`ding.policy.sac`¶

`ding.policy.sac` ¶

`DiscreteSACPolicy` ¶

Bases: Policy
Overview
Policy class of discrete SAC algorithm. Paper link: https://arxiv.org/abs/1910.07207.
`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.
`SACPolicy` ¶

Bases: Policy
Overview
Policy class of continuous SAC algorithm. Paper link: https://arxiv.org/pdf/1801.01290.pdf
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.
`SQILSACPolicy` ¶

Bases: SACPolicy
Overview
Policy class of continuous SAC algorithm with SQIL extension. SAC paper link: https://arxiv.org/pdf/1801.01290.pdf SQIL paper link: https://arxiv.org/abs/1905.11108
Full Source Code

../ding/policy/sac.py
from typing import List, Dict, Any, Tuple, Unionfrom collections import namedtupleimport copyimport numpy as npimport torchimport torch.nn as nnimport torch.nn.functional as Ffrom torch.distributions import Normal, Independentfrom ding.torch_utils import Adam, to_devicefrom ding.rl_utils import v_1step_td_data, v_1step_td_error, get_train_sample, q_v_1step_td_error, q_v_1step_td_datafrom 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('discrete_sac')class DiscreteSACPolicy(Policy):    """    Overview:        Policy class of discrete SAC algorithm. Paper link: https://arxiv.org/abs/1910.07207.    """    config = dict(        # (str) RL policy register name (refer to function "POLICY_REGISTRY").        type='discrete_sac',        # (bool) Whether to use cuda for network and loss computation.        cuda=False,        # (bool) Whether to belong to on-policy or off-policy algorithm, DiscreteSAC is an off-policy algorithm.        on_policy=False,        # (bool) Whether to use priority sampling in buffer. Default to False in DiscreteSAC.        priority=False,        # (bool) Whether 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.        random_collect_size=10000,        # (bool) Whether to need policy-specific data in process transition.        transition_with_policy_data=True,        # (bool) Whether to enable multi-agent training setting.        multi_agent=False,        model=dict(            # (bool) Whether to use double-soft-q-net for target q computation.            # For more details, please refer to TD3 about Clipped Double-Q Learning trick.            twin_critic=True,        ),        # 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.            update_per_collect=1,            # (int) Minibatch size for one gradient descent.            batch_size=256,            # (float) Learning rate for soft q network.            learning_rate_q=3e-4,            # (float) Learning rate for policy network.            learning_rate_policy=3e-4,            # (float) Learning rate for auto temperature parameter `\alpha`.            learning_rate_alpha=3e-4,            # (float) Used for soft update of the target network,            # aka. Interpolation factor in EMA update for target network.            target_theta=0.005,            # (float) Discount factor for the discounted sum of rewards, aka. gamma.            discount_factor=0.99,            # (float) Entropy regularization coefficient in SAC.            # Please check out the original SAC paper (arXiv 1801.01290): Eq 1 for more details.            # If auto_alpha is set  to `True`, alpha is initialization for auto `\alpha`.            alpha=0.2,            # (bool) Whether to use auto temperature parameter `\alpha` .            # Temperature parameter `\alpha` determines the relative importance of the entropy term against the reward.            # Please check out the original SAC paper (arXiv 1801.01290): Eq 1 for more details.            # Note that: Using auto alpha needs to set the above `learning_rate_alpha`.            auto_alpha=True,            # (bool) Whether to use auto `\alpha` in log space.            log_space=True,            # (float) Target policy entropy value for auto temperature (alpha) adjustment.            target_entropy=None,            # (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 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,            # (float) Weight uniform initialization max range in the last output layer            init_w=3e-3,        ),        # 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,            # (bool) Whether to collect logit in `process_transition`.            # In some algorithm like guided cost learning, we need to use logit to train the reward model.            collector_logit=False,        ),        eval=dict(),  # for compability        other=dict(            replay_buffer=dict(                # (int) Maximum size of replay buffer. Usually, larger buffer size is good                # for SAC but cost more storage.                replay_buffer_size=1000000,            ),        ),    )    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 'discrete_maqac', ['ding.model.template.maqac']        else:            return 'discrete_qac', ['ding.model.template.qac']    def _init_learn(self) -> None:        """        Overview:            Initialize the learn mode of policy, including related attributes and modules. For DiscreteSAC, it mainly \            contains three optimizers, algorithm-specific arguments such as gamma and twin_critic, main and target \            model. Especially, the ``auto_alpha`` mechanism for balancing max entropy target is also initialized here.            