DI-engine/ding/policy/atoc.py

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

from ding.torch_utils import Adam, to_device
from ding.rl_utils import v_1step_td_data, v_1step_td_error, get_train_sample
from ding.model import model_wrap
from ding.utils import POLICY_REGISTRY
from ding.utils.data import default_collate, default_decollate
from .base_policy import Policy
from .common_utils import default_preprocess_learn


@POLICY_REGISTRY.register('atoc')
class ATOCPolicy(Policy):
    r"""
    Overview:
        Policy class of ATOC algorithm.
    Interface:
        __init__, set_setting, __repr__, state_dict_handle
    Property:
        learn_mode, collect_mode, eval_mode
    """

    config = dict(
        # (str) RL policy register name (refer to function "POLICY_REGISTRY").
        type='atoc',
        # (bool) Whether to use cuda for network.
        cuda=False,
        # (bool) whether use on-policy training pipeline(behaviour policy and training policy are the same)
        on_policy=False,
        # (bool) Whether use priority(priority sample, IS weight, update priority)
        priority=False,
        # (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True.
        priority_IS_weight=False,
        model=dict(
            # (bool) Whether to use communication module in ATOC, if not, it is a multi-agent DDPG
            communication=True,
            # (int) The number of thought size
            thought_size=8,
            # (int) The number of agent for each communication group
            agent_per_group=2,
        ),
        learn=dict(
            # (int) Collect n_sample data, update model n_iteration time
            update_per_collect=5,
            # (int) The number of data for a train iteration
            batch_size=64,
            # (float) Gradient-descent step size of actor
            learning_rate_actor=0.001,
            # (float) Gradient-descent step size of critic
            learning_rate_critic=0.001,
            # ==============================================================
            # The following configs is algorithm-specific
            # ==============================================================
            # (float) Target network update weight, theta * new_w + (1 - theta) * old_w, defaults in [0, 0.1]
            target_theta=0.005,
            # (float) Discount factor for future reward, defaults int [0, 1]
            discount_factor=0.99,
            # (bool) Whether to use communication module in ATOC, if not, it is a multi-agent DDPG
            communication=True,
            # (int) The frequency of actor update, each critic update
            actor_update_freq=1,
            # (bool) Whether use noise in action output when learning
            noise=True,
            # (float) The std of noise distribution for target policy smooth
            noise_sigma=0.15,
            # (float, float) The minimum and maximum value of noise
            noise_range=dict(
                min=-0.5,
                max=0.5,
            ),
            # (bool) Whether to use reward batch norm in the total batch
            reward_batch_norm=False,
            ignore_done=False,
        ),
        collect=dict(
            # (int) Collect n_sample data, update model n_iteration time
            # n_sample=64,
            # (int) Unroll length of a train iteration(gradient update step)
            unroll_len=1,
            # ==============================================================
            # The following configs is algorithm-specific
            # ==============================================================
            # (float) The std of noise distribution for exploration
            noise_sigma=0.4,
        ),
        eval=dict(),
        other=dict(
            replay_buffer=dict(
                # (int) The max size of replay buffer
                replay_buffer_size=100000,
                # (int) The max use count of data, if count is bigger than this value, the data will be removed
                max_use=10,
            ),
        ),
    )

    def default_model(self) -> Tuple[str, List[str]]:
        return 'atoc', ['ding.model.template.atoc']

    def _init_learn(self) -> None:
        r"""
        Overview:
            Learn mode init method. Called by ``self.__init__``.
            Init actor and critic optimizers, algorithm config, main and target models.
        """
        self._priority = self._cfg.priority
        self._priority_IS_weight = self._cfg.priority_IS_weight
        assert not self._priority and not self._priority_IS_weight
        # algorithm config
        self._communication = self._cfg.learn.communication
        self._gamma = self._cfg.learn.discount_factor
        self._actor_update_freq = self._cfg.learn.actor_update_freq
        # actor and critic optimizer
        self._optimizer_actor = Adam(
            self._model.actor.parameters(),
            lr=self._cfg.learn.learning_rate_actor,
        )
        self._optimizer_critic = Adam(
            self._model.critic.parameters(),
            lr=self._cfg.learn.learning_rate_critic,
        )
        if self._communication:
            self._optimizer_actor_attention = Adam(
                self._model.actor.attention.parameters(),
                lr=self._cfg.learn.learning_rate_actor,
            )
        self._reward_batch_norm = self._cfg.learn.reward_batch_norm

