`ding.policy.acer`¶

`ding.policy.acer` ¶

`ACERPolicy` ¶

Bases: Policy
Overview
Policy class of ACER algorithm.
Config
== ======================= ======== ============== ===================================== ======================= ID Symbol Type Default Value Description Other(Shape) == ======================= ======== ============== ===================================== ======================= 1 type str acer | RL policy register name, refer to | this arg is optional, | registry POLICY_REGISTRY | a placeholder 2 cuda bool False | Whether to use cuda for network | this arg can be diff- | | erent from modes 3 on_policy bool False | Whether the RL algorithm is | on-policy or off-policy 4 trust_region bool True | Whether the RL algorithm use trust | | region constraint | 5 trust_region_value float 1.0 | maximum range of the trust region | 6 unroll_len int 32 | trajectory length to calculate | Q retrace target 7 learn.update int 4 | How many updates(iterations) to | this args can be vary per_collect | train after collector's one | from envs. Bigger val | collection. Only | | valid in serial training | means more off-policy 8 c_clip_ratio float 1.0 | clip ratio of importance weights | == ======================= ======== ============== ===================================== =======================
Full Source Code

../ding/policy/acer.py
from collections import namedtuplefrom typing import List, Dict, Any, Tupleimport copyimport torchfrom ding.model import model_wrapfrom ding.rl_utils import get_train_sample, compute_q_retraces, acer_policy_error,\     acer_value_error, acer_trust_region_updatefrom ding.torch_utils import Adam, RMSprop, to_devicefrom ding.utils import POLICY_REGISTRYfrom ding.utils.data import default_collate, default_decollatefrom ding.policy.base_policy import PolicyEPS = 1e-8@POLICY_REGISTRY.register('acer')class ACERPolicy(Policy):    r"""    Overview:        Policy class of ACER algorithm.    Config:        == ======================= ======== ============== ===================================== =======================        ID Symbol                  Type     Default Value  Description                           Other(Shape)        == ======================= ======== ============== ===================================== =======================        1  ``type``                str      acer           | RL policy register name, refer to   | this arg is optional,                                                           | registry ``POLICY_REGISTRY``        | a placeholder        2  ``cuda``                bool     False          | Whether to use cuda for network     | this arg can be diff-                                                           |                                     | erent from modes        3  ``on_policy``           bool     False          | Whether the RL algorithm is                                                           | on-policy or off-policy        4  ``trust_region``        bool     True           | Whether the RL algorithm use trust  |                                                           | region constraint                   |        5  ``trust_region_value``  float    1.0            | maximum range of the trust region   |        6  ``unroll_len``          int      32             | trajectory length to calculate                                                           | Q retrace target        7   ``learn.update``       int      4              | How many updates(iterations) to     | this args can be vary            ``per_collect``                                | train after collector's one         | from envs. Bigger val                                                           |  collection. Only                   |                                                           | valid in serial training            | means more off-policy        8   ``c_clip_ratio``       float    1.0            | clip ratio of importance weights    |        == ======================= ======== ============== ===================================== =======================    """    unroll_len = 32    config = dict(        type='acer',        cuda=False,        # (bool) whether to use on-policy training pipeline (behaviour policy and training policy are the same)        # here we follow ppo serial pipeline, the original is False        on_policy=False,        priority=False,        # (bool) Whether to use Importance Sampling Weight to correct biased update. If True, priority must be True.        priority_IS_weight=False,        learn=dict(            # (str) the type of gradient clip method            grad_clip_type=None,            # (float) max value when ACER use gradient clip            clip_value=None,            # (int) collect n_sample data, train model