int         25000          | Number of randomly collected      | Default to 25000 for           | ``collect_size``                               | training samples in replay        | DDPG/TD3, 10000 for           |                                                | buffer when training starts.      | sac.        4  | ``model.twin_``     bool        False          | Whether to use two critic         | Default False for           | ``critic``                                     | networks or only one.             | DDPG, Clipped Double           |                                                |                                   | Q-learning method in           |                                                |                                   | TD3 paper.        5  | ``learn.learning``  float       1e-3           | Learning rate for actor           |           | ``_rate_actor``                                | network(aka. policy).             |        6  | ``learn.learning``  float       1e-3           | Learning rates for critic         |           | ``_rate_critic``                               | network (aka. Q-network).         |        7  | ``learn.actor_``    int         2              | When critic network updates       | Default 1 for DDPG,           | ``update_freq``                                | once, how many times will actor   | 2 for TD3. Delayed           |                                                | network update.                   | Policy Updates method           |                                                |                                   | in TD3 paper.        8  | ``learn.noise``     bool        False          | Whether to add noise on target    | Default False for           |                                                | network's action.                 | DDPG, True for TD3.           |                                                |                                   | Target Policy Smoo-           |                                                |                                   | thing Regularization           |                                                |                                   | in TD3 paper.        9  | ``learn.-``         bool        False          | Determine whether to ignore       | Use ignore_done only           | ``ignore_done``                                | done flag.                        | in halfcheetah env.        10 | ``learn.-``         float       0.005          | Used for soft update of the       | aka. Interpolation           | ``target_theta``                               | target network.                   | factor in polyak aver-           |                                                |                                   | aging for target           |                                                |                                   | networks.        11 | ``collect.-``       float       0.1            | Used for add noise during co-     | Sample noise from dis-           | ``noise_sigma``                                | llection, through controlling     | tribution, Ornstein-           |                                                | the sigma of distribution         | Uhlenbeck process in           |                                                |                                   | DDPG paper, Gaussian           |                                                |                                   | process in ours.        == ====================  ========    =============  =================================   =======================    """    config = dict(        # (str) RL policy register name (refer to function "POLICY_REGISTRY").        type='ddpg',        # (bool) Whether to use cuda in policy.        cuda=False,        # (bool) Whether learning policy is the same as collecting data policy(on-policy). Default False in DDPG.        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,        # (int) Number of training samples(randomly collected) in replay buffer when training starts.        # Default 25000 in DDPG/TD3.        random_collect_size=25000,        # (bool) Whether to need policy data in process transition.        transition_with_policy_data=False,        # (str) Action space type, including ['continuous', 'hybrid'].        action_space='continuous',        # (bool) Whether use batch normalization for reward.        reward_batch_norm=False,        # (bool) Whether to enable multi-agent training setting.        multi_agent=False,        # 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=1,            # (int) Minibatch size for gradient descent.            batch_size=256,            # (float) Learning rates for actor network(aka. policy).            learning_rate_actor=1e-3,            # (float) Learning rates for critic network(aka. Q-network).            learning_rate_critic=1e-3,            # (bool) Whether ignore done(usually for max step termination env. e.g. pendulum)            # 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 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,            # (float) target_theta: Used for soft update of the target network,            # aka. Interpolation factor in polyak averaging for target networks.            # Default to 0.005.            target_theta=0.005,            # (float) discount factor for the discounted sum of rewards, aka. gamma.            discount_factor=0.99,            # (int) When critic network updates once, how many times will actor network update.            # Delayed Policy Updates in original TD3 paper(https://arxiv.org/pdf/1802.09477.pdf).            # Default 1 for DDPG, 2 for TD3.            