`ding.policy.r2d2`¶

`ding.policy.r2d2` ¶

`R2D2Policy` ¶

Bases: Policy
Overview
Policy class of R2D2, from paper Recurrent Experience Replay in Distributed Reinforcement Learning . R2D2 proposes that several tricks should be used to improve upon DRQN, namely some recurrent experience replay tricks and the burn-in mechanism for off-policy training.
`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.
.. note:: The user can define and use customized network model but must obey the same inferface definition indicated by import_names path. For example about R2D2, its registered name is drqn and the import_names is ding.model.template.q_learning.
Full Source Code

../ding/policy/r2d2.py
import copyfrom collections import namedtuplefrom typing import List, Dict, Any, Tuple, Union, Optionalimport torchfrom ding.model import model_wrapfrom ding.rl_utils import q_nstep_td_data, q_nstep_td_error, q_nstep_td_error_with_rescale, get_nstep_return_data, \    get_train_samplefrom ding.torch_utils import Adam, to_devicefrom ding.utils import POLICY_REGISTRYfrom ding.utils.data import timestep_collate, default_collate, default_decollatefrom .base_policy import Policy@POLICY_REGISTRY.register('r2d2')class R2D2Policy(Policy):    """    Overview:        Policy class of R2D2, from paper `Recurrent Experience Replay in Distributed Reinforcement Learning` .        R2D2 proposes that several tricks should be used to improve upon DRQN, namely some recurrent experience replay \        tricks and the burn-in mechanism for off-policy training.    Config:        == ==================== ======== ============== ======================================== =======================        ID Symbol               Type     Default Value  Description                              Other(Shape)        == ==================== ======== ============== ======================================== =======================        1  ``type``             str      r2d2           | 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  ``priority``         bool     False          | Whether use priority(PER)              | Priority sample,                                                                                                 | update priority        5  | ``priority_IS``    bool     False          | Whether use Importance Sampling Weight           | ``_weight``                                | to correct biased update. If True,                                                        | priority must be True.        6  | ``discount_``      float    0.997,         | Reward's future discount factor, aka.  | May be 1 when sparse           | ``factor``                  [0.95, 0.999]  | gamma                                  | reward env        7  ``nstep``            int      3,             | N-step reward discount sum for target                                         [3, 5]         | q_value estimation        8  ``burnin_step``      int      2              | The timestep of burnin operation,                                                        | which is designed to RNN hidden state                                                        | difference caused by off-policy        9  | ``learn.update``   int      1              | 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        10 | ``learn.batch_``   int      64             | The number of samples of an iteration           | ``size``        11 | ``learn.learning`` float    0.001          | Gradient step length of an iteration.           | ``_rate``        12 | ``learn.value_``   bool     True           | Whether use value_rescale function for           | ``rescale``                                | predicted value        13 | ``learn.target_``  int      100            | Frequence of target network update.    | Hard(assign) update           | ``update_freq``        14 | ``learn.ignore_``  bool     False          | Whether ignore done for target value   | Enable it for some           | ``done``                                   | calculation.                           | fake termination env        15 ``collect.n_sample`` int      [8, 128]       | The number of training samples of a    | It varies from                                                        | call of collector.                     | different envs        16 | ``collect.unroll`` int      1              | unroll length of an iteration          | In RNN, unroll_len>1           | ``_len``        == ==================== ======== ============== ======================================== =======================    """    config = dict(        # (str) RL policy register name (refer to function "POLICY_REGISTRY").        type='r2d2',        # (bool) Whether to use cuda for network.        cuda=False,        # (bool) Whether the RL algorithm is on-policy or off-policy.        on_policy=False,        # (bool) Whether to use priority(priority sample, IS weight, update priority)        priority=True,        # (bool) Whether to use Importance Sampling Weight to correct biased update. If True, priority must be True.        priority_IS_weight=True,        # (float) Reward's future discount factor, aka. gamma.        