from typing import List, Dict, Any, Tuple, Union, Optional from collections import namedtuple import torch import copy from ding.torch_utils import to_device, RMSprop 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 timestep_collate, default_collate, default_decollate from .base_policy import Policy from .common_utils import default_preprocess_learn @POLICY_REGISTRY.register('collaq') class CollaQPolicy(Policy): r""" Overview: Policy class of CollaQ algorithm. CollaQ is a multi-agent reinforcement learning algorithm Interface: _init_learn, _data_preprocess_learn, _forward_learn, _reset_learn, _state_dict_learn, _load_state_dict_learn\ _init_collect, _forward_collect, _reset_collect, _process_transition, _init_eval, _forward_eval\ _reset_eval, _get_train_sample, default_model Config: == ==================== ======== ============== ======================================== ======================= ID Symbol Type Default Value Description Other(Shape) == ==================== ======== ============== ======================================== ======================= 1 ``type`` str collaq | RL policy register name, refer to | this arg is optional, | registry ``POLICY_REGISTRY`` | a placeholder 2 ``cuda`` bool True | 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_`` bool False | Whether use Importance Sampling | IS weight | ``IS_weight`` | Weight to correct biased update. 6 | ``learn.update_`` int 20 | 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 7 | ``learn.target_`` float 0.001 | Target network update momentum | between[0,1] | ``update_theta`` | parameter. 8 | ``learn.discount`` float 0.99 | Reward's future discount factor, aka. | may be 1 when sparse | ``_factor`` | gamma | reward env 9 | ``learn.collaq`` float 1.0 | The weight of collaq MARA loss | ``_loss_weight`` == ==================== ======== ============== ======================================== ======================= """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='collaq', # (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, learn=dict( # (int) Collect n_episode data, update_model n_iteration times update_per_collect=20, # (int) The number of data for a train iteration batch_size=32, # (float) Gradient-descent step size learning_rate=0.0005, # ============================================================== # The following configs is algorithm-specific # ============================================================== # (float) Target network update weight, theta * new_w + (1 - theta) * old_w, defaults in [0, 0.1] target_update_theta=0.001, # (float) Discount factor for future reward, defaults int [0, 1] discount_factor=0.99, # (float) The weight of collaq MARA loss collaq_loss_weight=1.0, # (float) clip_value=100, # (bool) Whether to use double DQN mechanism(target q for surpassing over estimation) double_q=False, ), 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=200000, ), replay_buffer=dict( # (int) max size of replay buffer replay_buffer_size=5000, max_reuse=10, ), ), ) 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 .. note:: The user can define and use customized network model but must obey the same inferface definition indicated \ by import_names path. For collaq, ``ding.model.collaq.CollaQ`` . """ return 'collaq', ['ding.model.template.collaq'] def _init_learn(self) -> None: """ Overview: Learn mode init method. Called by ``self.__init__``. Init the learner model of CollaQPolicy 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 - alpha (:obj:`float`): The collaQ loss factor, the weight for calculating MARL loss - agent_num (:obj:`int`): Since this is a multi-agent algorithm, we need to input the agent num. - batch_size (:obj:`int`): Need batch size info to init hidden_state plugins """ self._priority = self._cfg.priority self._priority_IS_weight = self._cfg.priority_IS_weight self._optimizer = RMSprop( params=self._model.parameters(), lr=self._cfg.learn.learning_rate, alpha=0.99, eps=0.00001 ) self._gamma = self._cfg.learn.discount_factor self._alpha = self._cfg.learn.collaq_loss_weight 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)] for _ in range(3)] ) 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)] for _ in range(3)] ) self._learn_model.reset() self._target_model.reset() def _data_preprocess_learn( self, data: List[Any], use_priority_IS_weight: bool = False, use_priority: bool = False, ) -> 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) if use_priority_IS_weight: assert use_priority, "Use IS Weight correction, but Priority is not used." if use_priority and use_priority_IS_weight: if 'priority_IS' in data: data['weight'] = data['priority_IS'] else: # for compability data['weight'] = data['IS'] else: 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, self.cfg.priority_IS_weight, self.cfg.priority) # ==================== # CollaQ 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']} ret = self._learn_model.forward(inputs, single_step=False) total_q = ret['total_q'] agent_colla_alone_q = ret['agent_colla_alone_q'].sum(-1).sum(-1) if self._cfg.learn.double_q: next_inputs = {'obs': data['next_obs']} 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, single_step=False)['total_q'] # td_loss calculation td_data = v_1step_td_data(total_q, target_total_q, data['reward'], data['done'], data['weight']) td_loss, td_error_per_sample = v_1step_td_error(td_data, self._gamma) # collaQ loss calculation colla_loss = (agent_colla_alone_q ** 2).mean() # combine loss with factor loss = colla_loss * self._alpha + td_loss # ==================== # CollaQ update # ==================== self._optimizer.zero_grad() loss.backward() grad_norm = torch.nn.utils.clip_grad_norm_(self._model.parameters(), self._cfg.learn.clip_value) self._optimizer.step() # ============= # after update # ============= self._target_model.update(self._learn_model.state_dict()) return { 'cur_lr': self._optimizer.defaults['lr'], 'total_loss': loss.item(), 'colla_loss': colla_loss.item(), 'td_loss': td_loss.item(), 'grad_norm': grad_norm, 'priority': torch.mean(td_error_per_sample.abs(), dim=0).tolist(), } 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': self._optimizer.state_dict(), } def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None: r""" 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.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. Enable the eps_greedy_sample and the hidden_state plugin. """ self._unroll_len = self._cfg.collect.unroll_len self._collect_model = model_wrap( self._model, wrapper_name='hidden_state', state_num=self._cfg.collect.env_num, save_prev_state=True, init_fn=lambda: [[None for _ in range(self._cfg.model.agent_num)] for _ in range(3)] ) 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: Forward function for collect mode with eps_greedy 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. - eps (:obj:`float`): epsilon value for exploration, which is decayed by collected env step. 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) data = {'obs': data} self._collect_model.eval() with torch.no_grad(): output = self._collect_model.forward(data, eps=eps, data_id=data_id) 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: r""" Overview: Reset collect 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._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', 'agent_colla_alone_q'] - 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. """ transition = { 'obs': obs, 'next_obs': timestep.obs, 'prev_state': model_output['prev_state'], 'action': model_output['action'], 'agent_colla_alone_q': model_output['agent_colla_alone_q'], 'reward': timestep.reward, 'done': timestep.done, } return transition def _init_eval(self) -> None: r""" Overview: Evaluate mode init method. Called by ``self.__init__``. Init eval model with argmax strategy and the hidden_state plugin. """ self._eval_model = model_wrap( self._model, wrapper_name='hidden_state', state_num=self._cfg.eval.env_num, save_prev_state=True, init_fn=lambda: [[None for _ in range(self._cfg.model.agent_num)] for _ in range(3)] ) 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 for 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) data = {'obs': data} self._eval_model.eval() with torch.no_grad(): output = self._eval_model.forward(data, data_id=data_id) 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: r""" Overview: Reset eval 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._eval_model.reset(data_id=data_id) 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 """ 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', 'colla_loss', 'td_loss', 'grad_norm']