import copy import torch from collections import namedtuple from typing import List, Dict, Any, Tuple, Union, Optional from ding.model import model_wrap 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.torch_utils import Adam, to_device 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('r2d2_gtrxl') class R2D2GTrXLPolicy(Policy): r""" Overview: Policy class of R2D2 adopting the Transformer architecture GTrXL as backbone. Config: == ==================== ======== ============== ======================================== ======================= ID Symbol Type Default Value Description Other(Shape) == ==================== ======== ============== ======================================== ======================= 1 ``type`` str r2d2_gtrxl | 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 0.99, | Reward's future discount factor, aka. | May be 1 when sparse | ``factor`` [0.95, 0.999] | gamma | reward env 7 | ``nstep`` int 5, | N-step reward discount sum for target [3, 5] | q_value estimation 8 | ``burnin_step`` int 1 | The timestep of burnin operation, | which is designed to warm-up GTrXL | memory 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 25 | unroll length of an iteration | unroll_len>1 | ``_len`` 17 | ``collect.seq`` int 20 | Training sequence length | unroll_len>=seq_len>1 | ``_len`` 18 | ``learn.init_`` str zero | 'zero' or 'old', how to initialize the | | ``memory`` | memory before each training iteration. | == ==================== ======== ============== ======================================== ======================= """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='r2d2_gtrxl', # (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=0.99, # (int) N-step reward for target q_value estimation nstep=5, # how many steps to use as burnin burnin_step=1, # (int) trajectory length unroll_len=25, # (int) training sequence length seq_len=20, learn=dict( update_per_collect=1, batch_size=64, learning_rate=0.0001, # ============================================================== # The following configs are algorithm-specific # ============================================================== # (int) Frequence of target network update. # target_update_freq=100, target_update_theta=0.001, ignore_done=False, # (bool) whether use value_rescale function for predicted value value_rescale=False, # 'zero' or 'old', how to initialize the memory in training init_memory='zero' ), collect=dict( # NOTE it is important that don't include key n_sample here, to make sure self._traj_len=INF each_iter_n_sample=32, # `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 'gtrxldqn', ['ding.model.template.q_learning'] def _init_learn(self) -> None: """ Overview: Init the learner model of GTrXLR2D2Policy. \ Target model has 2 wrappers: 'target' for weights update and 'transformer_segment' to split trajectories \ in segments. Learn model has 2 wrappers: 'argmax' to select the best action and 'transformer_segment'. Arguments: - 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 - seq_len (:obj:`int`): Training sequence length - init_memory (:obj:`str`): 'zero' or 'old', how to initialize the memory before each training iteration. .. note:: The ``_init_learn`` method takes the argument from the self._cfg.learn in the config file """ 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._batch_size = self._cfg.learn.batch_size self._seq_len = self._cfg.seq_len self._value_rescale = self._cfg.learn.value_rescale self._init_memory = self._cfg.learn.init_memory assert self._init_memory in ['zero', 'old'] 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, seq_len=self._seq_len, wrapper_name='transformer_segment') self._learn_model = model_wrap(self._model, wrapper_name='argmax_sample') self._learn_model = model_wrap(self._learn_model, seq_len=self._seq_len, wrapper_name='transformer_segment') 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 """ if self._init_memory == 'old' and 'prev_memory' in data[0].keys(): # retrieve the memory corresponding to the first and n_step(th) element in each trajectory and remove it # from 'data' prev_mem = [b['prev_memory'][0] for b in data] prev_mem_target = [b['prev_memory'][self._nstep] for b in data] # stack the memory entries along the batch dimension, # reshape the new memory to have shape (layer_num+1, memory_len, bs, embedding_dim) compatible with GTrXL prev_mem_batch = torch.stack(prev_mem, 0).permute(1, 2, 0, 3) prev_mem_target_batch = torch.stack(prev_mem_target, 0).permute(1, 2, 0, 3) data = timestep_collate(data) data['prev_memory_batch'] = prev_mem_batch data['prev_memory_target_batch'] = prev_mem_target_batch else: 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) # 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._unroll_len] ignore_done = self._cfg.learn.ignore_done if ignore_done: data['done'] = [None for _ in range(self._unroll_len)] else: data['done'] = data['done'].