DI-engine/ding/policy/ngu.py

from typing import List, Dict, Any, Tuple, Union, Optional
from collections import namedtuple
import torch
import copy

from ding.torch_utils import Adam, to_device
from ding.rl_utils import q_nstep_td_data, q_nstep_td_error, q_nstep_td_error_with_rescale, get_nstep_return_data, \
    get_train_sample
from ding.model import model_wrap
from ding.utils import POLICY_REGISTRY
from ding.utils.data import timestep_collate, default_collate, default_decollate
from .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'],
                    'enable_fast_timestep': True
                }
                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'],
            'enable_fast_timestep': True
        }
        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'],
            'enable_fast_timestep': True
        }
        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'
        ]