`ding.policy.qtran`¶

`ding.policy.qtran` ¶

`QTRANPolicy` ¶

Bases: Policy
Overview
Policy class of QTRAN algorithm. QTRAN is a multi model reinforcement learning algorithm, you can view the paper in the following link https://arxiv.org/abs/1803.11485
`default_model()` ¶

Overview
Return this algorithm default model setting for demonstration.
Returns: - model_info (:obj:Tuple[str, List[str]]): model name and mode import_names .. note:: The user can define and use customized network model but must obey the same inferface definition indicated by import_names path. For QTRAN, ding.model.qtran.qtran
Full Source Code

../ding/policy/qtran.py
from typing import List, Dict, Any, Tuple, Union, Optionalfrom collections import namedtupleimport torchimport torch.nn.functional as Fimport copyfrom easydict import EasyDictfrom ding.torch_utils import Adam, RMSprop, to_devicefrom ding.rl_utils import v_1step_td_data, v_1step_td_error, get_epsilon_greedy_fn, get_train_samplefrom ding.model import model_wrapfrom ding.utils import POLICY_REGISTRYfrom ding.utils.data import timestep_collate, default_collate, default_decollatefrom .base_policy import Policy@POLICY_REGISTRY.register('qtran')class QTRANPolicy(Policy):    """    Overview:        Policy class of QTRAN algorithm. QTRAN is a multi model reinforcement learning algorithm, \        you can view the paper in the following link https://arxiv.org/abs/1803.11485    Config:        == ==================== ======== ============== ======================================== =======================        ID Symbol               Type     Default Value  Description                              Other(Shape)        == ==================== ======== ============== ======================================== =======================        1  ``type``             str      qtran          | RL policy register name, refer to      | this arg is optional,                                                        | registry ``POLICY_REGISTRY``           | a placeholder        2  ``cuda``             bool     True           | Whether to use cuda for network        | this arg can be diff-                                                                                                 | erent from modes        3  ``on_policy``        bool     False          | Whether the RL algorithm is on-policy                                                        | or off-policy        4. ``priority``         bool     False          | Whether use priority(PER)              | priority sample,                                                                                                 | update priority        5  | ``priority_``      bool     False          | Whether use Importance Sampling        | IS weight           | ``IS_weight``                              | Weight to correct biased update.        6  | ``learn.update_``  int      20             | How many updates(iterations) to train  | this args can be vary           | ``per_collect``                            | after collector's one collection. Only | from envs. Bigger val                                                        | valid in serial training               | means more off-policy        7  | ``learn.target_``  float    0.001          | Target network update momentum         | between[0,1]           | ``update_theta``                           | parameter.        8  | ``learn.discount`` float    0.99           | Reward's future discount factor, aka.  | may be 1 when sparse           | ``_factor``                                | gamma                                  | reward env        == ==================== ======== ============== ======================================== =======================    """    config = dict(        # (str) RL policy register name (refer to function "POLICY_REGISTRY").        type='qtran',        # (bool) Whether to use cuda for network.        cuda=True,        # (bool) Whether the RL algorithm is on-policy or off-policy.        on_policy=False,        # (bool) Whether use priority(priority sample, IS weight, update priority)        priority=False,        # (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True.        priority_IS_weight=False,        learn=dict(            update_per_collect=20,            batch_size=32,            learning_rate=0.0005,            clip_value=1.5,            # ==============================================================            # The following configs is algorithm-specific            # ==============================================================            # (float) Target network update momentum parameter.            # in [0, 1].            target_update_theta=0.008,            # (float) The discount factor for future rewards,            # in [0, 1].            