update_per_collect times            # here we follow ppo serial pipeline            update_per_collect=4,            # (int) the number of data for a train iteration            batch_size=16,            # (float) loss weight of the value network, the weight of policy network is set to 1            value_weight=0.5,            # (float) loss weight of the entropy regularization, the weight of policy network is set to 1            entropy_weight=0.0001,            # (float) discount factor for future reward, defaults int [0, 1]            discount_factor=0.9,            # (float) additional discounting parameter            lambda_=0.95,            # (int) the trajectory length to calculate v-trace target            unroll_len=unroll_len,            # (float) clip ratio of importance weights            c_clip_ratio=10,            trust_region=True,            trust_region_value=1.0,            learning_rate_actor=0.0005,            learning_rate_critic=0.0005,            target_theta=0.01        ),        collect=dict(            # (int) collect n_sample data, train model n_iteration times            # n_sample=16,            # (int) the trajectory length to calculate v-trace target            unroll_len=unroll_len,            # (float) discount factor for future reward, defaults int [0, 1]            discount_factor=0.9,            gae_lambda=0.95,            collector=dict(                type='sample',                collect_print_freq=1000,            ),        ),        eval=dict(evaluator=dict(eval_freq=200, ), ),        other=dict(replay_buffer=dict(            replay_buffer_size=1000,            max_use=16,        ), ),    )    def default_model(self) -> Tuple[str, List[str]]:        return 'acer', ['ding.model.template.acer']    def _init_learn(self) -> None:        r"""        Overview:            Learn mode init method. Called by ``self.__init__``.            Initialize the optimizer, algorithm config and main model.        """        # Optimizer        self._optimizer_actor = Adam(            self._model.actor.parameters(),            lr=self._cfg.learn.learning_rate_actor,            grad_clip_type=self._cfg.learn.grad_clip_type,            clip_value=self._cfg.learn.clip_value        )        self._optimizer_critic = Adam(            self._model.critic.parameters(),            lr=self._cfg.learn.learning_rate_critic,        )        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._action_shape = self._cfg.model.action_shape        self._unroll_len = self._cfg.learn.unroll_len        # Algorithm config        self._priority = self._cfg.priority        self._priority_IS_weight = self._cfg.priority_IS_weight        self._value_weight = self._cfg.learn.value_weight        self._entropy_weight = self._cfg.learn.entropy_weight        self._gamma = self._cfg.learn.discount_factor        # self._rho_clip_ratio = self._cfg.learn.rho_clip_ratio        self._c_clip_ratio = self._cfg.learn.c_clip_ratio        # self._rho_pg_clip_ratio = self._cfg.learn.rho_pg_clip_ratio        self._use_trust_region = self._cfg.learn.trust_region        self._trust_region_value = self._cfg.learn.trust_region_value        # Main model        self._learn_model.reset()        self._target_model.reset()    def _data_preprocess_learn(self, data: List[Dict[str, Any]]):        """        Overview:            Data preprocess function of learn mode.            Convert list trajectory data to to trajectory data, which is a dict of tensors.        Arguments:            - data (:obj:`List[Dict[str, Any]]`): List type data, a list of data for training. Each list element is a \            dict, whose values are torch.Tensor or np.ndarray or dict/list combinations, keys include at least 'obs',\             'next_obs', 'logit', 'action', 'reward', 'done'        Returns:            - data (:obj:`dict`): Dict type data. Values are torch.Tensor or np.ndarray or dict/list combinations. \        ReturnsKeys:            - necessary: 'logit', 'action', 'reward', 'done', 'weight', 'obs_plus_1'.            - optional and not used in later computation: 'obs', 'next_obs'.'IS', 'collect_iter', 'replay_unique_id', \                'replay_buffer_idx', 'priority', 'staleness', 'use'.        