`ding.policy.r2d3`¶

`ding.policy.r2d3` ¶

`R2D3Policy` ¶

Bases: Policy
Overview
Policy class of r2d3, from paper Making Efficient Use of Demonstrations to Solve Hard Exploration Problems .
Config
Full Source Code

../ding/policy/r2d3.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_error_with_rescale, get_nstep_return_data, \    get_train_sample, dqfd_nstep_td_error, dqfd_nstep_td_error_with_rescale, dqfd_nstep_td_datafrom 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('r2d3')class R2D3Policy(Policy):    r"""    Overview:        Policy class of r2d3, from paper `Making Efficient Use of Demonstrations to Solve Hard Exploration Problems` .    Config:        == ==================== ======== ============== ======================================== =======================        ID Symbol               Type     Default Value  Description                              Other(Shape)        == ==================== ======== ============== ======================================== =======================        1  ``type``             str      dqn            | 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='r2d3',        # (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 use priority(priority sample, IS weight, update priority)        priority=True,        # (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True.        priority_IS_weight=True,        # ==============================================================        # The following configs are algorithm-specific        # ==============================================================        # (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=2,        # (int) the trajectory length to unroll the RNN network minus        # the timestep of burnin operation        learn_unroll_len=80,        learn=dict(            update_per_collect=1,            batch_size=64,            learning_rate=0.0001,            # ==============================================================            # The following configs are algorithm-specific            # ==============================================================            # (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,            ignore_done=False,        ),        collect=dict(            # NOTE it is important that don't include key n_sample here, to make sure self._traj_len=INF            # each_iter_n_sample=32,            # `env_num` is used in hidden state, should equal to that one in env config.            # User should specify this value in user config.            env_num=None,        ),        eval=dict(            # `env_num` is used in hidden state, should equal to that one in env config.            # User should specify this value in user config.            env_num=None,        ),        other=dict(            eps=dict(                type='exp',                start=0.95,                end=0.05,                decay=10000,            ),            replay_buffer=dict(replay_buffer_size=10000, ),        ),    )    def default_model(self) -> Tuple[str, List[str]]:        return 'drqn', ['ding.model.template.q_learning']    def _init_learn(self) -> None:        r"""        Overview:            Init the learner model of r2d3Policy        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            - nstep (:obj:`int`): The num of n step return            - value_rescale (:obj:`bool`): Whether to use value rescaled loss in algorithm            - burnin_step (:obj:`int`): The num of step of burnin        """        self.lambda1 = self._cfg.learn.lambda1  # n-step return        self.lambda2 = self._cfg.learn.lambda2  # supervised loss        self.lambda3 = self._cfg.learn.lambda3  # L2        self.lambda_one_step_td = self._cfg.learn.lambda_one_step_td  # 1-step return        # margin function in JE, here we implement this as a constant        self.margin_function = self._cfg.learn.margin_function        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, weight_decay=self.lambda3, optim_type='adamw'        )        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:        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, including at least \                ['main_obs', 'target_obs', 'burnin_obs', 'action', 'reward', 'done', 'weight']            - data_info (:obj:`dict`): the data info, such as replay_buffer_idx, replay_unique_id        """        # 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)        bs = 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-self._nstep]        ignore_done = self._cfg.learn.ignore_done        if ignore_done:            data['done'] = [None for _ in range(self._sequence_len - bs)]        else:            data['done'] = data['done'][bs:].float()            # 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-self._nstep],        # below is two different implementation ways        if 'value_gamma' not in data:            data['value_gamma'] = [None for _ in range(self._sequence_len - bs)]        else:            data['value_gamma'] = data['value_gamma'][bs:]        if 'weight' not in data:            data['weight'] = [None for _ in range(self._sequence_len - bs)]        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        data['action'] = data['action'][bs:-self._nstep]        data['reward'] = data['reward'][bs:-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        data['burnin_nstep_obs'] = data['obs'][:bs + 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'][bs:-self._nstep]        # the target_obs is used to calculate the target_q_value        data['target_obs'] = data['obs'][bs + self._nstep:]        # TODO(pu)        data['target_obs_one_step'] = data['obs'][bs + 1:]        if ignore_done:            data['done_one_step'] = [None for _ in range(self._sequence_len - bs)]        else:            data['done_one_step'] = data['done_one_step'][bs:].float()        return data    def _forward_learn(self, data: dict) -> Dict[str, Any]:        r"""        Overview:            Forward and backward function of learn mode.            Acquire the data, calculate the loss and optimize learner model.        