`ding.policy.ngu`¶

`ding.policy.ngu` ¶

`NGUPolicy` ¶

Bases: Policy
Overview
Policy class of NGU. The corresponding paper is never give up: learning directed exploration strategies.
Config
Full Source Code

../ding/policy/ngu.py
from typing import List, Dict, Any, Tuple, Union, Optionalfrom collections import namedtupleimport torchimport copyfrom ding.torch_utils import Adam, to_devicefrom 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.model import model_wrapfrom ding.utils import POLICY_REGISTRYfrom ding.utils.data import timestep_collate, default_collate, default_decollatefrom .base_policy import Policy@POLICY_REGISTRY.register('ngu')class NGUPolicy(Policy):    r"""    Overview:        Policy class of NGU. The corresponding paper is `never give up: learning directed exploration strategies`.    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='ngu',        # (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=20,        # (int) <learn_unroll_len> is the total length of [sequence sample] minus        # the length of burnin part in [sequence sample],        # i.e., <sequence sample length> = <unroll_len> = <burnin_step> + <learn_unroll_len>        learn_unroll_len=80,  # set this key according to the episode length        learn=dict(            update_per_collect=1,            batch_size=64,            learning_rate=0.0001,            # ==============================================================            # The following configs are algorithm-specific            # ==============================================================            # (float type) target_update_theta: Used for soft update of the target network,            # aka. Interpolation factor in polyak averaging for target networks.            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 set key traj_len_inf=True here,            # to make sure self._traj_len=INF in serial_sample_collector.py.            # In sequence-based policy, for each collect_env,            # we want to collect data of length self._traj_len=INF            # unless the episode enters the 'done' state.            # In each collect phase, we collect a total of <n_sample> sequence samples.            n_sample=32,            traj_len_inf=True,            # `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 'ngu', ['ding.model.template.ngu']    def _init_learn(self) -> None:        r"""        Overview:            Init the learner model of R2D2Policy        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._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)        # here we should not adopt the 'assign' mode of target network here because the reset bug        # self._target_model = model_wrap(        #     self._target_model,        #     wrapper_name='target',        #     update_type='assign',        #     update_kwargs={'freq': self._cfg.learn.target_update_freq}        # )        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, save_prev_state=True        )        self._learn_model = model_wrap(            self._model, wrapper_name='hidden_state', state_num=self._cfg.learn.batch_size, save_prev_state=True        )        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]        ignore_done = self._cfg.learn.ignore_done        if ignore_done:            data['done'] = [None for _ in range(self._sequence_len - bs - self._nstep)]        else:            data['done'] = data['done'][bs:].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 - 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        # 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]        data['burnin_nstep_action'] = data['action'][:bs + self._nstep]        data['burnin_nstep_reward'] = data['reward'][:bs + self._nstep]        data['burnin_nstep_beta'] = data['beta'][:bs + self._nstep]        # split obs into three parts 'burnin_obs' [0:bs], 'main_obs' [bs:bs+nstep], 'target_obs' [bs+nstep:]        # data['burnin_obs'] = data['obs'][:bs]        data['main_obs'] = data['obs'][bs:-self._nstep]        data['target_obs'] = data['obs'][bs + self._nstep:]        # data['burnin_action'] = data['action'][:bs]        data['main_action'] = data['action'][bs:-self._nstep]        data['target_action'] = data['action'][bs + self._nstep:]        # data['burnin_reward'] = data['reward'][:bs]        data['main_reward'] = data['reward'][bs:-self._nstep]        data['target_reward'] = data['reward'][bs + self._nstep:]        # data['burnin_beta'] = data['beta'][:bs]        data['main_beta'] = data['beta'][bs:-self._nstep]        data['target_beta'] = data['beta'][bs + self._nstep:]        # Note that Must be here after the previous slicing operation        data['action'] = data['action'][bs:-self._nstep]        data['reward'] = data['reward'][bs:-self._nstep]        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()        # use 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'],                    'action': data['burnin_nstep_action'],                    'reward': data['burnin_nstep_reward'],                    'beta': data['burnin_nstep_beta'],                }                tmp = self._learn_model.forward(                    inputs, saved_state_timesteps=[self._burnin_step, self._burnin_step + self._nstep]                )                tmp_target = self._target_model.forward(                    inputs, saved_state_timesteps=[self._burnin_step, self._burnin_step + self._nstep]                )        inputs = {            'obs': data['main_obs'],            'action': data['main_action'],            'reward': data['main_reward'],            'beta': data['main_beta'],        }        self._learn_model.reset(data_id=None, state=tmp['saved_state'][0])        q_value = self._learn_model.forward(inputs)['logit']        self._learn_model.reset(data_id=None, state=tmp['saved_state'][1])        self._target_model.reset(data_id=None, state=tmp_target['saved_state'][1])        next_inputs = {            'obs': data['target_obs'],            'action': data['target_action'],            'reward': data['target_reward'],            'beta': data['target_beta'],        }        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 = [            None for _ in range(self._sequence_len - self._burnin_step)        ]  # NOTE this is important, because we use diffrent gamma according to their beta in NGU alg.        # T, B, nstep -> T, nstep, B        reward = reward.permute(0, 2, 1).contiguous()        loss = []        td_error = []        self._gamma = [self.index_to_gamma[int(i)] for i in data['main_beta'][0]]  # T, B -> B, e.g. 75,64 -> 64        # reward torch.Size([4, 5, 64])        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_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))        # 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.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, "ngu use default <unroll_len = learn_unroll_len + burnin_step>"        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        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()        self.index_to_gamma = {  # NOTE            i: 1 - torch.exp(                (                    (self._cfg.collect.env_num - 1 - i) * torch.log(torch.tensor(1 - 0.997)) +                    i * torch.log(torch.tensor(1 - 0.99))                ) / (self._cfg.collect.env_num - 1)            )            for i in range(self._cfg.collect.env_num)        }        # NOTE: for NGU policy collect phase        self.beta_index = {            i: torch.randint(0, self._cfg.collect.env_num, [1])            for i in range(self._cfg.collect.env_num)        }        # epsilon=0.4, alpha=9        self.eps = {i: 0.4 ** (1 + 8 * i / (self._cfg.collect.env_num - 1)) for i in range(self._cfg.collect.env_num)}    def _forward_collect(self, data: dict) -> 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()))        obs = data['obs']        prev_action = data['prev_action'].long()        prev_reward_extrinsic = data['prev_reward_extrinsic']        beta_index = default_collate(list(self.beta_index.values()))        if len(data_id) != self._cfg.collect.env_num:            # in case, some env is in reset state and only return part data            beta_index = beta_index[data_id]        if self._cuda:            obs = to_device(obs, self._device)            beta_index = to_device(beta_index, self._device)            prev_action = to_device(prev_action, self._device)            prev_reward_extrinsic = to_device(prev_reward_extrinsic, self._device)        # TODO(pu): add prev_reward_intrinsic to network input,        #  reward uses some kind of embedding instead of 1D value        data = {            'obs': obs,            'prev_action': prev_action,            'prev_reward_extrinsic': prev_reward_extrinsic,            'beta': beta_index        }        self._collect_model.eval()        with torch.no_grad():            output = self._collect_model.forward(data, data_id=data_id, eps=self.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)        # NOTE: for NGU policy, in collect phase, each episode, we sample a new beta for each env        if data_id is not None:            self.beta_index[data_id[0]] = torch.randint(0, self._cfg.collect.env_num, [1])    def _process_transition(self, obs: Any, model_output: dict, timestep: namedtuple, env_id) -> 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.        """        if hasattr(timestep, 'null'):            transition = {                'beta': self.beta_index[env_id],                'obs': obs['obs'],  # NOTE: input obs including obs, prev_action, prev_reward_extrinsic                'action': model_output['action'],                'prev_state': model_output['prev_state'],                'reward': timestep.reward,                'done': timestep.done,                'null': timestep.null,            }        else:            transition = {                'beta': self.beta_index[env_id],                'obs': obs['obs'],  # NOTE: input obs including obs, prev_action, prev_reward_extrinsic                'action': model_output['action'],                'prev_state': model_output['prev_state'],                'reward': timestep.reward,                'done': timestep.done,                'null': False,            }        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        """        data = get_nstep_return_data(data, self._nstep, gamma=self.index_to_gamma[int(data[0]['beta'])].item())        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()        # NOTE: for NGU policy eval phase        # beta_index = 0 -> beta is approximately 0        self.beta_index = {i: torch.tensor([0]) for i in range(self._cfg.eval.env_num)}    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()))        obs = data['obs']        prev_action = data['prev_action'].long()        prev_reward_extrinsic = data['prev_reward_extrinsic']        beta_index = default_collate(list(self.beta_index.values()))        if len(data_id) != self._cfg.collect.env_num:            # in case, some env is in reset state and only return part data            beta_index = beta_index[data_id]        if self._cuda:            obs = to_device(obs, self._device)            beta_index = to_device(beta_index, self._device)            prev_action = to_device(prev_action, self._device)            prev_reward_extrinsic = to_device(prev_reward_extrinsic, self._device)        # TODO(pu): add prev_reward_intrinsic to network input,        #  reward uses some kind of embedding instead of 1D value        data = {            'obs': obs,            'prev_action': prev_action,            'prev_reward_extrinsic': prev_reward_extrinsic,            'beta': beta_index        }        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', 'priority', 'q_s_taken-a_t0', 'target_q_s_max-a_t0', 'q_s_a-mean_t0'        ]
ding.policy.ngu¶

ding.policy.ngu ¶

NGUPolicy ¶

Full Source Code

`ding.policy.ngu`¶

`ding.policy.ngu` ¶

`NGUPolicy` ¶
