from typing import List, Dict, Any, Tuple, Union, Optional from collections import namedtuple import torch import copy from ding.torch_utils import RMSprop, to_device from ding.rl_utils import v_1step_td_data, v_1step_td_error, get_train_sample, \ v_nstep_td_data, v_nstep_td_error, get_nstep_return_data from ding.model import model_wrap from ding.utils import POLICY_REGISTRY from ding.utils.data import timestep_collate, default_collate, default_decollate from .qmix import QMIXPolicy @POLICY_REGISTRY.register('madqn') class MADQNPolicy(QMIXPolicy): config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='madqn', # (bool) Whether to use cuda for network. cuda=True, # (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=False, # (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True. priority_IS_weight=False, nstep=3, learn=dict( update_per_collect=20, batch_size=32, learning_rate=0.0005, clip_value=100, # ============================================================== # The following configs is algorithm-specific # ============================================================== # (float) Target network update momentum parameter. # in [0, 1]. target_update_theta=0.008, # (float) The discount factor for future rewards, # in [0, 1]. discount_factor=0.99, # (bool) Whether to use double DQN mechanism(target q for surpassing over estimation) double_q=False, weight_decay=1e-5, ), collect=dict( # (int) Only one of [n_sample, n_episode] shoule be set n_episode=32, # (int) Cut trajectories into pieces with length "unroll_len", the length of timesteps # in each forward when training. In qmix, it is greater than 1 because there is RNN. unroll_len=10, ), eval=dict(), other=dict( eps=dict( # (str) Type of epsilon decay type='exp', # (float) Start value for epsilon decay, in [0, 1]. # 0 means not use epsilon decay. start=1, # (float) Start value for epsilon decay, in [0, 1]. end=0.05, # (int) Decay length(env step) decay=50000, ), replay_buffer=dict( replay_buffer_size=5000, # (int) The maximum reuse times of each data max_reuse=1e+9, max_staleness=1e+9, ), ), ) def default_model(self) -> Tuple[str, List[str]]: """ Overview: Return this algorithm default model setting for demonstration. Returns: - model_info (:obj:`Tuple[str, List[str]]`): model name and mode import_names """ return 'madqn', ['ding.model.template.madqn'] def _init_learn(self) -> None: self._priority = self._cfg.priority self._priority_IS_weight = self._cfg.priority_IS_weight assert not self._priority and not self._priority_IS_weight, "Priority is not implemented in QMIX" self._optimizer_current = RMSprop( params=self._model.current.parameters(), lr=self._cfg.learn.learning_rate, alpha=0.99, eps=0.00001, weight_decay=self._cfg.learn.weight_decay ) self._optimizer_cooperation = RMSprop( params=self._model.cooperation.parameters(), lr=self._cfg.learn.learning_rate, alpha=0.99, eps=0.00001, weight_decay=self._cfg.learn.weight_decay ) self._gamma = self._cfg.learn.discount_factor self._nstep = self._cfg.nstep 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, init_fn=lambda: [None for _ in range(self._cfg.model.agent_num)] ) self._learn_model = model_wrap( self._model, wrapper_name='hidden_state', state_num=self._cfg.learn.batch_size, init_fn=lambda: [None for _ in range(self._cfg.model.agent_num)] ) self._learn_model.reset() self._target_model.reset() def _data_preprocess_learn(self, data: List[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, from \ [len=B, ele={dict_key: [len=T, ele=Tensor(any_dims)]}] -> {dict_key: Tensor([T, B, any_dims])} """ # data preprocess data = timestep_collate(data) if self._cuda: data = to_device(data, self._device) data['weight'] = data.get('weight', None) data['done'] = data['done'].float() return data def _forward_learn(self, data: dict) -> Dict[str, Any]: r""" Overview: Forward and backward function of learn mode. Arguments: - data (:obj:`Dict[str, Any]`): Dict type data, a batch of data for training, values are torch.Tensor or \ np.ndarray or dict/list combinations. Returns: - info_dict (:obj:`Dict[str, Any]`): Dict type data, a info dict indicated training result, which will be \ recorded in text log and tensorboard, values are python scalar or a list of scalars. ArgumentsKeys: - necessary: ``obs``, ``next_obs``, ``action``, ``reward``, ``weight``, ``prev_state``, ``done`` ReturnsKeys: - necessary: ``cur_lr``, ``total_loss`` - cur_lr (:obj:`float`): Current learning rate - total_loss (:obj:`float`): The calculated loss """ data = self._data_preprocess_learn(data) # ==================== # Q-mix forward # ==================== self._learn_model.train() self._target_model.train() # for hidden_state plugin, we need to reset the main model and target model self._learn_model.reset(state=data['prev_state'][0]) self._target_model.reset(state=data['prev_state'][0]) inputs = {'obs': data['obs'], 'action': data['action']} total_q = self._learn_model.forward(inputs, single_step=False)['total_q'] if self._cfg.learn.double_q: next_inputs = {'obs': data['next_obs']} self._learn_model.reset(state=data['prev_state'][1]) logit_detach = self._learn_model.forward(next_inputs, single_step=False)['logit'].clone().detach() next_inputs = {'obs': data['next_obs'], 'action': logit_detach.argmax(dim=-1)} else: next_inputs = {'obs': data['next_obs']} with torch.no_grad(): target_total_q = self._target_model.forward(next_inputs, cooperation=True, single_step=False)['total_q'] if self._nstep == 