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        self._twin_critic = self._cfg.model.twin_critic        self._optimizer_q = Adam(            self._model.critic.parameters(),            lr=self._cfg.learn.learning_rate_q,        )        self._optimizer_policy = Adam(            self._model.actor.parameters(),            lr=self._cfg.learn.learning_rate_policy,        )        # Algorithm-Specific Config        self._gamma = self._cfg.learn.discount_factor        if self._cfg.learn.auto_alpha:            if self._cfg.learn.target_entropy is None:                assert 'action_shape' in self._cfg.model, "DiscreteSAC need network model with action_shape variable"                self._target_entropy = -np.prod(self._cfg.model.action_shape)            else:                self._target_entropy = self._cfg.learn.target_entropy            if self._cfg.learn.log_space:                self._log_alpha = torch.log(torch.FloatTensor([self._cfg.learn.alpha]))                self._log_alpha = self._log_alpha.to(self._device).requires_grad_()                self._alpha_optim = torch.optim.Adam([self._log_alpha], lr=self._cfg.learn.learning_rate_alpha)                assert self._log_alpha.shape == torch.Size([1]) and self._log_alpha.requires_grad                self._alpha = self._log_alpha.detach().exp()                self._auto_alpha = True                self._log_space = True            else:                self._alpha = torch.FloatTensor([self._cfg.learn.alpha]).to(self._device).requires_grad_()                self._alpha_optim = torch.optim.Adam([self._alpha], lr=self._cfg.learn.learning_rate_alpha)                self._auto_alpha = True                self._log_space = False        else:            self._alpha = torch.tensor(                [self._cfg.learn.alpha], requires_grad=False, device=self._device, dtype=torch.float32            )            self._auto_alpha = False        # Main and target models        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='base')        self._learn_model.reset()        self._target_model.reset()    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 SAC, each element in list is a dict containing at least the following keys: ``obs``, ``action``, \                ``logit``, ``reward``, ``next_obs``, ``done``. Sometimes, it also contains other keys like ``weight``.        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 DiscreteSACPolicy: \            ``ding.policy.tests.test_discrete_sac``.        """        loss_dict = {}        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=False        )        if self._cuda:            data = to_device(data, self._device)        self._learn_model.train()        self._target_model.train()        obs = data['obs']        next_obs = data['next_obs']        reward = data['reward']        done = data['done']        logit = data['logit']        action = data['action']        # 1. predict q value        q_value = self._learn_model.forward(obs, mode='compute_critic')['q_value']        dist = torch.distributions.categorical.Categorical(logits=logit)        dist_entropy = dist.entropy()        entropy = dist_entropy.mean()        # 2. predict target value        # target q value. SARSA: first predict next action, then calculate next q value        with torch.no_grad():            policy_output_next = self._learn_model.forward(next_obs, mode='compute_actor')            if self._cfg.multi_agent:                policy_output_next['logit'][policy_output_next['action_mask'] == 0.0] = -1e8            prob = F.softmax(policy_output_next['logit'], dim=-1)            log_prob = torch.log(prob + 1e-8)            target_q_value = self._target_model.forward(next_obs, mode='compute_critic')['q_value']            # the value of a policy according to the maximum entropy objective            if self._twin_critic:                # find min one as target q value                target_value = (                    prob * (torch.min(target_q_value[0], target_q_value[1]) - self._alpha * log_prob.squeeze(-1))                ).sum(dim=-1)            else:                target_value = (prob * (target_q_value - self._alpha * log_prob.squeeze(-1))).sum(dim=-1)        # 3. compute q loss        if self._twin_critic:            q_data0 = q_v_1step_td_data(q_value[0], target_value, action, reward, done, data['weight'])            loss_dict['critic_loss'], td_error_per_sample0 = q_v_1step_td_error(q_data0, self._gamma)            q_data1 = q_v_1step_td_data(q_value[1], target_value, action, reward, done, data['weight'])            loss_dict['twin_critic_loss'], td_error_per_sample1 = q_v_1step_td_error(q_data1, self._gamma)            td_error_per_sample = (td_error_per_sample0 + td_error_per_sample1) / 2        else:            q_data = q_v_1step_td_data(q_value, target_value, action, reward, done, data['weight'])            loss_dict['critic_loss'], td_error_per_sample = q_v_1step_td_error(q_data, self._gamma)        # 4. update q network        self._optimizer_q.zero_grad()        loss_dict['critic_loss'].backward()        if self._twin_critic:            loss_dict['twin_critic_loss'].backward()        self._optimizer_q.step()        # 5. evaluate to get action distribution        policy_output = self._learn_model.forward(obs, mode='compute_actor')        # 6. apply discrete action mask in multi_agent setting        if self._cfg.multi_agent:            policy_output['logit'][policy_output['action_mask'] == 0.0] = -1e8        logit = policy_output['logit']        prob = F.softmax(logit, dim=-1)        log_prob = F.log_softmax(logit, dim=-1)        with torch.no_grad():            new_q_value = self._learn_model.forward(obs, mode='compute_critic')['q_value']            if self._twin_critic:                new_q_value = torch.min(new_q_value[0], new_q_value[1])        # 7. compute policy loss        # we need to sum different actions' policy loss and calculate the average value of a batch        policy_loss = (prob * (self._alpha * log_prob - new_q_value)).sum(dim=-1).mean()        loss_dict['policy_loss'] = policy_loss        # 8. update policy network        self._optimizer_policy.zero_grad()        loss_dict['policy_loss'].backward()        self._optimizer_policy.step()        # 9. compute alpha loss        if self._auto_alpha:            if self._log_space:                log_prob = log_prob + self._target_entropy                loss_dict['alpha_loss'] = (-prob.detach() * (self._log_alpha * log_prob.detach())).sum(dim=-1).mean()                self._alpha_optim.zero_grad()                loss_dict['alpha_loss'].backward()                self._alpha_optim.step()                self._alpha = self._log_alpha.detach().exp()            else:                log_prob = log_prob + self._target_entropy                loss_dict['alpha_loss'] = (-prob.detach() * (self._alpha * log_prob.detach())).sum(dim=-1).mean()                self._alpha_optim.zero_grad()                loss_dict['alpha_loss'].backward()                self._alpha_optim.step()                self._alpha.data = torch.where(self._alpha > 0, self._alpha,                                               torch.zeros_like(self._alpha)).requires_grad_()        loss_dict['total_loss'] = sum(loss_dict.values())        # target update        self._target_model.update(self._learn_model.state_dict())        return {            'total_loss': loss_dict['total_loss'].item(),            'policy_loss': loss_dict['policy_loss'].item(),            'critic_loss': loss_dict['critic_loss'].item(),            'cur_lr_q': self._optimizer_q.defaults['lr'],            'cur_lr_p': self._optimizer_policy.defaults['lr'],            'priority': td_error_per_sample.abs().tolist(),            'td_error': td_error_per_sample.detach().mean().item(),            'alpha': self._alpha.item(),            'q_value_1': target_q_value[0].detach().mean().item(),            'q_value_2': target_q_value[1].detach().mean().item(),            'target_value': target_value.detach().mean().item(),            'entropy': entropy.item(),        }    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.        """        ret = {            'model': self._learn_model.state_dict(),            'target_model': self._target_model.state_dict(),            'optimizer_q': self._optimizer_q.state_dict(),            'optimizer_policy': self._optimizer_policy.state_dict(),        }        if self._auto_alpha:            ret.update({'optimizer_alpha': self._alpha_optim.state_dict()})        return ret    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_q.load_state_dict(state_dict['optimizer_q'])        self._optimizer_policy.load_state_dict(state_dict['optimizer_policy'])        if self._auto_alpha:            self._alpha_optim.load_state_dict(state_dict['optimizer_alpha'])    def _init_collect(self) -> None:        """        Overview:            Initialize the collect mode of policy, including related attributes and modules. For SAC, it contains the \            collect_model to balance the exploration and exploitation with the epsilon and multinomial sample \            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        # Empirically, we found that eps_greedy_multinomial_sample works better than multinomial_sample        # and eps_greedy_sample, and we don't divide logit by alpha,        # for the details please refer to ding/model/wrapper/model_wrappers        self._collect_model = model_wrap(self._model, wrapper_name='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 DiscreteSACPolicy: \            ``ding.policy.tests.test_discrete_sac``.        """        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', 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 _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 discrete SAC, it contains obs, next_obs, logit, 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 discrete SAC, it contains the action and the logit 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 _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 discrete SAC, 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 DiscreteSAC, it contains \            the eval model to greedily select action type 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='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 DiscreteSACPolicy: \            ``ding.policy.tests.test_discrete_sac``.        """        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.        """        twin_critic = ['twin_critic_loss'] if self._twin_critic else []        if self._auto_alpha:            return super()._monitor_vars_learn() + [                'alpha_loss', 'policy_loss', 'critic_loss', 'cur_lr_q', 'cur_lr_p', 'target_q_value', 'q_value_1',                'q_value_2', 'alpha', 'td_error', 'target_value', 'entropy'            ] + twin_critic        else:            return super()._monitor_vars_learn() + [                'policy_loss', 'critic_loss', 'cur_lr_q', 'cur_lr_p', 'target_q_value', 'q_value_1', 'q_value_2',                'alpha', 'td_error', 'target_value', 'entropy'            ] + twin_critic@POLICY_REGISTRY.register('sac')class SACPolicy(Policy):    """    Overview:        Policy class of continuous SAC algorithm. Paper link: https://arxiv.org/pdf/1801.01290.pdf    Config:           == ====================  ========    =============  ================================= =======================           ID Symbol                Type        Default Value  Description                       Other           == ====================  ========    =============  ================================= =======================           1  ``type``              str         sac            | RL policy register name, refer  | this arg is optional,                                                               | to registry ``POLICY_REGISTRY`` | a placeholder           2  ``cuda``              bool        True           | Whether to use cuda for network |           3  ``on_policy``         bool        False          | SAC is an off-policy            |                                                               | algorithm.                      |           4  ``priority``          bool        False          | Whether to use priority         |                                                               | sampling in buffer.             |           5  | ``priority_IS_``    bool        False          | Whether use Importance Sampling |              | ``weight``                                     | weight to correct biased update |           6  | ``random_``         int         10000          | Number of randomly collected    | Default to 10000 for              | ``collect_size``                               | training samples in replay      | SAC, 25000 for DDPG/              |                                                | buffer when training starts.    | TD3.           7  | ``learn.learning``  float       3e-4           | Learning rate for soft q        | Defalut to 1e-3              | ``_rate_q``                                    | network.                        |           8  | ``learn.learning``  float       3e-4           | Learning rate for policy        | Defalut to 1e-3              | ``_rate_policy``                               | network.                        |           9  | ``learn.alpha``     float       0.2            | Entropy regularization          | alpha is initiali-              |                                                | coefficient.                    | zation for auto              |                                                |                                 | alpha, when              |                                                |                                 | auto_alpha is True           10 | ``learn.``          bool        False          | Determine whether to use        | Temperature parameter              | ``auto_alpha``                                 | auto temperature parameter      | determines the              |                                                | alpha.                          | relative importance              |                                                |                                 | of the entropy term              |                                                |                                 | against the reward.           11 | ``learn.-``         bool        False          | Determine whether to ignore     | Use ignore_done only              | ``ignore_done``                                | done flag.                      | in env like Pendulum           12 | ``learn.-``         float       0.005          | Used for soft update of the     | aka. Interpolation              | ``target_theta``                               | target network.                 | factor in polyak aver              |                                                |                                 | aging for target              |                                                |                                 | networks.           == ====================  ========    =============  ================================= =======================    """    config = dict(        # (str) RL policy register name (refer to function "POLICY_REGISTRY").        type='sac',        # (bool) Whether to use cuda for network and loss computation.        cuda=False,        # (bool) Whether to belong to on-policy or off-policy algorithm, SAC is an off-policy algorithm.        on_policy=False,        # (bool) Whether to use priority sampling in buffer. Default to False in SAC.        priority=False,        # (bool) Whether 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.        random_collect_size=10000,        # (bool) Whether to need policy-specific data in process transition.        transition_with_policy_data=True,        # (bool) Whether to enable multi-agent training setting.        multi_agent=False,        model=dict(            # (bool) Whether to use double-soft-q-net for target q computation.            # For more details, please refer to TD3 about Clipped Double-Q Learning trick.            twin_critic=True,            # (str) Use reparameterization trick for continous action.            action_space='reparameterization',        ),        # 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.            update_per_collect=1,            # (int) Minibatch size for one gradient descent.            batch_size=256,            # (float) Learning rate for soft q network.            learning_rate_q=3e-4,            # (float) Learning rate for policy network.            learning_rate_policy=3e-4,            # (float) Learning rate for auto temperature parameter `\alpha`.            learning_rate_alpha=3e-4,            # (float) Used for soft update of the target network,            # aka. Interpolation factor in EMA update for target network.            target_theta=0.005,            # (float) discount factor for the discounted sum of rewards, aka. gamma.            discount_factor=0.99,            # (float) Entropy regularization coefficient in SAC.            # Please check out the original SAC paper (arXiv 1801.01290): Eq 1 for more details.            # If auto_alpha is set  to `True`, alpha is initialization for auto `\alpha`.            alpha=0.2,            # (bool) Whether to use auto temperature parameter `\alpha` .            # Temperature parameter `\alpha` determines the relative importance of the entropy term against the reward.            # Please check out the original SAC paper (arXiv 1801.01290): Eq 1 for more details.            # Note that: Using auto alpha needs to set the above `learning_rate_alpha`.            auto_alpha=True,            # (bool) Whether to use auto `\alpha` in log space.            log_space=True,            # (float) Target policy entropy value for auto temperature (alpha) adjustment.            target_entropy=None,            # (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) Weight uniform initialization max range in the last output layer.            init_w=3e-3,        ),        # collect_mode config        collect=dict(            # (int) How many training samples collected in one collection procedure.            n_sample=1,            # (int) Split episodes or trajectories into pieces with length `unroll_len`.            unroll_len=1,            # (bool) Whether to collect logit in `process_transition`.            # In some algorithm like guided cost learning, we need to use logit to train the reward model.            collector_logit=False,        ),        eval=dict(),  # for compability        other=dict(            replay_buffer=dict(                # (int) Maximum size of replay buffer. Usually, larger buffer size is good                # for SAC but cost more storage.                replay_buffer_size=1000000,            ),        ),    )    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 SAC, it mainly \            contains three optimizers, algorithm-specific arguments such as gamma and twin_critic, main and target \            model. Especially, the ``auto_alpha`` mechanism for balancing max entropy target is also initialized here.            