from collections import namedtuple from typing import List, Dict, Any, Tuple import copy import torch from ding.model import model_wrap from ding.rl_utils import get_train_sample, compute_q_retraces, acer_policy_error,\ acer_value_error, acer_trust_region_update from ding.torch_utils import Adam, RMSprop, to_device from ding.utils import POLICY_REGISTRY from ding.utils.data import default_collate, default_decollate from ding.policy.base_policy import Policy EPS = 1e-8 @POLICY_REGISTRY.register('acer') class ACERPolicy(Policy): r""" Overview: Policy class of ACER algorithm. Config: == ======================= ======== ============== ===================================== ======================= ID Symbol Type Default Value Description Other(Shape) == ======================= ======== ============== ===================================== ======================= 1 ``type`` str acer | 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 ``trust_region`` bool True | Whether the RL algorithm use trust | | region constraint | 5 ``trust_region_value`` float 1.0 | maximum range of the trust region | 6 ``unroll_len`` int 32 | trajectory length to calculate | Q retrace target 7 ``learn.update`` int 4 | How many updates(iterations) to | this args can be vary ``per_collect`` | train after collector's one | from envs. Bigger val | collection. Only | | valid in serial training | means more off-policy 8 ``c_clip_ratio`` float 1.0 | clip ratio of importance weights | == ======================= ======== ============== ===================================== ======================= """ unroll_len = 32 config = dict( type='acer', cuda=False, # (bool) whether use on-policy training pipeline(behaviour policy and training policy are the same) # here we follow ppo serial pipeline, the original is False on_policy=False, priority=False, # (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True. priority_IS_weight=False, learn=dict( # (str) the type of gradient clip method grad_clip_type=None, # (float) max value when ACER use gradient clip clip_value=None, # (int) collect n_sample data, train model update_per_collect times # here we follow ppo serial pipeline update_per_collect=4, # (int) the number of data for a train iteration batch_size=16, # (float) loss weight of the value network, the weight of policy network is set to 1 value_weight=0.5, # (float) loss weight of the entropy regularization, the weight of policy network is set to 1 entropy_weight=0.0001, # (float) discount factor for future reward, defaults int [0, 1] discount_factor=0.9, # (float) additional discounting parameter lambda_=0.95, # (int) the trajectory length to calculate v-trace target unroll_len=unroll_len, # (float) clip ratio of importance weights c_clip_ratio=10, trust_region=True, trust_region_value=1.0, learning_rate_actor=0.0005, learning_rate_critic=0.0005, target_theta=0.01 ), collect=dict( # (int) collect n_sample data, train model n_iteration times # n_sample=16, # (int) the trajectory length to calculate v-trace target unroll_len=unroll_len, # (float) discount factor for future reward, defaults int [0, 1] discount_factor=0.9, gae_lambda=0.95, collector=dict( type='sample', collect_print_freq=1000, ), ), eval=dict(evaluator=dict(eval_freq=200, ), ), other=dict(replay_buffer=dict( replay_buffer_size=1000, max_use=16, ), ), ) def default_model(self) -> Tuple[str, List[str]]: return 'acer', ['ding.model.template.acer'] def _init_learn(self) -> None: r""" Overview: Learn mode init method. Called by ``self.