`ding.policy.collaq`¶

`ding.policy.collaq` ¶

`CollaQPolicy` ¶

Bases: Policy
Overview
Policy class of CollaQ algorithm. CollaQ is a multi-agent reinforcement learning algorithm
`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 collaq, ding.model.collaq.CollaQ .
Full Source Code

../ding/policy/collaq.py
from typing import List, Dict, Any, Tuple, Union, Optionalfrom collections import namedtupleimport torchimport copyfrom ding.torch_utils import to_device, RMSpropfrom ding.rl_utils import v_1step_td_data, v_1step_td_error, get_train_samplefrom ding.model import model_wrapfrom ding.utils import POLICY_REGISTRYfrom ding.utils.data import timestep_collate, default_collate, default_decollatefrom .base_policy import Policyfrom .common_utils import default_preprocess_learn@POLICY_REGISTRY.register('collaq')class CollaQPolicy(Policy):    r"""    Overview:        Policy class of CollaQ algorithm. CollaQ is a multi-agent reinforcement learning algorithm    Interface:        _init_learn, _data_preprocess_learn, _forward_learn, _reset_learn, _state_dict_learn, _load_state_dict_learn\            _init_collect, _forward_collect, _reset_collect, _process_transition, _init_eval, _forward_eval\            _reset_eval, _get_train_sample, default_model    Config:        == ==================== ======== ============== ======================================== =======================        ID Symbol               Type     Default Value  Description                              Other(Shape)        == ==================== ======== ============== ======================================== =======================        1  ``type``             str      collaq         | 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        9  | ``learn.collaq``   float    1.0            | The weight of collaq MARA loss           | ``_loss_weight``        == ==================== ======== ============== ======================================== =======================    """    config = dict(        # (str) RL policy register name (refer to function "POLICY_REGISTRY").        type='collaq',        # (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(            # (int) Collect n_episode data, update_model n_iteration times            update_per_collect=20,            # (int) The number of data for a train iteration            batch_size=32,            # (float) Gradient-descent step size            learning_rate=0.0005,            # ==============================================================            # The following configs is algorithm-specific            # ==============================================================            # (float) Target network update weight, theta * new_w + (1 - theta) * old_w, defaults in [0, 0.1]            target_update_theta=0.001,            # (float) Discount factor for future reward, defaults int [0, 1]            discount_factor=0.99,            # (float) The weight of collaq MARA loss            collaq_loss_weight=1.0,            # (float)            clip_value=100,            # (bool) Whether to use double DQN mechanism(target q for surpassing over estimation)            double_q=False,        ),        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=200000,            ),            replay_buffer=dict(                # (int) max size of replay buffer                replay_buffer_size=5000,                max_reuse=10,            ),        ),    )    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 collaq, ``ding.model.collaq.CollaQ`` .        """        return 'collaq', ['ding.model.template.collaq']    def _init_learn(self) -> None:        """        Overview:            Learn mode init method. Called by ``self.__init__``.            Init the learner model of CollaQPolicy        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            - alpha (:obj:`float`): The collaQ loss factor, the weight for calculating MARL loss            - agent_num (:obj:`int`): Since this is a multi-agent algorithm, we need to input the 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        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._alpha = self._cfg.learn.collaq_loss_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)] for _ in range(3)]        )        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)] for _ in range(3)]        )        self._learn_model.reset()        self._target_model.reset()    def _data_preprocess_learn(            self,            data: List[Any],            use_priority_IS_weight: bool = False,            use_priority: bool = False,    ) -> 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)        if use_priority_IS_weight:            assert use_priority, "Use IS Weight correction, but Priority is not used."        if use_priority and use_priority_IS_weight:            if 'priority_IS' in data:                data['weight'] = data['priority_IS']            else:  # for compability                data['weight'] = data['IS']        else:            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, self.cfg.priority_IS_weight, self.cfg.priority)        # ====================        # CollaQ 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']}        ret = self._learn_model.forward(inputs, single_step=False)        total_q = ret['total_q']        agent_colla_alone_q = ret['agent_colla_alone_q'].sum(-1).sum(-1)        if self._cfg.learn.double_q:            next_inputs = {'obs': data['next_obs']}            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, single_step=False)['total_q']        # td_loss calculation        td_data = v_1step_td_data(total_q, target_total_q, data['reward'], data['done'], data['weight'])        td_loss, td_error_per_sample = v_1step_td_error(td_data, self._gamma)        # collaQ loss calculation        colla_loss = (agent_colla_alone_q ** 2).mean()        # combine loss with factor        loss = colla_loss * self._alpha + td_loss        # ====================        # CollaQ update        # ====================        self._optimizer.zero_grad()        loss.backward()        grad_norm = torch.nn.utils.clip_grad_norm_(self._model.parameters(), self._cfg.learn.clip_value)        self._optimizer.step()        # =============        # after update        # =============        self._target_model.update(self._learn_model.state_dict())        return {            'cur_lr': self._optimizer.defaults['lr'],            'total_loss': loss.item(),            'colla_loss': colla_loss.item(),            'td_loss': td_loss.item(),            'grad_norm': grad_norm,            'priority': torch.mean(td_error_per_sample.abs(), dim=0).tolist(),        }    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)] for _ in range(3)]        )        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', 'agent_colla_alone_q']            - 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.        """        transition = {            'obs': obs,            'next_obs': timestep.obs,            'prev_state': model_output['prev_state'],            'action': model_output['action'],            'agent_colla_alone_q': model_output['agent_colla_alone_q'],            '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)] for _ in range(3)]        )        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 for 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', 'colla_loss', 'td_loss', 'grad_norm']
ding.policy.collaq¶

ding.policy.collaq ¶

CollaQPolicy ¶

default_model() ¶

Full Source Code

`ding.policy.collaq`¶

`ding.policy.collaq` ¶

`CollaQPolicy` ¶

`default_model()` ¶