1: v_data = v_1step_td_data(total_q, target_total_q, data['reward'], data['done'], data['weight']) loss, td_error_per_sample = v_1step_td_error(v_data, self._gamma) # for visualization with torch.no_grad(): if data['done'] is not None: target_v = self._gamma * (1 - data['done']) * target_total_q + data['reward'] else: target_v = self._gamma * target_total_q + data['reward'] else: data['reward'] = data['reward'].permute(0, 2, 1).contiguous() loss = [] td_error_per_sample = [] for t in range(self._cfg.collect.unroll_len): v_data = v_nstep_td_data( total_q[t], target_total_q[t], data['reward'][t], data['done'][t], data['weight'], self._gamma ) # calculate v_nstep_td critic_loss loss_i, td_error_per_sample_i = v_nstep_td_error(v_data, self._gamma, self._nstep) loss.append(loss_i) td_error_per_sample.append(td_error_per_sample_i) loss = sum(loss) / (len(loss) + 1e-8) td_error_per_sample = sum(td_error_per_sample) / (len(td_error_per_sample) + 1e-8) self._optimizer_current.zero_grad() loss.backward() grad_norm = torch.nn.utils.clip_grad_norm_(self._model.current.parameters(), self._cfg.learn.clip_value) self._optimizer_current.step() # cooperation self._learn_model.reset(state=data['prev_state'][0]) self._target_model.reset(state=data['prev_state'][0]) cooperation_total_q = self._learn_model.forward(inputs, cooperation=True, single_step=False)['total_q'] next_inputs = {'obs': data['next_obs']} with torch.no_grad(): cooperation_target_total_q = self._target_model.forward( next_inputs, cooperation=True, single_step=False )['total_q'] if self._nstep == 1: v_data = v_1step_td_data( cooperation_total_q, cooperation_target_total_q, data['reward'], data['done'], data['weight'] ) cooperation_loss, _ = v_1step_td_error(v_data, self._gamma) else: cooperation_loss_all = [] for t in range(self._cfg.collect.unroll_len): v_data = v_nstep_td_data( cooperation_total_q[t], cooperation_target_total_q[t], data['reward'][t], data['done'][t], data['weight'], self._gamma ) cooperation_loss, _ = v_nstep_td_error(v_data, self._gamma, self._nstep) cooperation_loss_all.append(cooperation_loss) cooperation_loss = sum(cooperation_loss_all) / (len(cooperation_loss_all) + 1e-8) self._optimizer_cooperation.zero_grad() cooperation_loss.backward() cooperation_grad_norm = torch.nn.utils.clip_grad_norm_( self._model.cooperation.parameters(), self._cfg.learn.clip_value ) self._optimizer_cooperation.step() # ============= # after update # ============= self._target_model.update(self._learn_model.state_dict()) return { 'cur_lr': self._optimizer_current.defaults['lr'], 'total_loss': loss.item(), 'total_q': total_q.mean().item() / self._cfg.model.agent_num, 'target_total_q': target_total_q.mean().item() / self._cfg.model.agent_num, 'grad_norm': grad_norm, 'cooperation_grad_norm': cooperation_grad_norm, 'cooperation_loss': cooperation_loss.item(), } def _reset_learn(self, data_id: Optional[List[int]] = None) -> None: r""" Overview: Reset learn model to the state indicated by data_id Arguments: - data_id (:obj:`Optional[List[int]]`): The id that store the state and we will reset\ the model state to the state indicated by data_id """ self._learn_model.reset(data_id=data_id) def _state_dict_learn(self) -> Dict[str, Any]: r""" Overview: Return the state_dict of learn mode, usually including model and optimizer. Returns: - state_dict (:obj:`Dict[str, Any]`): the dict of current policy learn state, for saving and restoring. """ return { 'model': self._learn_model.state_dict(), 'target_model': self._target_model.state_dict(), 'optimizer_current': self._optimizer_current.state_dict(), 'optimizer_cooperation': self._optimizer_cooperation.state_dict(), } def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None: """ Overview: Load the state_dict variable into policy learn mode. Arguments: - state_dict (:obj:`Dict[str, Any]`): the dict of policy learn state saved before. .. tip:: If you want to only load some parts of model, you can simply set the ``strict`` argument in \ load_state_dict to ``False``, or refer to ``ding.torch_utils.checkpoint_helper`` for more \ complicated operation. """ self._learn_model.load_state_dict(state_dict['model']) self._target_model.load_state_dict(state_dict['target_model']) self._optimizer_current.load_state_dict(state_dict['optimizer_current']) self._optimizer_cooperation.load_state_dict(state_dict['optimizer_cooperation']) 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 ['obs', 'reward', 'done']\ (here 'obs' indicates obs after env step). Returns: - transition (:obj:`dict`): Dict type transition data, including 'obs', 'next_obs', 'prev_state',\ 'action', 'reward', 'done' """ transition = { 'obs': obs, 'next_obs': timestep.obs, 'prev_state': model_output['prev_state'], 'action': model_output['action'], 'reward': timestep.reward, 'done': timestep.done, } return transition def _get_train_sample(self, data: list) -> Union[None, List[Any]]: r""" Overview: Get the train sample from trajectory. Arguments: - data (:obj:`list`): The trajectory's cache Returns: - samples (:obj:`dict`): The training samples generated """ if self._cfg.nstep == 1: return get_train_sample(data, self._unroll_len) else: data = get_nstep_return_data(data, self._nstep, gamma=self._gamma) return get_train_sample(data, self._unroll_len) 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', 'total_loss', 'total_q', 'target_total_q', 'grad_norm', 'target_reward_total_q', 'cooperation_grad_norm', 'cooperation_loss' ]