discount_factor=0.997,        # (int) N-step reward for target q_value estimation        nstep=5,        # (int) the timestep of burnin operation, which is designed to RNN hidden state difference        # caused by off-policy        burnin_step=20,        # (int) the trajectory length to unroll the RNN network minus        # the timestep of burnin operation        learn_unroll_len=80,        # learn_mode config        learn=dict(            # (int) The number of training updates (iterations) to perform after each data collection by the collector.            # A larger "update_per_collect" value implies a more off-policy approach.            # The whole pipeline process follows this cycle: collect data -> update policy -> collect data -> ...            update_per_collect=1,            # (int) The number of samples in a training batch.            batch_size=64,            # (float) The step size of gradient descent, determining the rate of learning.            learning_rate=0.0001,            # (int) Frequence of target network update.            # target_update_freq=100,            target_update_theta=0.001,            # (bool) whether use value_rescale function for predicted value            value_rescale=True,            # (bool) Whether ignore done(usually for max step termination env).            # 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,        ),        # collect_mode config        collect=dict(            # (int) How many training samples collected in one collection procedure.            # In each collect phase, we collect a total of <n_sample> sequence samples.            n_sample=32,            # (bool) It is important that set key traj_len_inf=True here,            # to make sure self._traj_len=INF in serial_sample_collector.py.            # In R2D2 policy, for each collect_env, we want to collect data of length self._traj_len=INF            # unless the episode enters the 'done' state.            traj_len_inf=True,            # (int) `env_num` is used in hidden state, should equal to that one in env config (e.g. collector_env_num).            # User should specify this value in user config. `None` is a placeholder.            env_num=None,        ),        # eval_mode config        eval=dict(            # (int) `env_num` is used in hidden state, should equal to that one in env config (e.g. evaluator_env_num).            # User should specify this value in user config.            env_num=None,        ),        other=dict(            # Epsilon greedy with decay.            eps=dict(                # (str) Type of decay. Supports either 'exp' (exponential) or 'linear'.                type='exp',                # (float) Initial value of epsilon at the start.                start=0.95,                # (float) Final value of epsilon after decay.                end=0.05,                # (int) The number of environment steps over which epsilon should decay.                decay=10000,            ),            replay_buffer=dict(                # (int) Maximum size of replay buffer. Usually, larger buffer size is better.                replay_buffer_size=10000,            ),        ),    )    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.        .. note::            The user can define and use customized network model but must obey the same inferface definition indicated \            by import_names path. For example about R2D2, its registered name is ``drqn`` and the import_names is \            ``ding.model.template.q_learning``.        """        return 'drqn', ['ding.model.template.q_learning']    def _init_learn(self) -> None:        """        Overview:            Initialize the learn mode of policy, including some attributes and modules. For R2D2, it mainly contains \            optimizer, algorithm-specific arguments such as burnin_step, value_rescale and gamma, main and target \            model. Because of the use of RNN, all the models should be wrappered with ``hidden_state`` which needs to \            be initialized with proper size.            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._optimizer = Adam(self._model.parameters(), lr=self._cfg.learn.learning_rate)        self._gamma = self._cfg.discount_factor        self._nstep = self._cfg.nstep        self._burnin_step = self._cfg.burnin_step        self._value_rescale = self._cfg.learn.value_rescale        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,        )        self._learn_model = model_wrap(            self._model,            wrapper_name='hidden_state',            state_num=self._cfg.learn.batch_size,        )        self._learn_model = model_wrap(self._learn_model, wrapper_name='argmax_sample')        self._learn_model.reset()        self._target_model.reset()    def _data_preprocess_learn(self, data: List[Dict[str, Any]]) -> Dict[str, torch.Tensor]:        """        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, torch.Tensor]`): The processed data, including at least \                ['main_obs', 'target_obs', 'burnin_obs', 'action', 'reward', 'done', 'weight']        """        # data preprocess        data = timestep_collate(data)        if self._cuda:            data = to_device(data, self._device)        if