        # main and target models
        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_theta}
        )
        if self._cfg.learn.noise:
            self._target_model = model_wrap(
                self._target_model,
                wrapper_name='action_noise',
                noise_type='gauss',
                noise_kwargs={
                    'mu': 0.0,
                    'sigma': self._cfg.learn.noise_sigma
                },
                noise_range=self._cfg.learn.noise_range
            )
        self._learn_model = model_wrap(self._model, wrapper_name='base')
        self._learn_model.reset()
        self._target_model.reset()

        self._forward_learn_cnt = 0  # count iterations

    def _forward_learn(self, data: dict) -> Dict[str, Any]:
        r"""
        Overview:
            Forward and backward function of learn mode.
        Arguments:
            - data (:obj:`dict`): Dict type data, including at least ['obs', 'action', 'reward', 'next_obs']
        Returns:
            - info_dict (:obj:`Dict[str, Any]`): Including at least actor and critic lr, different losses.
        """
        loss_dict = {}
        data = default_preprocess_learn(data, ignore_done=self._cfg.learn.ignore_done, use_nstep=False)
        if self._cuda:
            data = to_device(data, self._device)
        # ====================
        # critic learn forward
        # ====================
        self._learn_model.train()
        self._target_model.train()
        next_obs = data['next_obs']
        reward = data['reward']
        if self._reward_batch_norm:
            reward = (reward - reward.mean()) / (reward.std() + 1e-8)
        # current q value
        q_value = self._learn_model.forward(data, mode='compute_critic')['q_value']
        # target q value.
        with torch.no_grad():
            next_action = self._target_model.forward(next_obs, mode='compute_actor')['action']
            next_data = {'obs': next_obs, 'action': next_action}
            target_q_value = self._target_model.forward(next_data, mode='compute_critic')['q_value']
        td_data = v_1step_td_data(q_value.mean(-1), target_q_value.mean(-1), reward, data['done'], data['weight'])
        critic_loss, td_error_per_sample = v_1step_td_error(td_data, self._gamma)
        loss_dict['critic_loss'] = critic_loss
        # ================
        # critic update
        # ================
        self._optimizer_critic.zero_grad()
        critic_loss.backward()
        self._optimizer_critic.step()
        # ===============================
        # actor learn forward and update
        # ===============================
        # actor updates every ``self._actor_update_freq`` iters
        if (self._forward_learn_cnt + 1) % self._actor_update_freq == 0:
            if self._communication:
                output = self._learn_model.forward(data['obs'], mode='compute_actor', get_delta_q=False)
                output['delta_q'] = data['delta_q']
                attention_loss = self._learn_model.forward(output, mode='optimize_actor_attention')['loss']
                loss_dict['attention_loss'] = attention_loss
                self._optimizer_actor_attention.zero_grad()
                attention_loss.backward()
                self._optimizer_actor_attention.step()

            output = self._learn_model.forward(data['obs'], mode='compute_actor', get_delta_q=False)

            critic_input = {'obs': data['obs'], 'action': output['action']}
            actor_loss = -self._learn_model.forward(critic_input, mode='compute_critic')['q_value'].mean()
            loss_dict['actor_loss'] = actor_loss
            # actor update
            self._optimizer_actor.zero_grad()
            actor_loss.backward()
            self._optimizer_actor.step()
        # =============
        # after update
        # =============
        loss_dict['total_loss'] = sum(loss_dict.values())
        self._forward_learn_cnt += 1
        self._target_model.update(self._learn_model.state_dict())
        return {
            'cur_lr_actor': self._optimizer_actor.defaults['lr'],
            'cur_lr_critic': self._optimizer_critic.defaults['lr'],
            'priority': td_error_per_sample.abs().tolist(),
            'q_value': q_value.mean().item(),
            **loss_dict,
        }

    def _state_dict_learn(self) -> Dict[str, Any]:
        return {
            'model': self._learn_model.state_dict(),
            'target_model': self._target_model.state_dict(),
            'optimizer_actor': self._optimizer_actor.state_dict(),
            'optimizer_critic': self._optimizer_critic.state_dict(),
            'optimize_actor_attention': self._optimizer_actor_attention.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_actor.load_state_dict(state_dict['optimizer_actor'])
        self._optimizer_critic.load_state_dict(state_dict['optimizer_critic'])
        self._optimizer_actor_attention.load_state_dict(state_dict['optimize_actor_attention'])

