`ding.policy.madqn`¶

`ding.policy.madqn` ¶

`MADQNPolicy` ¶

Bases: QMIXPolicy
`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
Full Source Code

../ding/policy/madqn.py
from typing import List, Dict, Any, Tuple, Union, Optionalfrom collections import namedtupleimport torchimport copyfrom ding.torch_utils import RMSprop, to_devicefrom ding.rl_utils import v_1step_td_data, v_1step_td_error, get_train_sample, \    v_nstep_td_data, v_nstep_td_error, get_nstep_return_datafrom ding.model import model_wrapfrom ding.utils import POLICY_REGISTRYfrom ding.utils.data import timestep_collate, default_collate, default_decollatefrom .qmix import QMIXPolicy@POLICY_REGISTRY.register('madqn')class MADQNPolicy(QMIXPolicy):    config = dict(        # (str) RL policy register name (refer to function "POLICY_REGISTRY").        type='madqn',        # (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,        nstep=3,        learn=dict(            update_per_collect=20,            batch_size=32,            learning_rate=0.0005,            clip_value=100,            # ==============================================================            # 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,            # (bool) Whether to use double DQN mechanism(target q for surpassing over estimation)            double_q=False,            weight_decay=1e-5,        ),        collect=dict(            # (int) Only one of [n_sample, n_episode] shoule be set            n_episode=32,            # (int) Cut trajectories into pieces with length "unroll_len", the length of timesteps            # in each forward when training. In qmix, 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        """        return 'madqn', ['ding.model.template.madqn']    def _init_learn(self) -> None:        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 QMIX"        self._optimizer_current = RMSprop(            params=self._model.current.parameters(),            lr=self._cfg.learn.learning_rate,            alpha=0.99,            eps=0.00001,            weight_decay=self._cfg.learn.weight_decay        )        self._optimizer_cooperation = RMSprop(            params=self._model.cooperation.parameters(),            lr=self._cfg.learn.learning_rate,            alpha=0.99,            eps=0.00001,            weight_decay=self._cfg.learn.weight_decay        )        self._gamma = self._cfg.learn.discount_factor        self._nstep = self._cfg.nstep        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']}        total_q = self._learn_model.forward(inputs, single_step=False)['total_q']        if self._cfg.learn.double_q:            next_inputs = {'obs': data['next_obs']}            self._learn_model.reset(state=data['prev_state'][1])            logit_detach = self._learn_model.forward(next_inputs, single_step=False)['logit'].clone().detach()            next_inputs = {'obs': data['next_obs'], 'action': logit_detach.argmax(dim=-1)}        else:            next_inputs = {'obs': data['next_obs']}        with torch.no_grad():            target_total_q = self._target_model.forward(next_inputs, cooperation=True, single_step=False)['total_q']        if self._nstep == 1:            v_data = v_1step_td_data(total_q, target_total_q, data['reward'], data['done'], data['weight'])            loss, td_error_per_sample = v_1step_td_error(v_data, self._gamma)            # for visualization            with torch.no_grad():                if data['done'] is not None:                    target_v = self._gamma * (1 - data['done']) * target_total_q + data['reward']                else:                    target_v = self._gamma * target_total_q + data['reward']        else:            data['reward'] = data['reward'].permute(0, 2, 1).contiguous()            loss = []            td_error_per_sample = []            for t in range(self._cfg.collect.unroll_len):                v_data = v_nstep_td_data(                    total_q[t], target_total_q[t], data['reward'][t], data['done'][t], data['weight'], None                )                # calculate v_nstep_td critic_loss                loss_i, td_error_per_sample_i = v_nstep_td_error(v_data, self._gamma, self._nstep)                loss.append(loss_i)                td_error_per_sample.append(td_error_per_sample_i)            loss = sum(loss) / (len(loss) + 1e-8)            td_error_per_sample = sum(td_error_per_sample) / (len(td_error_per_sample) + 1e-8)        self._optimizer_current.zero_grad()        loss.backward()        grad_norm = torch.nn.utils.clip_grad_norm_(self._model.current.parameters(), self._cfg.learn.clip_value)        self._optimizer_current.step()        # cooperation        self._learn_model.reset(state=data['prev_state'][0])        self._target_model.reset(state=data['prev_state'][0])        cooperation_total_q = self._learn_model.forward(inputs, cooperation=True, single_step=False)['total_q']        next_inputs = {'obs': data['next_obs']}        with torch.no_grad():            cooperation_target_total_q = self._target_model.forward(                next_inputs, cooperation=True, single_step=False            )['total_q']        if self._nstep == 1:            v_data = v_1step_td_data(                cooperation_total_q, cooperation_target_total_q, data['reward'], data['done'], data['weight']            )            cooperation_loss, _ = v_1step_td_error(v_data, self._gamma)        else:            cooperation_loss_all = []            for t in range(self._cfg.collect.unroll_len):                v_data = v_nstep_td_data(                    cooperation_total_q[t],                    cooperation_target_total_q[t],                    data['reward'][t],                    data['done'][t],                    data['weight'],                    None,                )                cooperation_loss, _ = v_nstep_td_error(v_data, self._gamma, self._nstep)                cooperation_loss_all.append(cooperation_loss)            cooperation_loss = sum(cooperation_loss_all) / (len(cooperation_loss_all) + 1e-8)        self._optimizer_cooperation.zero_grad()        cooperation_loss.backward()        cooperation_grad_norm = torch.nn.utils.clip_grad_norm_(            self._model.cooperation.parameters(), self._cfg.learn.clip_value        )        self._optimizer_cooperation.step()        # =============        # after update        # =============        self._target_model.update(self._learn_model.state_dict())        return {            'cur_lr': self._optimizer_current.defaults['lr'],            'total_loss': loss.item(),            'total_q': total_q.mean().item() / self._cfg.model.agent_num,            'target_total_q': target_total_q.mean().item() / self._cfg.model.agent_num,            'grad_norm': grad_norm,            'cooperation_grad_norm': cooperation_grad_norm,            'cooperation_loss': cooperation_loss.item(),        }    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_current': self._optimizer_current.state_dict(),            'optimizer_cooperation': self._optimizer_cooperation.state_dict(),        }    def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None:        """        Overview:            Load the state_dict variable into policy learn mode.        Arguments:            - state_dict (:obj:`Dict[str, Any]`): the dict of policy learn state saved before.        .. tip::            If you want to only load some parts of model, you can simply set the ``strict`` argument in \            load_state_dict to ``False``, or refer to ``ding.torch_utils.checkpoint_helper`` for more \            complicated operation.        """        self._learn_model.load_state_dict(state_dict['model'])        self._target_model.load_state_dict(state_dict['target_model'])        self._optimizer_current.load_state_dict(state_dict['optimizer_current'])        self._optimizer_cooperation.load_state_dict(state_dict['optimizer_cooperation'])    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 _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        """        if self._cfg.nstep == 1:            return get_train_sample(data, self._unroll_len)        else:            data = get_nstep_return_data(data, self._nstep, gamma=self._gamma)            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', 'total_q', 'target_total_q', 'grad_norm', 'target_reward_total_q',            'cooperation_grad_norm', 'cooperation_loss'        ]
ding.policy.madqn¶

ding.policy.madqn ¶

MADQNPolicy ¶

default_model() ¶

Full Source Code

`ding.policy.madqn`¶

`ding.policy.madqn` ¶

`MADQNPolicy` ¶

`default_model()` ¶