Arguments:            - data (:obj:`dict`): Dict type data, including at least \                ['main_obs', 'target_obs', 'burnin_obs', 'action', 'reward', 'done', 'weight']        Returns:            - info_dict (:obj:`Dict[str, Any]`): Including cur_lr and total_loss                - cur_lr (:obj:`float`): Current learning rate                - total_loss (:obj:`float`): The calculated loss        """        # forward        data = self._data_preprocess_learn(data)        self._learn_model.train()        self._target_model.train()        # take out the hidden state in timestep=0        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, self._burnin_step + 1]                )                burnin_output_target = self._target_model.forward(                    inputs,                    saved_state_timesteps=[self._burnin_step, self._burnin_step + self._nstep, self._burnin_step + 1]                )        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']        # n-step        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']        # one-step        self._learn_model.reset(data_id=None, state=burnin_output['saved_state'][2])        self._target_model.reset(data_id=None, state=burnin_output_target['saved_state'][2])        next_inputs_one_step = {'obs': data['target_obs_one_step']}        with torch.no_grad():            target_q_value_one_step = self._target_model.forward(next_inputs_one_step)['logit']            # argmax_action double_dqn            target_q_action_one_step = self._learn_model.forward(next_inputs_one_step)['action']        action, reward, done, weight = data['action'], data['reward'], data['done'], data['weight']        value_gamma = data['value_gamma']        done_one_step = data['done_one_step']        # T, B, nstep -> T, nstep, B        reward = reward.permute(0, 2, 1).contiguous()        loss = []        loss_nstep = []        loss_1step = []        loss_sl = []        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 = dqfd_nstep_td_data(                q_value[t],                target_q_value[t],                action[t],                target_q_action[t],                reward[t],                done[t],                done_one_step[t],                weight[t],                target_q_value_one_step[t],                target_q_action_one_step[t],                data['is_expert'][t],  # is_expert flag(expert 1, agent 0)            )            if self._value_rescale:                l, e, loss_statistics = dqfd_nstep_td_error_with_rescale(                    td_data,                    self._gamma,                    self.lambda1,                    self.lambda2,                    self.margin_function,                    self.lambda_one_step_td,                    self._nstep,                    False,                    value_gamma=value_gamma[t],                )                loss.append(l)                # td_error.append(e.abs())  # first sum then abs                td_error.append(e)  # first abs then sum                # loss statistics for debugging                loss_nstep.append(loss_statistics[0])                loss_1step.append(loss_statistics[1])                loss_sl.append(loss_statistics[2])            else:                l, e, loss_statistics = dqfd_nstep_td_error(                    td_data,                    self._gamma,                    self.lambda1,                    self.lambda2,                    self.margin_function,                    self.lambda_one_step_td,                    self._nstep,                    False,                    value_gamma=value_gamma[t],                )                loss.append(l)                # td_error.append(e.abs())  # first sum then abs                td_error.append(e)  # first abs then sum                # loss statistics for debugging                loss_nstep.append(loss_statistics[0])                loss_1step.append(loss_statistics[1])                loss_sl.append(loss_statistics[2])        loss = sum(loss) / (len(loss) + 1e-8)        # loss statistics for debugging        loss_nstep = sum(loss_nstep) / (len(loss_nstep) + 1e-8)        loss_1step = sum(loss_1step) / (len(loss_1step) + 1e-8)        loss_sl = sum(loss_sl) / (len(loss_sl) + 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))        # 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(),            # loss statistics for debugging            'nstep_loss': loss_nstep.item(),            '1step_loss': loss_1step.item(),            'sl_loss': loss_sl.item(),            'priority': td_error_per_sample.abs().tolist(),            # 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:        self._learn_model.reset(data_id=data_id)    def _state_dict_learn(self) -> Dict[str, Any]:        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:        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.        """        assert 'unroll_len' not in self._cfg.collect, "r2d3 use default unroll_len"        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 compatibility        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, eps: float) -> dict:        r"""        Overview:            Collect output according to eps_greedy plugin        Arguments:            - data (:obj:`dict`): Dict type data, including at least ['obs'].        Returns:            - data (:obj:`dict`): The collected data        """        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:        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 ['reward', 'done'] \                (here 'obs' indicates obs after env step).        Returns:            - transition (:obj:`dict`): Dict type transition data.        """        transition = {            'obs': obs,            'action': model_output['action'],            'prev_state': model_output['prev_state'],            'reward': timestep.reward,            'done': timestep.done,        }        return transition    def _get_train_sample(self, data: list) -> Union[None, List[Any]]:        r"""        Overview:            Get the trajectory and the n step return data, then sample from the n_step return data        Arguments:            - data (:obj:`list`): The trajectory's cache        Returns:            - samples (:obj:`dict`): The training samples generated        """        from copy import deepcopy        data_one_step = deepcopy(get_nstep_return_data(data, 1, gamma=self._gamma))        data = get_nstep_return_data(data, self._nstep, gamma=self._gamma)        for i in range(len(data)):            # here we record the one-step done, we don't need record one-step reward,            # because the n-step reward in data already include one-step reward            data[i]['done_one_step'] = data_one_step[i]['done']        return get_train_sample(data, self._sequence_len)    def _init_eval(self) -> None:        r"""        Overview:            Evaluate mode init method. Called by ``self.__init__``.            Init eval model with argmax strategy.        """        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) -> dict:        r"""        Overview:            Forward function of collect mode, similar to ``self._forward_collect``.        Arguments:            - data (:obj:`dict`): Dict type data, including at least ['obs'].        Returns:            - output (:obj:`dict`): Dict type data, including at least inferred action according to input obs.        """        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:        self._eval_model.reset(data_id=data_id)    def _monitor_vars_learn(self) -> List[str]:        return super()._monitor_vars_learn() + [            'total_loss', 'nstep_loss', '1step_loss', 'sl_loss', 'priority', 'q_s_taken-a_t0', 'target_q_s_max-a_t0',            'q_s_a-mean_t0'        ]
ding.policy.r2d3¶

ding.policy.r2d3 ¶

R2D3Policy ¶

Full Source Code

`ding.policy.r2d3`¶

`ding.policy.r2d3` ¶

`R2D3Policy` ¶