actor_update_freq=1,            # (bool) Whether to add noise on target network's action.            # Target Policy Smoothing Regularization in original TD3 paper(https://arxiv.org/pdf/1802.09477.pdf).            # Default True for TD3, False for DDPG.            noise=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=1,            # (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 compability        other=dict(            replay_buffer=dict(                # (int) Maximum size of replay buffer. Usually, larger buffer size is better.                replay_buffer_size=100000,            ),        ),    )    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.        """        if self._cfg.multi_agent:            return 'continuous_maqac', ['ding.model.template.maqac']        else:            return 'continuous_qac', ['ding.model.template.qac']    def _init_learn(self) -> None:        """        Overview:            Initialize the learn mode of policy, including related attributes and modules. For DDPG, it mainly \            contains two optimizers, algorithm-specific arguments such as gamma and twin_critic, 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        # actor and critic optimizer        self._optimizer_actor = Adam(            self._model.actor.parameters(),            lr=self._cfg.learn.learning_rate_actor,        )        self._optimizer_critic = Adam(            self._model.critic.parameters(),            lr=self._cfg.learn.learning_rate_critic,        )        self._reward_batch_norm = self._cfg.reward_batch_norm        self._gamma = self._cfg.learn.discount_factor        self._actor_update_freq = self._cfg.learn.actor_update_freq        self._twin_critic = self._cfg.model.twin_critic  # True for TD3, False for DDPG        # main and target models        self._target_model = copy.deepcopy(self._model)        self._learn_model = model_wrap(self._model, wrapper_name='base')        if self._cfg.action_space == 'hybrid':            self._learn_model = model_wrap(self._learn_model, wrapper_name='hybrid_argmax_sample')            self._target_model = model_wrap(self._target_model, wrapper_name='hybrid_argmax_sample')        self._target_model = model_wrap(            self._target_model,            wrapper_name='target',            update_type='momentum',            update_kwargs={'theta': self._cfg.learn.target_theta}        )        if self._cfg.learn.noise:            self._target_model = model_wrap(                self._target_model,                wrapper_name='action_noise',                noise_type='gauss',                noise_kwargs={                    'mu': 0.0,                    'sigma': self._cfg.learn.noise_sigma                },                noise_range=self._cfg.learn.noise_range            )        self._learn_model.reset()        self._target_model.reset()        self._forward_learn_cnt = 0  # count iterations    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, action, 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 DDPG, 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 ``logit`` which is used for hybrid action space.        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 DDPGPolicy: ``ding.policy.tests.test_ddpg``.        """        loss_dict = {}        data = default_preprocess_learn(            data,            use_priority=self._cfg.priority,            use_priority_IS_weight=self._cfg.priority_IS_weight,            ignore_done=self._cfg.learn.ignore_done,            use_nstep=False        )        if self._cuda:            data = to_device(data, self._device)        # ====================        # critic learn forward        # ====================        self._learn_model.train()        self._target_model.train()        next_obs = data['next_obs']        reward = data['reward']        if self._reward_batch_norm:            reward = (reward - reward.mean()) / (reward.std() + 1e-8)        # current q value        q_value = self._learn_model.forward(data, mode='compute_critic')['q_value']        # target q value.        with torch.no_grad():            next_actor_data = self._target_model.forward(next_obs, mode='compute_actor')            next_actor_data['obs'] = next_obs            target_q_value = self._target_model.forward(next_actor_data, mode='compute_critic')['q_value']        q_value_dict = {}        target_q_value_dict = {}        if self._twin_critic:            # TD3: two critic networks            target_q_value = torch.min(target_q_value[0], target_q_value[1])  # find min one as target q value            q_value_dict['q_value'] = q_value[0].mean().data.item()            q_value_dict['q_value_twin'] = q_value[1].mean().data.item()            target_q_value_dict['target q_value'] = target_q_value.mean().data.item()            # critic network1            td_data = v_1step_td_data(q_value[0], target_q_value, reward, data['done'], data['weight'])            critic_loss, td_error_per_sample1 = v_1step_td_error(td_data, self._gamma)            loss_dict['critic_loss'] = critic_loss            # critic network2(twin network)            td_data_twin = v_1step_td_data(q_value[1], target_q_value, reward, data['done'], data['weight'])            critic_twin_loss, td_error_per_sample2 = v_1step_td_error(td_data_twin, self._gamma)            loss_dict['critic_twin_loss'] = critic_twin_loss            td_error_per_sample = (td_error_per_sample1 + td_error_per_sample2) / 2        else:            # DDPG: single critic network            q_value_dict['q_value'] = q_value.mean().data.item()            target_q_value_dict['target q_value'] = target_q_value.mean().data.item()            td_data = v_1step_td_data(q_value, target_q_value, reward, data['done'], data['weight'])            