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        self._twin_critic = self._cfg.model.twin_critic        # Weight Init for the last output layer        if hasattr(self._model, 'actor_head'):  # keep compatibility            init_w = self._cfg.learn.init_w            self._model.actor_head[-1].mu.weight.data.uniform_(-init_w, init_w)            self._model.actor_head[-1].mu.bias.data.uniform_(-init_w, init_w)            self._model.actor_head[-1].log_sigma_layer.weight.data.uniform_(-init_w, init_w)            self._model.actor_head[-1].log_sigma_layer.bias.data.uniform_(-init_w, init_w)        self._optimizer_q = Adam(            self._model.critic.parameters(),            lr=self._cfg.learn.learning_rate_q,        )        self._optimizer_policy = Adam(            self._model.actor.parameters(),            lr=self._cfg.learn.learning_rate_policy,        )        # Algorithm-Specific Config        self._gamma = self._cfg.learn.discount_factor        if self._cfg.learn.auto_alpha:            if self._cfg.learn.target_entropy is None:                assert 'action_shape' in self._cfg.model, "SAC need network model with action_shape variable"                self._target_entropy = -np.prod(self._cfg.model.action_shape)            else:                self._target_entropy = self._cfg.learn.target_entropy            if self._cfg.learn.log_space:                self._log_alpha = torch.log(torch.FloatTensor([self._cfg.learn.alpha]))                self._log_alpha = self._log_alpha.to(self._device).requires_grad_()                self._alpha_optim = torch.optim.Adam([self._log_alpha], lr=self._cfg.learn.learning_rate_alpha)                assert self._log_alpha.shape == torch.Size([1]) and self._log_alpha.requires_grad                self._alpha = self._log_alpha.detach().exp()                self._auto_alpha = True                self._log_space = True            else:                self._alpha = torch.FloatTensor([self._cfg.learn.alpha]).to(self._device).requires_grad_()                self._alpha_optim = torch.optim.Adam([self._alpha], lr=self._cfg.learn.learning_rate_alpha)                self._auto_alpha = True                self._log_space = False        else:            self._alpha = torch.tensor(                [self._cfg.learn.alpha], requires_grad=False, device=self._device, dtype=torch.float32            )            self._auto_alpha = False        # Main and target models        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='base')        self._learn_model.reset()        self._target_model.reset()    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 SAC, 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``.        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 SACPolicy: ``ding.policy.tests.test_sac``.        """        loss_dict = {}        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=False        )        if self._cuda:            data = to_device(data, self._device)        self._learn_model.train()        self._target_model.train()        obs = data['obs']        next_obs = data['next_obs']        reward = data['reward']        done = data['done']        # 1. predict q value        q_value = self._learn_model.forward(data, mode='compute_critic')['q_value']        # 2. predict target value        with torch.no_grad():            (mu, sigma) = self._learn_model.forward(next_obs, mode='compute_actor')['logit']            dist = Independent(Normal(mu, sigma), 1)            pred = dist.rsample()            next_action = torch.tanh(pred)            y = 1 - next_action.pow(2) + 1e-6            # keep dimension for loss computation (usually for action space is 1 env. e.g. pendulum)            next_log_prob = dist.log_prob(pred).unsqueeze(-1)            next_log_prob = next_log_prob - torch.log(y).sum(-1, keepdim=True)            next_data = {'obs': next_obs, 'action': next_action}            target_q_value = self._target_model.forward(next_data, mode='compute_critic')['q_value']            # the value of a policy according to the maximum entropy objective            if self._twin_critic:                # find min one as target q value                target_q_value = torch.min(target_q_value[0],                                           target_q_value[1]) - self._alpha * next_log_prob.squeeze(-1)            else:                target_q_value = target_q_value - self._alpha * next_log_prob.squeeze(-1)        # 3. compute q loss        if self._twin_critic:            q_data0 = v_1step_td_data(q_value[0], target_q_value, reward, done, data['weight'])            loss_dict['critic_loss'], td_error_per_sample0 = v_1step_td_error(q_data0, self._gamma)            q_data1 = v_1step_td_data(q_value[1], target_q_value, reward, done, data['weight'])            loss_dict['twin_critic_loss'], td_error_per_sample1 = v_1step_td_error(q_data1, self._gamma)            td_error_per_sample = (td_error_per_sample0 + td_error_per_sample1) / 2        else:            q_data = v_1step_td_data(q_value, target_q_value, reward, done, data['weight'])            loss_dict['critic_loss'], td_error_per_sample = v_1step_td_error(q_data, self._gamma)        # 4. update q network        self._optimizer_q.zero_grad()        if self._twin_critic:            (loss_dict['critic_loss'] + loss_dict['twin_critic_loss']).backward()        else:            loss_dict['critic_loss'].backward()        self._optimizer_q.step()        # 5. evaluate to get action distribution        (mu, sigma) = self._learn_model.forward(data['obs'], mode='compute_actor')['logit']        dist = Independent(Normal(mu, sigma), 1)        pred = dist.rsample()        action = torch.tanh(pred)        y = 1 - action.pow(2) + 1e-6        # keep dimension for loss computation (usually for action space is 1 env. e.g. pendulum)        log_prob = dist.log_prob(pred).unsqueeze(-1)        log_prob = log_prob - torch.log(y).sum(-1, keepdim=True)        eval_data = {'obs': obs, 'action': action}        new_q_value = self._learn_model.forward(eval_data, mode='compute_critic')['q_value']        if self._twin_critic:            new_q_value = torch.min(new_q_value[0], new_q_value[1])        # 6. compute policy loss        policy_loss = (self._alpha * log_prob - new_q_value.unsqueeze(-1)).mean()        loss_dict['policy_loss'] = policy_loss        # 7. update policy network        self._optimizer_policy.zero_grad()        loss_dict['policy_loss'].backward()        self._optimizer_policy.step()        # 8. compute alpha loss        if self._auto_alpha:            if self._log_space:                log_prob = log_prob + self._target_entropy                loss_dict['alpha_loss'] = -(self._log_alpha * log_prob.detach()).mean()                self._alpha_optim.zero_grad()                loss_dict['alpha_loss'].backward()                self._alpha_optim.step()                self._alpha = self._log_alpha.detach().exp()            else:                log_prob = log_prob + self._target_entropy                loss_dict['alpha_loss'] = -(self._alpha * log_prob.detach()).mean()                self._alpha_optim.zero_grad()                loss_dict['alpha_loss'].backward()                self._alpha_optim.step()                self._alpha = max(0, self._alpha)        loss_dict['total_loss'] = sum(loss_dict.values())        # target update        self._target_model.update(self._learn_model.state_dict())        return {            'cur_lr_q': self._optimizer_q.defaults['lr'],            'cur_lr_p': self._optimizer_policy.defaults['lr'],            'priority': td_error_per_sample.abs().tolist(),            'td_error': td_error_per_sample.detach().mean().item(),            'alpha': self._alpha.item(),            'target_q_value': target_q_value.detach().mean().item(),            'transformed_log_prob': log_prob.mean().item(),            **loss_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.        """        ret = {            'model': self._learn_model.state_dict(),            'target_model': self._target_model.state_dict(),            'optimizer_q': self._optimizer_q.state_dict(),            'optimizer_policy': self._optimizer_policy.state_dict(),        }        if self._auto_alpha:            ret.update({'optimizer_alpha': self._alpha_optim.state_dict()})        return ret    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_q.load_state_dict(state_dict['optimizer_q'])        self._optimizer_policy.load_state_dict(state_dict['optimizer_policy'])        if self._auto_alpha:            self._alpha_optim.load_state_dict(state_dict['optimizer_alpha'])    def _init_collect(self) -> None:        """        Overview:            Initialize the collect mode of policy, including related attributes and modules. For SAC, it contains the \            collect_model 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        self._collect_model = model_wrap(self._model, wrapper_name='base')        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::            ``logit`` in SAC means the mu and sigma of Gaussioan distribution. Here we use this name for consistency.        .. note::            For more detailed examples, please refer to our unittest for SACPolicy: ``ding.policy.tests.test_sac``.        """        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():            (mu, sigma) = self._collect_model.forward(data, mode='compute_actor')['logit']            dist = Independent(Normal(mu, sigma), 1)            action = torch.tanh(dist.rsample())            output = {'logit': (mu, sigma), 'action': action}        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 continuous SAC, it contains obs, next_obs, action, reward, done. The logit \            will be also added when ``collector_logit`` is True.        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 continuous SAC, it contains the action and the logit (mu and sigma) 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.        """        if self._cfg.collect.collector_logit:            transition = {                'obs': obs,                'next_obs': timestep.obs,                'logit': policy_output['logit'],                'action': policy_output['action'],                'reward': timestep.reward,                'done': timestep.done,            }        else:            transition = {                'obs': obs,                'next_obs': timestep.obs,                'action': policy_output['action'],                'reward': timestep.reward,                'done': timestep.done,            }        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 continuous SAC, 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 SAC, it contains the \            eval model, which is equipped with ``base`` model wrapper to ensure compability.            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')        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::            ``logit`` in SAC means the mu and sigma of Gaussioan distribution. Here we use this name for consistency.        .. note::            For more detailed examples, please refer to our unittest for SACPolicy: ``ding.policy.tests.test_sac``.        """        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():            (mu, sigma) = self._eval_model.forward(data, mode='compute_actor')['logit']            action = torch.tanh(mu)  # deterministic_eval            output = {'action': action}        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.        """        twin_critic = ['twin_critic_loss'] if self._twin_critic else []        alpha_loss = ['alpha_loss'] if self._auto_alpha else []        return [            'value_loss'            'alpha_loss',            'policy_loss',            'critic_loss',            'cur_lr_q',            'cur_lr_p',            'target_q_value',            'alpha',            'td_error',            'transformed_log_prob',        ] + twin_critic + alpha_loss@POLICY_REGISTRY.register('sqil_sac')class SQILSACPolicy(SACPolicy):    """    Overview:        Policy class of continuous SAC algorithm with SQIL extension.        SAC paper link: https://arxiv.org/pdf/1801.01290.pdf        SQIL paper link: https://arxiv.org/abs/1905.11108    """    def _init_learn(self) -> None:        """        Overview:            Initialize the learn mode of policy, including related attributes and modules. For SAC, it mainly \            contains three optimizers, algorithm-specific arguments such as gamma and twin_critic, main and target \            model. Especially, the ``auto_alpha`` mechanism for balancing max entropy target is also initialized here.            