self._priority_IS_weight:            assert self._priority, "Use IS Weight correction, but Priority is not used."        if self._priority and self._priority_IS_weight:            data['weight'] = data['IS']        else:            data['weight'] = data.get('weight', None)        burnin_step = self._burnin_step        # data['done'], data['weight'], data['value_gamma'] is used in def _forward_learn() to calculate        # the q_nstep_td_error, should be length of [self._sequence_len-self._burnin_step]        ignore_done = self._cfg.learn.ignore_done        if ignore_done:            data['done'] = [None for _ in range(self._sequence_len - burnin_step)]        else:            data['done'] = data['done'][burnin_step:].float()  # for computation of online model self._learn_model            # NOTE that after the proprocessing of  get_nstep_return_data() in _get_train_sample            # the data['done'] [t] is already the n-step done        # if the data don't include 'weight' or 'value_gamma' then fill in None in a list        # with length of [self._sequence_len-self._burnin_step],        # below is two different implementation ways        if 'value_gamma' not in data:            data['value_gamma'] = [None for _ in range(self._sequence_len - burnin_step)]        else:            data['value_gamma'] = data['value_gamma'][burnin_step:]        if 'weight' not in data or data['weight'] is None:            data['weight'] = [None for _ in range(self._sequence_len - burnin_step)]        else:            data['weight'] = data['weight'] * torch.ones_like(data['done'])            # every timestep in sequence has same weight, which is the _priority_IS_weight in PER        # cut the seq_len from burn_in step to (seq_len - nstep) step        data['action'] = data['action'][burnin_step:-self._nstep]        # cut the seq_len from burn_in step to (seq_len - nstep) step        data['reward'] = data['reward'][burnin_step:-self._nstep]        # the burnin_nstep_obs is used to calculate the init hidden state of rnn for the calculation of the q_value,        # target_q_value, and target_q_action        # these slicing are all done in the outermost layer, which is the seq_len dim        data['burnin_nstep_obs'] = data['obs'][:burnin_step + self._nstep]        # the main_obs is used to calculate the q_value, the [bs:-self._nstep] means using the data from        # [bs] timestep to [self._sequence_len-self._nstep] timestep        data['main_obs'] = data['obs'][burnin_step:-self._nstep]        # the target_obs is used to calculate the target_q_value        data['target_obs'] = data['obs'][burnin_step + self._nstep:]        return data    def _forward_learn(self, data: List[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 (trajectory for R2D2) from the replay buffer and then \            returns the output result, including various training information such as loss, q value, priority.        Arguments:            - data (:obj:`List[List[Dict[int, Any]]]`): The input data used for policy forward, including a batch of \                training samples. For each dict element, 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 time and \                batch dimension by the utility functions ``self._data_preprocess_learn``. \                For R2D2, each element in list is a trajectory with the length of ``unroll_len``, and the element in \                trajectory list is a dict containing at least the following keys: ``obs``, ``action``, ``prev_state``, \                ``reward``, ``next_obs``, ``done``. Sometimes, it also contains other keys such as ``weight`` \                and ``value_gamma``.        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 R2D2Policy: ``ding.policy.tests.test_r2d2``.        """        # forward        data = self._data_preprocess_learn(data)  # output datatype: Dict        self._learn_model.train()        self._target_model.train()        # use the hidden state in timestep=0        # note the reset method is performed at the hidden state wrapper, to reset self._state.        self._learn_model.reset(data_id=None, state=data['prev_state'][0])        self._target_model.reset(data_id=None, state=data['prev_state'][0])        if len(data['burnin_nstep_obs']) != 0:            with torch.no_grad():                inputs = {'obs': data['burnin_nstep_obs']}                burnin_output = self._learn_model.forward(                    inputs, saved_state_timesteps=[self._burnin_step, self._burnin_step + self._nstep]                )  # keys include 'logit', 'hidden_state' 'saved_state', \                # 'action', for their specific dim, please refer to DRQN model                burnin_output_target = self._target_model.forward(                    inputs, saved_state_timesteps=[self._burnin_step, self._burnin_step + self._nstep]                )        self._learn_model.reset(data_id=None, state=burnin_output['saved_state'][0])        inputs = {'obs': data['main_obs']}        q_value = self._learn_model.forward(inputs)['logit']        self._learn_model.reset(data_id=None, state=burnin_output['saved_state'][1])        self._target_model.reset(data_id=None, state=burnin_output_target['saved_state'][1])        