critic_loss, td_error_per_sample = v_1step_td_error(td_data, self._gamma)            loss_dict['critic_loss'] = critic_loss        # ================        # critic update        # ================        self._optimizer_critic.zero_grad()        for k in loss_dict:            if 'critic' in k:                loss_dict[k].backward()        self._optimizer_critic.step()        # ===============================        # actor learn forward and update        # ===============================        # actor updates every ``self._actor_update_freq`` iters        if (self._forward_learn_cnt + 1) % self._actor_update_freq == 0:            actor_data = self._learn_model.forward(data['obs'], mode='compute_actor')            actor_data['obs'] = data['obs']            if self._twin_critic:                actor_loss = -self._learn_model.forward(actor_data, mode='compute_critic')['q_value'][0].mean()            else:                actor_loss = -self._learn_model.forward(actor_data, mode='compute_critic')['q_value'].mean()            loss_dict['actor_loss'] = actor_loss            # actor update            self._optimizer_actor.zero_grad()            actor_loss.backward()            self._optimizer_actor.step()        # =============        # after update        # =============        loss_dict['total_loss'] = sum(loss_dict.values())        self._forward_learn_cnt += 1        self._target_model.update(self._learn_model.state_dict())        if self._cfg.action_space == 'hybrid':            action_log_value = -1.  # TODO(nyz) better way to viz hybrid action        else:            action_log_value = data['action'].mean()        return {            'cur_lr_actor': self._optimizer_actor.defaults['lr'],            'cur_lr_critic': self._optimizer_critic.defaults['lr'],            # 'q_value': np.array(q_value).mean(),            'action': action_log_value,            'priority': td_error_per_sample.abs().tolist(),            'td_error': td_error_per_sample.abs().mean(),            **loss_dict,            **q_value_dict,            **target_q_value_dict,        }    def _state_dict_learn(self) -> Dict[str, Any]:        """        Overview:            Return the state_dict of learn mode, usually including model, target_model and optimizers.        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_actor': self._optimizer_actor.state_dict(),            'optimizer_critic': self._optimizer_critic.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_actor.load_state_dict(state_dict['optimizer_actor'])        self._optimizer_critic.load_state_dict(state_dict['optimizer_critic'])    def _init_collect(self) -> None:        """        Overview:            Initialize the collect mode of policy, including related attributes and modules. For DDPG, it contains the \            collect_model to balance the exploration and exploitation with the perturbed noise mechanism, and other \            algorithm-specific arguments such as unroll_len. \            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``.        """        self._unroll_len = self._cfg.collect.unroll_len        # collect model        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        )        if self._cfg.action_space == 'hybrid':            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], **kwargs) -> 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.        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 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 DDPGPolicy: ``ding.policy.tests.test_ddpg``.        """        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, mode='compute_actor', **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: 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 DDPG, it contains obs, next_obs, action, reward, done.        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 DDPG, it contains the action and the logit of the action (in hybrid action space).            - 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'],            'reward': timestep.reward,            'done': timestep.done,        }        if self._cfg.action_space == 'hybrid':            transition['logit'] = policy_output['logit']        return transition    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 DDPG, a train sample is a processed transition (unroll_len=1).        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.        """        return get_train_sample(transitions, self._unroll_len)    def _init_eval(self) -> None:        """        Overview:            Initialize the eval mode of policy, including related attributes and modules. For DDPG, it contains the \            eval model to greedily select action type with argmax q_value mechanism for hybrid 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``.        """        self._eval_model = model_wrap(self._model, wrapper_name='base')        if self._cfg.action_space == 'hybrid':            self._eval_model = model_wrap(self._eval_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 DDPGPolicy: ``ding.policy.tests.test_ddpg``.        """        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, mode='compute_actor')        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.        """        ret = [            'cur_lr_actor', 'cur_lr_critic', 'critic_loss', 'actor_loss', 'total_loss', 'q_value', 'q_value_twin',            'action', 'td_error'        ]        if self._twin_critic:            ret += ['critic_twin_loss']        return ret
ding.policy.ddpg¶

ding.policy.ddpg ¶

DDPGPolicy ¶

default_model() ¶

Full Source Code

`ding.policy.ddpg`¶

`ding.policy.ddpg` ¶

`DDPGPolicy` ¶

`default_model()` ¶