discount_factor=0.99,            # (float) the loss weight of TD-error            td_weight=1,            # (float) the loss weight of Opt Loss            opt_weight=0.01,            # (float) the loss weight of Nopt Loss            nopt_min_weight=0.0001,            # (bool) Whether to use double DQN mechanism(target q for surpassing over estimation)            double_q=True,        ),        collect=dict(            # (int) Only one of [n_sample, n_episode] shoule be set            # n_sample=32 * 16,            # (int) Cut trajectories into pieces with length "unroll_len", the length of timesteps            # in each forward when training. In qtran, it is greater than 1 because there is RNN.            unroll_len=10,        ),        eval=dict(),        other=dict(            eps=dict(                # (str) Type of epsilon decay                type='exp',                # (float) Start value for epsilon decay, in [0, 1].                # 0 means not use epsilon decay.                start=1,                # (float) Start value for epsilon decay, in [0, 1].                end=0.05,                # (int) Decay length(env step)                decay=50000,            ),            replay_buffer=dict(                replay_buffer_size=5000,                # (int) The maximum reuse times of each data                max_reuse=1e+9,                max_staleness=1e+9,            ),        ),    )    def default_model(self) -> Tuple[str, List[str]]:        """        Overview:            Return this algorithm default model setting for demonstration.        Returns:            - model_info (:obj:`Tuple[str, List[str]]`): model name and mode import_names        .. note::            The user can define and use customized network model but must obey the same inferface definition indicated \            by import_names path. For QTRAN, ``ding.model.qtran.qtran``        """        return 'qtran', ['ding.model.template.qtran']    def _init_learn(self) -> None:        """        Overview:            Learn mode init method. Called by ``self.__init__``.            Init the learner model of QTRANPolicy        Arguments:            .. note::                The _init_learn method takes the argument from the self._cfg.learn in the config file            - learning_rate (:obj:`float`): The learning rate fo the optimizer            - gamma (:obj:`float`): The discount factor            - agent_num (:obj:`int`): This is a multi-agent algorithm, we need to input agent num.            - batch_size (:obj:`int`): Need batch size info to init hidden_state plugins        """        self._priority = self._cfg.priority        self._priority_IS_weight = self._cfg.priority_IS_weight        assert not self._priority and not self._priority_IS_weight, "Priority is not implemented in QTRAN"        self._optimizer = RMSprop(            params=self._model.parameters(), lr=self._cfg.learn.learning_rate, alpha=0.99, eps=0.00001        )        self._gamma = self._cfg.learn.discount_factor        self._td_weight = self._cfg.learn.td_weight        self._opt_weight = self._cfg.learn.opt_weight        self._nopt_min_weight = self._cfg.learn.nopt_min_weight        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_update_theta}        )        self._target_model = model_wrap(            self._target_model,            wrapper_name='hidden_state',            state_num=self._cfg.learn.batch_size,            init_fn=lambda: [None for _ in range(self._cfg.model.agent_num)]        )        self._learn_model = model_wrap(            self._model,            wrapper_name='hidden_state',            state_num=self._cfg.learn.batch_size,            init_fn=lambda: [None for _ in range(self._cfg.model.agent_num)]        )        self._learn_model.reset()        self._target_model.reset()    def _data_preprocess_learn(self, data: List[Any]) -> dict:        r"""        Overview:            Preprocess the data to fit the required data format for learning        Arguments:            - data (:obj:`List[Dict[str, Any]]`): the data collected from collect function        Returns:            - data (:obj:`Dict[str, Any]`): the processed data, from \                [len=B, ele={dict_key: [len=T, ele=Tensor(any_dims)]}] -> {dict_key: Tensor([T, B, any_dims])}        """        # data preprocess        data = timestep_collate(data)        if self._cuda:            data = to_device(data, self._device)        data['weight'] = data.get('weight', None)        data['done'] = data['done'].float()        return data    def _forward_learn(self, data: dict) -> Dict[str, Any]:        r"""        Overview:            Forward and backward function of learn mode.        Arguments:            - data (:obj:`Dict[str, Any]`): Dict type data, a batch of data for training, values are torch.Tensor or \                np.ndarray or dict/list combinations.        