next_inputs = {'obs': data['target_obs']}        with torch.no_grad():            target_q_value = self._target_model.forward(next_inputs)['logit']            # argmax_action double_dqn            target_q_action = self._learn_model.forward(next_inputs)['action']        action, reward, done, weight = data['action'], data['reward'], data['done'], data['weight']        value_gamma = data['value_gamma']        # T, B, nstep -> T, nstep, B        reward = reward.permute(0, 2, 1).contiguous()        loss = []        td_error = []        for t in range(self._sequence_len - self._burnin_step - self._nstep):            # here t=0 means timestep <self._burnin_step> in the original sample sequence, we minus self._nstep            # because for the last <self._nstep> timestep in the sequence, we don't have their target obs            td_data = q_nstep_td_data(                q_value[t], target_q_value[t], action[t], target_q_action[t], reward[t], done[t], weight[t]            )            if self._value_rescale:                l, e = q_nstep_td_error_with_rescale(td_data, self._gamma, self._nstep, value_gamma=value_gamma[t])                loss.append(l)                td_error.append(e.abs())            else:                l, e = q_nstep_td_error(td_data, self._gamma, self._nstep, value_gamma=value_gamma[t])                loss.append(l)                # td will be a list of the length                # <self._sequence_len - self._burnin_step - self._nstep>                # and each value is a tensor of the size batch_size                td_error.append(e.abs())        loss = sum(loss) / (len(loss) + 1e-8)        # using the mixture of max and mean absolute n-step TD-errors as the priority of the sequence        td_error_per_sample = 0.9 * torch.max(            torch.stack(td_error), dim=0        )[0] + (1 - 0.9) * (torch.sum(torch.stack(td_error), dim=0) / (len(td_error) + 1e-8))        # torch.max(torch.stack(td_error), dim=0) will return tuple like thing, please refer to torch.max        # td_error shape list(<self._sequence_len-self._burnin_step-self._nstep>, B),        # for example, (75,64)        # torch.sum(torch.stack(td_error), dim=0) can also be replaced with sum(td_error)        # update        self._optimizer.zero_grad()        loss.backward()        self._optimizer.step()        # after update        self._target_model.update(self._learn_model.state_dict())        # the information for debug        batch_range = torch.arange(action[0].shape[0])        q_s_a_t0 = q_value[0][batch_range, action[0]]        target_q_s_a_t0 = target_q_value[0][batch_range, target_q_action[0]]        return {            'cur_lr': self._optimizer.defaults['lr'],            'total_loss': loss.item(),            'priority': td_error_per_sample.tolist(),  # note abs operation has been performed above            # the first timestep in the sequence, may not be the start of episode            'q_s_taken-a_t0': q_s_a_t0.mean().item(),            'target_q_s_max-a_t0': target_q_s_a_t0.mean().item(),            'q_s_a-mean_t0': q_value[0].mean().item(),        }    def _reset_learn(self, data_id: Optional[List[int]] = None) -> None:        """        Overview:            Reset some stateful variables for learn mode when necessary, such as the hidden state of RNN or the \            memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \            varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \            different trajectories in ``data_id`` will have different hidden state in RNN.        Arguments:            - data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \                (i.e. RNN hidden_state in R2D2) specified by ``data_id``.        """        self._learn_model.reset(data_id=data_id)    def _state_dict_learn(self) -> Dict[str, Any]:        """        Overview:            Return the state_dict of learn mode, usually including model, target_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:        """        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:        """        Overview:            Initialize the collect mode of policy, including related attributes and modules. For R2D2, it contains the \            collect_model to balance the exploration and exploitation with epsilon-greedy sample mechanism and \            maintain the hidden state of rnn. Besides, there are some initialization operations about other \            algorithm-specific arguments such as burnin_step, unroll_len and nstep.            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``.        .. tip::            Some variables need to initialize independently in different modes, such as gamma and nstep in R2D2. This \            design is for the convenience of parallel execution of different policy modes.        """        self._nstep = self._cfg.nstep        self._burnin_step = self._cfg.burnin_step        self._gamma = self._cfg.discount_factor        self._sequence_len = self._cfg.learn_unroll_len + self._cfg.burnin_step        self._unroll_len = self._sequence_len        # for r2d2, this hidden_state wrapper is to add the 'prev hidden state' for each transition.        # Note that collect env forms a batch and the key is added for the batch simultaneously.        