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._unroll_len_add_burnin_step-self._burnin_step], # below is two different implementation ways if 'value_gamma' not in data: data['value_gamma'] = [None for _ in range(self._unroll_len)] else: data['value_gamma'] = data['value_gamma'] if 'weight' not in data or data['weight'] is None: data['weight'] = [None for _ in range(self._unroll_len)] 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 data['action'] = data['action'][:-self._nstep] data['reward'] = data['reward'][:-self._nstep] data['main_obs'] = data['obs'][:-self._nstep] # the target_obs is used to calculate the target_q_value data['target_obs'] = data['obs'][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 """ data = self._data_preprocess_learn(data) # shape (seq_len, bs, obs_dim) self._learn_model.train() self._target_model.train() if self._init_memory == 'old': # use the previous hidden state memory self._learn_model.reset_memory(state=data['prev_memory_batch']) self._target_model.reset_memory(state=data['prev_memory_target_batch']) elif self._init_memory == 'zero': # use the zero-initialized state memory self._learn_model.reset_memory() self._target_model.reset_memory() inputs = data['main_obs'] q_value = self._learn_model.forward(inputs)['logit'] # shape (seq_len, bs, act_dim) next_inputs = data['target_obs'] with torch.no_grad(): target_q_value = self._target_model.forward(next_inputs)['logit'] if self._init_memory == 'old': self._learn_model.reset_memory(state=data['prev_memory_target_batch']) elif self._init_memory == 'zero': self._learn_model.reset_memory() target_q_action = self._learn_model.forward(next_inputs)['action'] # argmax_action double_dqn action, reward, done, weight = data['action'], data['reward'], data['done'], data['weight'] value_gamma = data['value_gamma'] # T, B, nstep -> T, nstep, B reward = reward.permute(0, 2, 1).contiguous() loss = [] td_error = [] for t in range(self._burnin_step, self._unroll_len - self._nstep): # here skip the first 'burnin_step' steps because we only needed that to initialize the memory, and # skip the last 'nstep' steps because 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]) 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(, 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]] ret = { '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(), } return ret def _reset_learn(self, data_id: Optional[List[int]] = None) -> None: self._learn_model.reset(data_id=data_id) self._target_model.reset(data_id=data_id) self._learn_model.reset_memory() self._target_model.reset_memory() def _state_dict_learn(self) -> Dict[str, Any]: return { 'model': self._learn_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._optimizer.load_state_dict(state_dict['optimizer']) def _init_collect(self) -> None: r""" Overview: Collect mode init method. Called by ``self.__init__``. Init unroll length and sequence len, collect model. """ assert 'unroll_len' not in self._cfg.collect, "Use default unroll_len" self._nstep = self._cfg.nstep self._gamma = self._cfg.discount_factor self._unroll_len = self._cfg.unroll_len self._seq_len = self._cfg.seq_len self._collect_model = model_wrap(self._model, wrapper_name='transformer_input', seq_len=self._seq_len) self._collect_model = model_wrap(self._collect_model, wrapper_name='eps_greedy_sample') self._collect_model = model_wrap( self._collect_model, wrapper_name='transformer_memory', batch_size=self.cfg.collect.env_num ) 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) self._collect_model.eval() with torch.no_grad(): output = self._collect_model.forward(data, eps=eps, data_id=data_id) del output['input_seq'] 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: # data_id is ID of env to be reset 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'] - 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. """ transition = { 'obs': obs, 'action': model_output['action'], 'prev_memory': model_output['memory'], # state of the memory before taking the 'action' 'prev_state': None, 'reward': timestep.reward, 'done': timestep.done, } 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 """ self._seq_len = self._cfg.seq_len data = get_nstep_return_data(data, self._nstep, gamma=self._gamma) 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 with argmax strategy. """ self._eval_model = model_wrap(self._model, wrapper_name='transformer_input', seq_len=self._seq_len) self._eval_model = model_wrap(self._eval_model, wrapper_name='argmax_sample') self._eval_model = model_wrap( self._eval_model, wrapper_name='transformer_memory', batch_size=self.cfg.eval.env_num ) 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, 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: 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' ]