    def _init_collect(self) -> None:
        r"""
        Overview:
            Collect mode init method. Called by ``self.__init__``.
            Init traj and unroll length, collect model.
        """
        self._unroll_len = self._cfg.collect.unroll_len
        # collect model
        self._collect_model = model_wrap(
            self._model,
            wrapper_name='action_noise',
            noise_type='gauss',
            noise_kwargs={
                'mu': 0.0,
                'sigma': self._cfg.collect.noise_sigma
            },
            noise_range=None,  # no noise clip in actor
        )
        self._collect_model.reset()

    def _forward_collect(self, data: dict) -> dict:
        r"""
        Overview:
            Forward function of collect mode.
        Arguments:
            - data (:obj:`Dict[str, Any]`): Dict type data, stacked env data for predicting policy_output(action), \
                values are torch.Tensor or np.ndarray or dict/list combinations, keys are env_id indicated by integer.
        Returns:
            - output (:obj:`Dict[int, Any]`): Dict type data, including at least inferred action according to input obs.
        ReturnsKeys
            - necessary: ``action``
        """
        data_id = list(data.keys())
        data = default_collate(list(data.values()))
        if self._cuda:
            data = to_device(data, self._device)
        self._collect_model.eval()
        with torch.no_grad():
            output = self._collect_model.forward(data, mode='compute_actor', get_delta_q=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 _process_transition(self, obs: Any, model_output: dict, timestep: namedtuple) -> Dict[str, Any]:
        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']
            - timestep (:obj:`namedtuple`): Output after env step, including at least ['obs', 'reward', 'done'] \
                (here 'obs' indicates obs after env step, i.e. next_obs).
        Return:
            - transition (:obj:`Dict[str, Any]`): Dict type transition data.
        """
        if self._communication:
            transition = {
                'obs': obs,
                'next_obs': timestep.obs,
                'action': model_output['action'],
                'delta_q': model_output['delta_q'],
                'reward': timestep.reward,
                'done': timestep.done,
            }
        else:
            transition = {
                'obs': obs,
                'next_obs': timestep.obs,
                'action': model_output['action'],
                'reward': timestep.reward,
                'done': timestep.done,
            }
        return transition

    def _get_train_sample(self, data: list) -> Union[None, List[Any]]:
        if self._communication:
            delta_q_batch = [d['delta_q'] for d in data]
            delta_min = torch.stack(delta_q_batch).min()
            delta_max = torch.stack(delta_q_batch).max()
            for i in range(len(data)):
                data[i]['delta_q'] = (data[i]['delta_q'] - delta_min) / (delta_max - delta_min + 1e-8)
        return get_train_sample(data, self._unroll_len)

    def _init_eval(self) -> None:
        r"""
        Overview:
            Evaluate mode init method. Called by ``self.__init__``.
            Init eval model. Unlike learn and collect model, eval model does not need noise.
        """
        self._eval_model = model_wrap(self._model, wrapper_name='base')
        self._eval_model.reset()

    def _forward_eval(self, data: dict) -> dict:
        r"""
        Overview:
            Forward function of eval mode, similar to ``self._forward_collect``.
        Arguments:
            - data (:obj:`Dict[str, Any]`): Dict type data, stacked env data for predicting policy_output(action), \
                values are torch.Tensor or np.ndarray or dict/list combinations, keys are env_id indicated by integer.
        Returns:
            - output (:obj:`Dict[int, Any]`): The dict of predicting action for the interaction with env.
        ReturnsKeys
            - necessary: ``action``
        """
        data_id = list(data.keys())
        data = default_collate(list(data.values()))
        if self._cuda:
            data = to_device(data, self._device)
        self._eval_model.eval()
        with torch.no_grad():
            output = self._eval_model.forward(data, mode='compute_actor')
        if self._cuda:
            output = to_device(output, 'cpu')
        output = default_decollate(output)
        return {i: d for i, d in zip(data_id, output)}

    def _monitor_vars_learn(self) -> List[str]:
        r"""
        Overview:
            Return variables' name if variables are to used in monitor.
        Returns:
            - vars (:obj:`List[str]`): Variables' name list.
        """
        return [
            'cur_lr_actor',
            'cur_lr_critic',
            'critic_loss',
            'actor_loss',
            'attention_loss',
            'total_loss',
            'q_value',
        ]