self._collect_model = model_wrap(            self._model, wrapper_name='hidden_state', state_num=self._cfg.collect.env_num, save_prev_state=True        )        self._collect_model = model_wrap(self._collect_model, wrapper_name='eps_greedy_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 (prev_state) 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::            RNN's hidden states are maintained in the policy, so we don't need pass them into data but to reset the \            hidden states with ``_reset_collect`` method when episode ends. Besides, the previous hidden states are \            necessary for training, so we need to return them in ``_process_transition`` 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 R2D2Policy: ``ding.policy.tests.test_r2d2``.        """        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():            # in collect phase, inference=True means that each time we only pass one timestep data,            # so the we can get the hidden state of rnn: <prev_state> at each timestep.            output = self._collect_model.forward(data, data_id=data_id, eps=eps, inference=True)        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:        """        Overview:            Reset some stateful variables for eval mode when necessary, such as the hidden state of RNN or the \            memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \            varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \            different environments/episodes in evaluation in ``data_id`` will have different hidden state in RNN.        Arguments:            - data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \                (i.e., RNN hidden_state in R2D2) specified by ``data_id``.        """        self._collect_model.reset(data_id=data_id)    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 R2D2, it contains obs, action, prev_state, reward, and 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 given the observation \                as input. For R2D2, it contains the action and the prev_state of RNN.            - 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,            'action': policy_output['action'],            'prev_state': policy_output['prev_state'],            '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 R2D2, a train sample is processed transitions with unroll_len \            length. This method is usually used in collectors to execute necessary \            RL data preprocessing before training, which can help learner amortize revelant time consumption. \            In addition, you can also implement this method as an identity function and do the data processing \            in ``self._forward_learn`` method.        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 sample is a fixed-length \                trajectory, and each element in a sample is the similar format as input transitions, but may contain \                more data for training, such as nstep reward and value_gamma factor.        """        transitions = get_nstep_return_data(transitions, self._nstep, gamma=self._gamma)        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 R2D2, it contains the \            eval model to greedily select action with argmax q_value mechanism and main the hidden state.            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='hidden_state', state_num=self._cfg.eval.env_num)        self._eval_model = model_wrap(self._eval_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. ``_forward_eval`` often use argmax sample method to get actions that \            q_value is the highest.        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::            RNN's hidden states are maintained in the policy, so we don't need pass them into data but to reset the \            hidden states with ``_reset_eval`` method when the episode ends.        .. 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 R2D2Policy: ``ding.policy.tests.test_r2d2``.        """        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, inference=True)        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:        """        Overview:            Reset some stateful variables for eval mode when necessary, such as the hidden state of RNN or the \            memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \            varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \            different environments/episodes in evaluation in ``data_id`` will have different hidden state in RNN.        Arguments:            - data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \                (i.e., RNN hidden_state in R2D2) specified by ``data_id``.        """        self._eval_model.reset(data_id=data_id)    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.        """        return super()._monitor_vars_learn() + [            'total_loss', 'priority', 'q_s_taken-a_t0', 'target_q_s_max-a_t0', 'q_s_a-mean_t0'        ]
ding.policy.r2d2¶

ding.policy.r2d2 ¶

R2D2Policy ¶

default_model() ¶

Full Source Code

`ding.policy.r2d2`¶

`ding.policy.r2d2` ¶

`R2D2Policy` ¶

`default_model()` ¶