Returns:            - info_dict (:obj:`Dict[str, Any]`): Dict type data, a info dict indicated training result, which will be \                recorded in text log and tensorboard, values are python scalar or a list of scalars.        ArgumentsKeys:            - necessary: ``obs``, ``next_obs``, ``action``, ``reward``, ``weight``, ``prev_state``, ``done``        ReturnsKeys:            - necessary: ``cur_lr``, ``total_loss``                - cur_lr (:obj:`float`): Current learning rate                - total_loss (:obj:`float`): The calculated loss        """        data = self._data_preprocess_learn(data)        # ====================        # Q-mix forward        # ====================        self._learn_model.train()        self._target_model.train()        # for hidden_state plugin, we need to reset the main model and target model        self._learn_model.reset(state=data['prev_state'][0])        self._target_model.reset(state=data['prev_state'][0])        inputs = {'obs': data['obs'], 'action': data['action']}        learn_ret = self._learn_model.forward(inputs, single_step=False)        total_q = learn_ret['total_q']        vs = learn_ret['vs']        agent_q_act = learn_ret['agent_q_act']        logit_detach = learn_ret['logit'].clone()        logit_detach[data['obs']['action_mask'] == 0.0] = -9999999        logit_q, logit_action = logit_detach.max(dim=-1, keepdim=False)        if self._cfg.learn.double_q:            next_inputs = {'obs': data['next_obs']}            double_q_detach = self._learn_model.forward(next_inputs, single_step=False)['logit'].clone().detach()            _, double_q_action = double_q_detach.max(dim=-1, keepdim=False)            next_inputs = {'obs': data['next_obs'], 'action': double_q_action}        else:            next_inputs = {'obs': data['next_obs']}        with torch.no_grad():            target_total_q = self._target_model.forward(next_inputs, single_step=False)['total_q']        # -- TD Loss --        td_data = v_1step_td_data(total_q, target_total_q.detach(), data['reward'], data['done'], data['weight'])        td_loss, td_error_per_sample = v_1step_td_error(td_data, self._gamma)        # -- TD Loss --        # -- Opt Loss --        if data['weight'] is None:            weight = torch.ones_like(data['reward'])        opt_inputs = {'obs': data['obs'], 'action': logit_action}        max_q = self._learn_model.forward(opt_inputs, single_step=False)['total_q']        opt_error = logit_q.sum(dim=2) - max_q.detach() + vs        opt_loss = (opt_error ** 2 * weight).mean()        # -- Opt Loss --        # -- Nopt Loss --        nopt_values = agent_q_act.sum(dim=2) - total_q.detach() + vs        nopt_error = nopt_values.clamp(max=0)        nopt_min_loss = (nopt_error ** 2 * weight).mean()        # -- Nopt Loss --        total_loss = self._td_weight * td_loss + self._opt_weight * opt_loss + self._nopt_min_weight * nopt_min_loss        # ====================        # Q-mix update        # ====================        self._optimizer.zero_grad()        total_loss.backward()        # just get grad_norm        grad_norm = torch.nn.utils.clip_grad_norm_(self._model.parameters(), 10000000)        self._optimizer.step()        # =============        # after update        # =============        self._target_model.update(self._learn_model.state_dict())        return {            'cur_lr': self._optimizer.defaults['lr'],            'total_loss': total_loss.item(),            'td_loss': td_loss.item(),            'opt_loss': opt_loss.item(),            'nopt_loss': nopt_min_loss.item(),            'grad_norm': grad_norm,        }    def _reset_learn(self, data_id: Optional[List[int]] = None) -> None:        r"""        Overview:            Reset learn model to the state indicated by data_id        Arguments:            - data_id (:obj:`Optional[List[int]]`): The id that store the state and we will reset\                the model state to the state indicated by data_id        """        self._learn_model.reset(data_id=data_id)    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(),            'optimizer': self._optimizer.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.load_state_dict(state_dict['optimizer'])    def _init_collect(self) -> None:        r"""        Overview:            Collect mode init method. Called by ``self.__init__``.            Init traj and unroll length, collect model.            Enable the eps_greedy_sample and the hidden_state plugin.        """        self._unroll_len = self._cfg.collect.unroll_len        self._collect_model = model_wrap(            self._model,            wrapper_name='hidden_state',            state_num=self._cfg.collect.env_num,            save_prev_state=True,            init_fn=lambda: [None for _ in range(self._cfg.model.agent_num)]        )        self._collect_model = model_wrap(self._collect_model, wrapper_name='eps_greedy_sample')        self._collect_model.reset()    def _forward_collect(self, data: dict, eps: float) -> dict:        r"""        Overview:            Forward function for collect mode with eps_greedy        Arguments:            - data (:obj:`Dict[str, Any]`): Dict type data, stacked env data for predicting policy_output(action), \                values are torch.Tensor or np.ndarray or dict/list combinations, keys are env_id indicated by integer.            - eps (:obj:`float`): epsilon value for exploration, which is decayed by collected env step.        Returns:            - output (:obj:`Dict[int, Any]`): Dict type data, including at least inferred action according to input obs.        ReturnsKeys            - necessary: ``action``        """        data_id = list(data.keys())        data = default_collate(list(data.values()))        if self._cuda:            data = to_device(data, self._device)        data = {'obs': data}        self._collect_model.eval()        with torch.no_grad():            output = self._collect_model.forward(data, eps=eps, data_id=data_id)        if self._cuda:            output = to_device(output, 'cpu')        output = default_decollate(output)        return {i: d for i, d in zip(data_id, output)}    def _reset_collect(self, data_id: Optional[List[int]] = None) -> None:        r"""        Overview:            Reset collect model to the state indicated by data_id        Arguments:            - data_id (:obj:`Optional[List[int]]`): The id that store the state and we will reset\                the model state to the state indicated by data_id        """        self._collect_model.reset(data_id=data_id)    def _process_transition(self, obs: Any, model_output: dict, timestep: namedtuple) -> dict:        r"""        Overview:            Generate dict type transition data from inputs.        Arguments:            - obs (:obj:`Any`): Env observation            - model_output (:obj:`dict`): Output of collect model, including at least ['action', 'prev_state']            - 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 'obs', 'next_obs', 'prev_state',\                'action', 'reward', 'done'        """        transition = {            'obs': obs,            'next_obs': timestep.obs,            'prev_state': model_output['prev_state'],            'action': model_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__``.            Init eval model with argmax strategy and the hidden_state plugin.        """        self._eval_model = model_wrap(            self._model,            wrapper_name='hidden_state',            state_num=self._cfg.eval.env_num,            save_prev_state=True,            init_fn=lambda: [None for _ in range(self._cfg.model.agent_num)]        )        self._eval_model = model_wrap(self._eval_model, wrapper_name='argmax_sample')        self._eval_model.reset()    def _forward_eval(self, data: dict) -> dict:        r"""        Overview:            Forward function of eval mode, 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``        """        data_id = list(data.keys())        data = default_collate(list(data.values()))        if self._cuda:            data = to_device(data, self._device)        data = {'obs': data}        self._eval_model.eval()        with torch.no_grad():            output = self._eval_model.forward(data, data_id=data_id)        if self._cuda:            output = to_device(output, 'cpu')        output = default_decollate(output)        return {i: d for i, d in zip(data_id, output)}    def _reset_eval(self, data_id: Optional[List[int]] = None) -> None:        r"""        Overview:            Reset eval model to the state indicated by data_id        Arguments:            - data_id (:obj:`Optional[List[int]]`): The id that store the state and we will reset\                the model state to the state indicated by data_id        """        self._eval_model.reset(data_id=data_id)    def _get_train_sample(self, data: list) -> Union[None, List[Any]]:        r"""        Overview:            Get the train sample from trajectory.        Arguments:            - data (:obj:`list`): The trajectory's cache        Returns:            - samples (:obj:`dict`): The training samples generated        """        return get_train_sample(data, self._unroll_len)    def _monitor_vars_learn(self) -> List[str]:        r"""        Overview:            Return variables' name if variables are to used in monitor.        Returns:            - vars (:obj:`List[str]`): Variables' name list.        """        return ['cur_lr', 'total_loss', 'td_loss', 'opt_loss', 'nopt_loss', 'grad_norm']
ding.policy.qtran¶