ReturnsShapes:            - obs_plus_1 (:obj:`torch.FloatTensor`): :math:`(T * B, obs_shape)`, where T is timestep, B is batch size \                and obs_shape is the shape of single env observation            - logit (:obj:`torch.FloatTensor`): :math:`(T, B, N)`, where N is action dim            - action (:obj:`torch.LongTensor`): :math:`(T, B)`            - reward (:obj:`torch.FloatTensor`): :math:`(T+1, B)`            - done (:obj:`torch.FloatTensor`): :math:`(T, B)`            - weight (:obj:`torch.FloatTensor`): :math:`(T, B)`        """        data = default_collate(data)        if self._cuda:            data = to_device(data, self._device)        data['weight'] = data.get('weight', None)        # shape (T+1)*B,env_obs_shape        data['obs_plus_1'] = torch.cat((data['obs'] + data['next_obs'][-1:]), dim=0)        data['logit'] = torch.cat(            data['logit'], dim=0        ).reshape(self._unroll_len, -1, self._action_shape)  # shape T,B,env_action_shape        data['action'] = torch.cat(data['action'], dim=0).reshape(self._unroll_len, -1)  # shape T,B,        data['done'] = torch.cat(data['done'], dim=0).reshape(self._unroll_len, -1).float()  # shape T,B,        data['reward'] = torch.cat(data['reward'], dim=0).reshape(self._unroll_len, -1)  # shape T,B,        data['weight'] = torch.cat(            data['weight'], dim=0        ).reshape(self._unroll_len, -1) if data['weight'] else None  # shape T,B        return data    def _forward_learn(self, data: List[Dict[str, Any]]) -> Dict[str, Any]:        r"""        Overview:            Forward computation graph of learn mode(updating policy).        Arguments:            - data (:obj:`List[Dict[str, Any]]`): List type data, a list of data for training. Each list element is a \            dict, whose values are torch.Tensor or np.ndarray or dict/list combinations, keys include at least 'obs',\             'next_obs', 'logit', 'action', 'reward', 'done'        Returns:            - info_dict (:obj:`Dict[str, Any]`): Dict type data, a info dict indicated training result, which will be \                recorded in text log and tensorboard, values are python scalar or a list of scalars.        ArgumentsKeys:            - necessary: ``obs``, ``action``, ``reward``, ``next_obs``, ``done``            - optional: 'collect_iter', 'replay_unique_id', 'replay_buffer_idx', 'priority', 'staleness', 'use', 'IS'        ReturnsKeys:            - necessary: ``cur_lr_actor``, ``cur_lr_critic``, ``actor_loss`,``bc_loss``,``policy_loss``,\                ``critic_loss``,``entropy_loss``        """        data = self._data_preprocess_learn(data)        self._learn_model.train()        action_data = self._learn_model.forward(data['obs_plus_1'], mode='compute_actor')        q_value_data = self._learn_model.forward(data['obs_plus_1'], mode='compute_critic')        avg_action_data = self._target_model.forward(data['obs_plus_1'], mode='compute_actor')        target_logit, behaviour_logit, avg_logit, actions, q_values, rewards, weights = self._reshape_data(            action_data, avg_action_data, q_value_data, data        )        # shape (T+1),B,env_action_shape        target_logit = torch.log_softmax(target_logit, dim=-1)        # shape T,B,env_action_shape        behaviour_logit = torch.log_softmax(behaviour_logit, dim=-1)        # shape (T+1),B,env_action_shape        avg_logit = torch.log_softmax(avg_logit, dim=-1)        with torch.no_grad():            # shape T,B,env_action_shape            ratio = torch.exp(target_logit[0:-1] - behaviour_logit)            # shape (T+1),B,1            v_pred = (q_values * torch.exp(target_logit)).sum(-1).unsqueeze(-1)            # Calculate retrace            q_retraces = compute_q_retraces(q_values, v_pred, rewards, actions, weights, ratio, self._gamma)        # the terminal states' weights are 0. it needs to be shift to count valid state        weights_ext = torch.ones_like(weights)        weights_ext[1:] = weights[0:-1]        weights = weights_ext        q_retraces = q_retraces[0:-1]  # shape T,B,1        q_values = q_values[0:-1]  # shape T,B,env_action_shape        v_pred = v_pred[0:-1]  # shape T,B,1        target_logit = target_logit[0:-1]  # shape T,B,env_action_shape        avg_logit = avg_logit[0:-1]  # shape T,B,env_action_shape        total_valid = weights.sum()  # 1        # ====================        # policy update        # ====================        actor_loss, bc_loss = acer_policy_error(            q_values, q_retraces, v_pred, target_logit, actions, ratio, self._c_clip_ratio        )        actor_loss = actor_loss * weights.unsqueeze(-1)        bc_loss = bc_loss * weights.unsqueeze(-1)        dist_new = torch.distributions.categorical.Categorical(logits=target_logit)        entropy_loss = (dist_new.entropy() * weights).unsqueeze(-1)  # shape T,B,1        total_actor_loss = (actor_loss + bc_loss + self._entropy_weight * entropy_loss).sum() / total_valid        self._optimizer_actor.zero_grad()        actor_gradients = torch.autograd.grad(-total_actor_loss, target_logit, retain_graph=True)        if self._use_trust_region:            actor_gradients = acer_trust_region_update(                actor_gradients, target_logit, avg_logit, self._trust_region_value            )        target_logit.backward(actor_gradients)        