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        self._twin_critic = self._cfg.model.twin_critic        # Weight Init for the last output layer        init_w = self._cfg.learn.init_w        self._model.actor_head[-1].mu.weight.data.uniform_(-init_w, init_w)        self._model.actor_head[-1].mu.bias.data.uniform_(-init_w, init_w)        self._model.actor_head[-1].log_sigma_layer.weight.data.uniform_(-init_w, init_w)        self._model.actor_head[-1].log_sigma_layer.bias.data.uniform_(-init_w, init_w)        self._optimizer_q = Adam(            self._model.critic.parameters(),            lr=self._cfg.learn.learning_rate_q,        )        self._optimizer_policy = Adam(            self._model.actor.parameters(),            lr=self._cfg.learn.learning_rate_policy,        )        # Algorithm-Specific Config        self._gamma = self._cfg.learn.discount_factor        if self._cfg.learn.auto_alpha:            if self._cfg.learn.target_entropy is None:                assert 'action_shape' in self._cfg.model, "SQILSACPolicy need network model with action_shape variable"                self._target_entropy = -np.prod(self._cfg.model.action_shape)            else:                self._target_entropy = self._cfg.learn.target_entropy            if self._cfg.learn.log_space:                self._log_alpha = torch.log(torch.FloatTensor([self._cfg.learn.alpha]))                self._log_alpha = self._log_alpha.to(self._device).requires_grad_()                self._alpha_optim = torch.optim.Adam([self._log_alpha], lr=self._cfg.learn.learning_rate_alpha)                assert self._log_alpha.shape == torch.Size([1]) and self._log_alpha.requires_grad                self._alpha = self._log_alpha.detach().exp()                self._auto_alpha = True                self._log_space = True            else:                self._alpha = torch.FloatTensor([self._cfg.learn.alpha]).to(self._device).requires_grad_()                self._alpha_optim = torch.optim.Adam([self._alpha], lr=self._cfg.learn.learning_rate_alpha)                self._auto_alpha = True                self._log_space = False        else:            self._alpha = torch.tensor(                [self._cfg.learn.alpha], requires_grad=False, device=self._device, dtype=torch.float32            )            self._auto_alpha = False        # Main and target models        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='base')        self._learn_model.reset()        self._target_model.reset()        # monitor cossimilarity and entropy switch        self._monitor_cos = True        self._monitor_entropy = True    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 SAC, 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``.        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::            For SQIL + SAC, input data is composed of two parts with the same size: agent data and expert data. \            Both of them are relabelled with new reward according to SQIL algorithm.        .. 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 SACPolicy: ``ding.policy.tests.test_sac``.        """        loss_dict = {}        if self._monitor_cos:            agent_data = default_preprocess_learn(                data[0:len(data) // 2],                use_priority=self._priority,                use_priority_IS_weight=self._cfg.priority_IS_weight,                ignore_done=self._cfg.learn.ignore_done,                use_nstep=False            )            expert_data = default_preprocess_learn(                data[len(data) // 2:],                use_priority=self._priority,                use_priority_IS_weight=self._cfg.priority_IS_weight,                ignore_done=self._cfg.learn.ignore_done,                use_nstep=False            )            if self._cuda:                agent_data = to_device(agent_data, self._device)                expert_data = to_device(expert_data, self._device)        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=False        )        if self._cuda:            data = to_device(data, self._device)        self._learn_model.train()        self._target_model.train()        obs = data['obs']        next_obs = data['next_obs']        reward = data['reward']        done = data['done']        # 1. predict q value        q_value = self._learn_model.forward(data, mode='compute_critic')['q_value']        # 2. predict target value        with torch.no_grad():            (mu, sigma) = self._learn_model.forward(next_obs, mode='compute_actor')['logit']            dist = Independent(Normal(mu, sigma), 1)            pred = dist.rsample()            next_action = torch.tanh(pred)            y = 1 - next_action.pow(2) + 1e-6            # keep dimension for loss computation (usually for action space is 1 env. e.g. pendulum)            next_log_prob = dist.log_prob(pred).unsqueeze(-1)            next_log_prob = next_log_prob - torch.log(y).sum(-1, keepdim=True)            next_data = {'obs': next_obs, 'action': next_action}            target_q_value = self._target_model.forward(next_data, mode='compute_critic')['q_value']            # the value of a policy according to the maximum entropy objective            if self._twin_critic:                # find min one as target q value                target_q_value = torch.min(target_q_value[0],                                           target_q_value[1]) - self._alpha * next_log_prob.squeeze(-1)            else:                target_q_value = target_q_value - self._alpha * next_log_prob.squeeze(-1)        # 3. compute q loss        if self._twin_critic:            q_data0 = v_1step_td_data(q_value[0], target_q_value, reward, done, data['weight'])            loss_dict['critic_loss'], td_error_per_sample0 = v_1step_td_error(q_data0, self._gamma)            q_data1 = v_1step_td_data(q_value[1], target_q_value, reward, done, data['weight'])            loss_dict['twin_critic_loss'], td_error_per_sample1 = v_1step_td_error(q_data1, self._gamma)            td_error_per_sample = (td_error_per_sample0 + td_error_per_sample1) / 2        else:            q_data = v_1step_td_data(q_value, target_q_value, reward, done, data['weight'])            loss_dict['critic_loss'], td_error_per_sample = v_1step_td_error(q_data, self._gamma)        # 4. update q network        self._optimizer_q.zero_grad()        if self._twin_critic:            (loss_dict['critic_loss'] + loss_dict['twin_critic_loss']).backward()        else:            loss_dict['critic_loss'].backward()        self._optimizer_q.step()        # 5. evaluate to get action distribution        if self._monitor_cos:            # agent            (mu, sigma) = self._learn_model.forward(agent_data['obs'], mode='compute_actor')['logit']            dist = Independent(Normal(mu, sigma), 1)            pred = dist.rsample()            action = torch.tanh(pred)            y = 1 - action.pow(2) + 1e-6            # keep dimension for loss computation (usually for action space is 1 env. e.g. pendulum)            agent_log_prob = dist.log_prob(pred).unsqueeze(-1)            agent_log_prob = agent_log_prob - torch.log(y).sum(-1, keepdim=True)            eval_data = {'obs': agent_data['obs'], 'action': action}            agent_new_q_value = self._learn_model.forward(eval_data, mode='compute_critic')['q_value']            if self._twin_critic:                agent_new_q_value = torch.min(agent_new_q_value[0], agent_new_q_value[1])            # expert            (mu, sigma) = self._learn_model.forward(expert_data['obs'], mode='compute_actor')['logit']            dist = Independent(Normal(mu, sigma), 1)            pred = dist.rsample()            action = torch.tanh(pred)            y = 1 - action.pow(2) + 1e-6            # keep dimension for loss computation (usually for action space is 1 env. e.g. pendulum)            expert_log_prob = dist.log_prob(pred).unsqueeze(-1)            expert_log_prob = expert_log_prob - torch.log(y).sum(-1, keepdim=True)            eval_data = {'obs': expert_data['obs'], 'action': action}            expert_new_q_value = self._learn_model.forward(eval_data, mode='compute_critic')['q_value']            if self._twin_critic:                expert_new_q_value = torch.min(expert_new_q_value[0], expert_new_q_value[1])        (mu, sigma) = self._learn_model.forward(data['obs'], mode='compute_actor')['logit']        dist = Independent(Normal(mu, sigma), 1)        # for monitor the entropy of policy        if self._monitor_entropy:            dist_entropy = dist.entropy()            entropy = dist_entropy.mean()        pred = dist.rsample()        action = torch.tanh(pred)        y = 1 - action.pow(2) + 1e-6        # keep dimension for loss computation (usually for action space is 1 env. e.g. pendulum)        log_prob = dist.log_prob(pred).unsqueeze(-1)        log_prob = log_prob - torch.log(y).sum(-1, keepdim=True)        eval_data = {'obs': obs, 'action': action}        new_q_value = self._learn_model.forward(eval_data, mode='compute_critic')['q_value']        if self._twin_critic:            new_q_value = torch.min(new_q_value[0], new_q_value[1])        # 6. compute policy loss        policy_loss = (self._alpha * log_prob - new_q_value.unsqueeze(-1)).mean()        loss_dict['policy_loss'] = policy_loss        # 7. update policy network        if self._monitor_cos:            agent_policy_loss = (self._alpha * agent_log_prob - agent_new_q_value.unsqueeze(-1)).mean()            expert_policy_loss = (self._alpha * expert_log_prob - expert_new_q_value.unsqueeze(-1)).mean()            loss_dict['agent_policy_loss'] = agent_policy_loss            loss_dict['expert_policy_loss'] = expert_policy_loss            self._optimizer_policy.zero_grad()            loss_dict['agent_policy_loss'].backward()            agent_grad = (list(list(self._learn_model.actor.children())[-1].children())[-1].weight.grad).mean()            self._optimizer_policy.zero_grad()            loss_dict['expert_policy_loss'].backward()            expert_grad = (list(list(self._learn_model.actor.children())[-1].children())[-1].weight.grad).mean()            cos = nn.CosineSimilarity(dim=0)            cos_similarity = cos(agent_grad, expert_grad)        self._optimizer_policy.zero_grad()        loss_dict['policy_loss'].backward()        self._optimizer_policy.step()        # 8. compute alpha loss        if self._auto_alpha:            if self._log_space:                log_prob = log_prob + self._target_entropy                loss_dict['alpha_loss'] = -(self._log_alpha * log_prob.detach()).mean()                self._alpha_optim.zero_grad()                loss_dict['alpha_loss'].backward()                self._alpha_optim.step()                self._alpha = self._log_alpha.detach().exp()            else:                log_prob = log_prob + self._target_entropy                loss_dict['alpha_loss'] = -(self._alpha * log_prob.detach()).mean()                self._alpha_optim.zero_grad()                loss_dict['alpha_loss'].backward()                self._alpha_optim.step()                self._alpha = max(0, self._alpha)        loss_dict['total_loss'] = sum(loss_dict.values())        # target update        self._target_model.update(self._learn_model.state_dict())        var_monitor = {            'cur_lr_q': self._optimizer_q.defaults['lr'],            'cur_lr_p': self._optimizer_policy.defaults['lr'],            'priority': td_error_per_sample.abs().tolist(),            'td_error': td_error_per_sample.detach().mean().item(),            'agent_td_error': td_error_per_sample.detach().chunk(2, dim=0)[0].mean().item(),            'expert_td_error': td_error_per_sample.detach().chunk(2, dim=0)[1].mean().item(),            'alpha': self._alpha.item(),            'target_q_value': target_q_value.detach().mean().item(),            'mu': mu.detach().mean().item(),            'sigma': sigma.detach().mean().item(),            'q_value0': new_q_value[0].detach().mean().item(),            'q_value1': new_q_value[1].detach().mean().item(),            **loss_dict,        }        if self._monitor_cos:            var_monitor['cos_similarity'] = cos_similarity.item()        if self._monitor_entropy:            var_monitor['entropy'] = entropy.item()        return var_monitor    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.        """        twin_critic = ['twin_critic_loss'] if self._twin_critic else []        alpha_loss = ['alpha_loss'] if self._auto_alpha else []        cos_similarity = ['cos_similarity'] if self._monitor_cos else []        entropy = ['entropy'] if self._monitor_entropy else []        return [            'value_loss'            'alpha_loss',            'policy_loss',            'critic_loss',            'cur_lr_q',            'cur_lr_p',            'target_q_value',            'alpha',            'td_error',            'agent_td_error',            'expert_td_error',            'mu',            'sigma',            'q_value0',            'q_value1',        ] + twin_critic + alpha_loss + cos_similarity + entropy
ding.policy.sac¶

ding.policy.sac ¶

DiscreteSACPolicy ¶

default_model() ¶

SACPolicy ¶

default_model() ¶

SQILSACPolicy ¶

Full Source Code

`ding.policy.sac`¶

`ding.policy.sac` ¶

`DiscreteSACPolicy` ¶

`default_model()` ¶

`SACPolicy` ¶

`default_model()` ¶

`SQILSACPolicy` ¶