self._optimizer_actor.step()        # ====================        # critic update        # ====================        critic_loss = (acer_value_error(q_values, q_retraces, actions) * weights.unsqueeze(-1)).sum() / total_valid        self._optimizer_critic.zero_grad()        critic_loss.backward()        self._optimizer_critic.step()        self._target_model.update(self._learn_model.state_dict())        with torch.no_grad():            kl_div = torch.exp(avg_logit) * (avg_logit - target_logit)            kl_div = (kl_div.sum(-1) * weights).sum() / total_valid        return {            'cur_actor_lr': self._optimizer_actor.defaults['lr'],            'cur_critic_lr': self._optimizer_critic.defaults['lr'],            'actor_loss': (actor_loss.sum() / total_valid).item(),            'bc_loss': (bc_loss.sum() / total_valid).item(),            'policy_loss': total_actor_loss.item(),            'critic_loss': critic_loss.item(),            'entropy_loss': (entropy_loss.sum() / total_valid).item(),            'kl_div': kl_div.item()        }    def _reshape_data(            self, action_data: Dict[str, Any], avg_action_data: Dict[str, Any], q_value_data: Dict[str, Any],            data: Dict[str, Any]    ) -> Tuple[Any, Any, Any, Any, Any, Any]:        r"""        Overview:            Obtain weights for loss calculating, where should be 0 for done positions            Update values and rewards with the weight        Arguments:            - output (:obj:`Dict[int, Any]`): Dict type data, output of learn_model forward. \             Values are torch.Tensor or np.ndarray or dict/list combinations, keys are value, logit.            - data (:obj:`Dict[int, Any]`): Dict type data, input of policy._forward_learn \             Values are torch.Tensor or np.ndarray or dict/list combinations. Keys includes at \             least ['logit', 'action', 'reward', 'done',]        Returns:            - data (:obj:`Tuple[Any]`): Tuple of target_logit, behaviour_logit, actions, \             values, rewards, weights        ReturnsShapes:            - target_logit (:obj:`torch.FloatTensor`): :math:`((T+1), B, Obs_Shape)`, where T is timestep,\             B is batch size and Obs_Shape is the shape of single env observation.            - behaviour_logit (:obj:`torch.FloatTensor`): :math:`(T, B, N)`, where N is action dim.            - avg_action_logit (:obj:`torch.FloatTensor`): :math: `(T+1, B, N)`, where N is action dim.            - actions (:obj:`torch.LongTensor`): :math:`(T, B)`            - values (:obj:`torch.FloatTensor`): :math:`(T+1, B)`            - rewards (:obj:`torch.FloatTensor`): :math:`(T, B)`            - weights (:obj:`torch.FloatTensor`): :math:`(T, B)`        """        target_logit = action_data['logit'].reshape(            self._unroll_len + 1, -1, self._action_shape        )  # shape (T+1),B,env_action_shape        behaviour_logit = data['logit']  # shape T,B,env_action_shape        avg_action_logit = avg_action_data['logit'].reshape(            self._unroll_len + 1, -1, self._action_shape        )  # shape (T+1),B,env_action_shape        actions = data['action']  # shape T,B        values = q_value_data['q_value'].reshape(            self._unroll_len + 1, -1, self._action_shape        )  # shape (T+1),B,env_action_shape        rewards = data['reward']  # shape T,B        weights_ = 1 - data['done']  # shape T,B        weights = torch.ones_like(rewards)  # shape T,B        weights = weights_        return target_logit, behaviour_logit, avg_action_logit, actions, values, rewards, weights    def _state_dict_learn(self) -> Dict[str, Any]:        r"""        Overview:            Return the state_dict of learn mode, usually including model and optimizer.        Returns:            - state_dict (:obj:`Dict[str, Any]`): the dict of current policy learn state, for saving and restoring.        """        return {            'model': self._learn_model.state_dict(),            'target_model': self._target_model.state_dict(),            'actor_optimizer': self._optimizer_actor.state_dict(),            'critic_optimizer': self._optimizer_critic.state_dict(),        }    def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None:        r"""        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['actor_optimizer'])        self._optimizer_critic.load_state_dict(state_dict['critic_optimizer'])    def _init_collect(self) -> None:        r"""        Overview:            Collect mode init method. Called by ``self.__init__``, initialize algorithm arguments and collect_model.            Use multinomial_sample to choose action.        """        self._collect_unroll_len = self._cfg.collect.unroll_len        self._collect_model = model_wrap(self._model, wrapper_name='multinomial_sample')        self._collect_model.reset()    def _forward_collect(self, data: Dict[int, Any]) -> Dict[int, Dict[str, Any]]:        r"""        Overview:            Forward computation graph of collect mode(collect training data).        Arguments:            - data (:obj:`Dict[int, Any]`): Dict type data, stacked env data for predicting \            action, values are torch.Tensor or np.ndarray or dict/list combinations,keys \            are env_id indicated by integer.        Returns:            - output (:obj:`Dict[int, Dict[str, Any]]`): Dict of predicting policy_output(logit, action) for each env.        ReturnsKeys            - necessary: ``logit``, ``action``        """        data_id = list(data.keys())        data = default_collate(list(data.values()))        if self._cuda:            data = to_device(data, self._device)        self._collect_model.eval()        with torch.no_grad():            output = self._collect_model.forward(data, mode='compute_actor')        if self._cuda:            output = to_device(output, 'cpu')        output = default_decollate(output)        output = {i: d for i, d in zip(data_id, output)}        return output    def _get_train_sample(self, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:        r"""        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.        Arguments:            - data (:obj:`List[Dict[str, Any]`): The trajectory data(a list of transition), each element is the same \                format as the return value of ``self._process_transition`` method.        Returns:            - samples (:obj:`dict`): List of training samples.        .. note::            We will vectorize ``process_transition`` and ``get_train_sample`` method in the following release version. \            And the user can customize the this data processing procedure by overriding this two methods and collector \            itself.        """        return get_train_sample(data, self._unroll_len)    def _process_transition(self, obs: Any, policy_output: Dict[str, Any], timestep: namedtuple) -> Dict[str, Any]:        r"""        Overview:               Generate dict type transition data from inputs.        Arguments:                - obs (:obj:`Any`): Env observation,can be torch.Tensor or np.ndarray or dict/list combinations.                - model_output (:obj:`dict`): Output of collect model, including ['logit','action']                - timestep (:obj:`namedtuple`): Output after env step, including at least ['obs', 'reward', 'done']\                       (here 'obs' indicates obs after env step).        Returns:               - transition (:obj:`dict`): Dict type transition data, including at least ['obs','next_obs', 'logit',\               'action','reward', 'done']        """        transition = {            'obs': obs,            'next_obs': timestep.obs,            'logit': policy_output['logit'],            'action': policy_output['action'],            'reward': timestep.reward,            'done': timestep.done,        }        return transition    def _init_eval(self) -> None:        r"""        Overview:            Evaluate mode init method. Called by ``self.__init__``, initialize eval_model,            and use argmax_sample to choose action.        """        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]:        r"""        Overview:            Forward computation graph of eval mode(evaluate policy performance), at most cases, it is similar to \            ``self._forward_collect``.        Arguments:            - data (:obj:`Dict[str, Any]`): Dict type data, stacked env data for predicting policy_output(action), \                values are torch.Tensor or np.ndarray or dict/list combinations, keys are env_id indicated by integer.        Returns:            - output (:obj:`Dict[int, Any]`): The dict of predicting action for the interaction with env.        ReturnsKeys            - necessary: ``action``            - optional: ``logit``        """        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)        output = {i: d for i, d in zip(data_id, output)}        return output    def _monitor_vars_learn(self) -> List[str]:        r"""        Overview:            Return this algorithm default model setting for demonstration.        Returns:            - model_info (:obj:`Tuple[str, List[str]]`): model name and mode import_names        .. note::            The user can define and use a customized network model but must obey the same interface definition \            indicated by import_names path. For IMPALA, ``ding.model.interface.IMPALA``        """        return ['actor_loss', 'bc_loss', 'policy_loss', 'critic_loss', 'entropy_loss', 'kl_div']
ding.policy.acer¶

ding.policy.acer ¶

ACERPolicy ¶

Full Source Code

`ding.policy.acer`¶

`ding.policy.acer` ¶

`ACERPolicy` ¶