__init__``. Initialize the optimizer, algorithm config and main model. """ # Optimizer self._optimizer_actor = Adam( self._model.actor.parameters(), lr=self._cfg.learn.learning_rate_actor, grad_clip_type=self._cfg.learn.grad_clip_type, clip_value=self._cfg.learn.clip_value ) self._optimizer_critic = Adam( self._model.critic.parameters(), lr=self._cfg.learn.learning_rate_critic, ) 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} ) self._learn_model = model_wrap(self._model, wrapper_name='base') self._action_shape = self._cfg.model.action_shape self._unroll_len = self._cfg.learn.unroll_len # Algorithm config self._priority = self._cfg.priority self._priority_IS_weight = self._cfg.priority_IS_weight self._value_weight = self._cfg.learn.value_weight self._entropy_weight = self._cfg.learn.entropy_weight self._gamma = self._cfg.learn.discount_factor # self._rho_clip_ratio = self._cfg.learn.rho_clip_ratio self._c_clip_ratio = self._cfg.learn.c_clip_ratio # self._rho_pg_clip_ratio = self._cfg.learn.rho_pg_clip_ratio self._use_trust_region = self._cfg.learn.trust_region self._trust_region_value = self._cfg.learn.trust_region_value # Main model self._learn_model.reset() self._target_model.reset() def _data_preprocess_learn(self, data: List[Dict[str, Any]]): """ Overview: Data preprocess function of learn mode. Convert list trajectory data to to trajectory data, which is a dict of tensors. Arguments: - data (:obj:`List[Dict[str, Any]]`): List type data, a list of data for training. Each list element is a \ dict, whose values are torch.Tensor or np.ndarray or dict/list combinations, keys include at least 'obs',\ 'next_obs', 'logit', 'action', 'reward', 'done' Returns: - data (:obj:`dict`): Dict type data. Values are torch.Tensor or np.ndarray or dict/list combinations. \ ReturnsKeys: - necessary: 'logit', 'action', 'reward', 'done', 'weight', 'obs_plus_1'. - optional and not used in later computation: 'obs', 'next_obs'.'IS', 'collect_iter', 'replay_unique_id', \ 'replay_buffer_idx', 'priority', 'staleness', 'use'. ReturnsShapes: - obs_plus_1 (:obj:`torch.FloatTensor`): :math:`(T * B, obs_shape)`, where T is timestep, B is batch size \ and obs_shape is the shape of single env observation - logit (:obj:`torch.FloatTensor`): :math:`(T, B, N)`, where N is action dim - action (:obj:`torch.LongTensor`): :math:`(T, B)` - reward (:obj:`torch.FloatTensor`): :math:`(T+1, B)` - done (:obj:`torch.FloatTensor`): :math:`(T, B)` - weight (:obj:`torch.FloatTensor`): :math:`(T, B)` """ data = default_collate(data) if self._cuda: data = to_device(data, self._device) data['weight'] = data.get('weight', None) # shape (T+1)*B,env_obs_shape data['obs_plus_1'] = torch.cat((data['obs'] + data['next_obs'][-1:]), dim=0) data['logit'] = torch.cat( data['logit'], dim=0 ).reshape(self._unroll_len, -1, self._action_shape) # shape T,B,env_action_shape data['action'] = torch.cat(data['action'], dim=0).reshape(self._unroll_len, -1) # shape T,B, data['done'] = torch.cat(data['done'], dim=0).reshape(self._unroll_len, -1).float() # shape T,B, data['reward'] = torch.cat(data['reward'], dim=0).reshape(self._unroll_len, -1) # shape T,B, data['weight'] = torch.cat( data['weight'], dim=0 ).reshape(self._unroll_len, -1) if data['weight'] else None # shape T,B return data def _forward_learn(self, data: List[Dict[str, Any]]) -> Dict[str, Any]: r""" Overview: Forward computation graph of learn mode(updating policy). Arguments: - data (:obj:`List[Dict[str, Any]]`): List type data, a list of data for training. Each list element is a \ dict, whose values are torch.Tensor or np.ndarray or dict/list combinations, keys include at least 'obs',\ 'next_obs', 'logit', 'action', 'reward', 'done' 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``, ``action``, ``reward``, ``next_obs``, ``done`` - optional: 'collect_iter', 'replay_unique_id', 'replay_buffer_idx', 'priority', 'staleness', 'use', 'IS' ReturnsKeys: - necessary: ``cur_lr_actor``, ``cur_lr_critic``, ``actor_loss`,``bc_loss``,``policy_loss``,\ ``critic_loss``,``entropy_loss`` """ data = self._data_preprocess_learn(data) self._learn_model.train() action_data = self._learn_model.forward(data['obs_plus_1'], mode='compute_actor') q_value_data = self._learn_model.forward(data['obs_plus_1'], mode='compute_critic') avg_action_data = self._target_model.forward(data['obs_plus_1'], mode='compute_actor') target_logit, behaviour_logit, avg_logit, actions, q_values, rewards, weights = self._reshape_data( action_data, avg_action_data, q_value_data, data ) # shape (T+1),B,env_action_shape target_logit = torch.log_softmax(target_logit, dim=-1) # shape T,B,env_action_shape behaviour_logit = torch.log_softmax(behaviour_logit, dim=-1) # shape (T+1),B,env_action_shape avg_logit = torch.log_softmax(avg_logit, dim=-1) with torch.no_grad(): # shape T,B,env_action_shape ratio = torch.exp(target_logit[0:-1] - behaviour_logit) # shape (T+1),B,1 v_pred = (q_values * torch.exp(target_logit)).sum(-1).unsqueeze(-1) # Calculate retrace q_retraces = compute_q_retraces(q_values, v_pred, rewards, actions, weights, ratio, self._gamma) # the terminal states' weights are 0. it needs to be shift to count valid state weights_ext = torch.ones_like(weights) weights_ext[1:] = weights[0:-1] weights = weights_ext q_retraces = q_retraces[0:-1] # shape T,B,1 q_values = q_values[0:-1] # shape T,B,env_action_shape v_pred = v_pred[0:-1] # shape T,B,1 target_logit = target_logit[0:-1] # shape T,B,env_action_shape avg_logit = avg_logit[0:-1] # shape T,B,env_action_shape total_valid = weights.sum() # 1 # ==================== # policy update # ==================== actor_loss, bc_loss = acer_policy_error( q_values, q_retraces, v_pred, target_logit, actions, ratio, self._c_clip_ratio ) actor_loss = actor_loss * weights.unsqueeze(-1) bc_loss = bc_loss * weights.unsqueeze(-1) dist_new = torch.distributions.categorical.Categorical(logits=target_logit) entropy_loss = (dist_new.entropy() * weights).unsqueeze(-1) # shape T,B,1 total_actor_loss = (actor_loss + bc_loss + self._entropy_weight * entropy_loss).sum() / total_valid self._optimizer_actor.zero_grad() actor_gradients = torch.autograd.grad(-total_actor_loss, target_logit, retain_graph=True) if self._use_trust_region: actor_gradients = acer_trust_region_update( actor_gradients, target_logit, avg_logit, self._trust_region_value ) target_logit.backward(actor_gradients) self._optimizer_actor.step() # ==================== # critic update # ==================== critic_loss = (acer_value_error(q_values, q_retraces, actions) * weights.unsqueeze(-1)).sum() / total_valid self._optimizer_critic.zero_grad() critic_loss.backward() self._optimizer_critic.step() self._target_model.update(self._learn_model.state_dict()) with torch.no_grad(): kl_div = torch.exp(avg_logit) * (avg_logit - target_logit) kl_div = (kl_div.sum(-1) * weights).sum() / total_valid return { 'cur_actor_lr': self._optimizer_actor.defaults['lr'], 'cur_critic_lr': self._optimizer_critic.defaults['lr'], 'actor_loss': (actor_loss.sum() / total_valid).item(), 'bc_loss': (bc_loss.sum() / total_valid).item(), 'policy_loss': total_actor_loss.item(), 'critic_loss': critic_loss.item(), 'entropy_loss': (entropy_loss.sum() / total_valid).item(), 'kl_div': kl_div.item() } def _reshape_data( self, action_data: Dict[str, Any], avg_action_data: Dict[str, Any], q_value_data: Dict[str, Any], data: Dict[str, Any] ) -> Tuple[Any, Any, Any, Any, Any, Any]: r""" Overview: Obtain weights for loss calculating, where should be 0 for done positions Update values and rewards with the weight Arguments: - output (:obj:`Dict[int, Any]`): Dict type data, output of learn_model forward. \ Values are torch.Tensor or np.ndarray or dict/list combinations,keys are value, logit. - data (:obj:`Dict[int, Any]`): Dict type data, input of policy._forward_learn \ Values are torch.Tensor or np.ndarray or dict/list combinations. Keys includes at \ least ['logit', 'action', 'reward', 'done',] Returns: - data (:obj:`Tuple[Any]`): Tuple of target_logit, behaviour_logit, actions, \ values, rewards, weights ReturnsShapes: - target_logit (:obj:`torch.FloatTensor`): :math:`((T+1), B, Obs_Shape)`, where T is timestep,\ B is batch size and Obs_Shape is the shape of single env observation. - behaviour_logit (:obj:`torch.FloatTensor`): :math:`(T, B, N)`, where N is action dim. - avg_action_logit (:obj:`torch.FloatTensor`): :math: `(T+1, B, N)`, where N is action dim. - actions (:obj:`torch.LongTensor`): :math:`(T, B)` - values (:obj:`torch.FloatTensor`): :math:`(T+1, B)` - rewards (:obj:`torch.FloatTensor`): :math:`(T, B)` - weights (:obj:`torch.FloatTensor`): :math:`(T, B)` """ target_logit = action_data['logit'].reshape( self._unroll_len + 1, -1, self._action_shape ) # shape (T+1),B,env_action_shape behaviour_logit = data['logit'] # shape T,B,env_action_shape avg_action_logit = avg_action_data['logit'].reshape( self._unroll_len + 1, -1, self._action_shape ) # shape (T+1),B,env_action_shape actions = data['action'] # shape T,B values = q_value_data['q_value'].reshape( self._unroll_len + 1, -1, self._action_shape ) # shape (T+1),B,env_action_shape rewards = data['reward'] # shape T,B weights_ = 1 - data['done'] # shape T,B weights = torch.ones_like(rewards) # shape T,B weights = weights_ return target_logit, behaviour_logit, avg_action_logit, actions, values, rewards, weights 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(), 'actor_optimizer': self._optimizer_actor.state_dict(), 'critic_optimizer': self._optimizer_critic.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_actor.load_state_dict(state_dict['actor_optimizer']) self._optimizer_critic.load_state_dict(state_dict['critic_optimizer']) def _init_collect(self) -> None: r""" Overview: Collect mode init method. Called by ``self.__init__``, initialize algorithm arguments and collect_model. Use multinomial_sample to choose action. """ self._collect_unroll_len = self._cfg.collect.unroll_len self._collect_model = model_wrap(self._model, wrapper_name='multinomial_sample') self._collect_model.reset() def _forward_collect(self, data: Dict[int, Any]) -> Dict[int, Dict[str, Any]]: r""" Overview: Forward computation graph of collect mode(collect training data). Arguments: - data (:obj:`Dict[int, Any]`): Dict type data, stacked env data for predicting \ action, values are torch.Tensor or np.ndarray or dict/list combinations,keys \ are env_id indicated by integer. Returns: - output (:obj:`Dict[int, Dict[str,Any]]`): Dict of predicting policy_output(logit, action) for each env. ReturnsKeys - necessary: ``logit``, ``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') if self._cuda: output = to_device(output, 'cpu') output = default_decollate(output) output = {i: d for i, d in zip(data_id, output)} return output def _get_train_sample(self, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]: r""" Overview: For a given trajectory(transitions, a list of transition) data, process it into a list of sample that \ can be used for training directly. Arguments: - data (:obj:`List[Dict[str, Any]`): The trajectory data(a list of transition), each element is the same \ format as the return value of ``self._process_transition`` method. Returns: - samples (:obj:`dict`): List of training samples. .. note:: We will vectorize ``process_transition`` and ``get_train_sample`` method in the following release version. \ And the user can customize the this data processing procedure by overriding this two methods and collector \ itself. """ return get_train_sample(data, self._unroll_len) def _process_transition(self, obs: Any, policy_output: Dict[str, Any], timestep: namedtuple) -> Dict[str, Any]: r""" Overview: Generate dict type transition data from inputs. Arguments: - obs (:obj:`Any`): Env observation,can be torch.Tensor or np.ndarray or dict/list combinations. - model_output (:obj:`dict`): Output of collect model, including ['logit','action'] - 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 at least ['obs','next_obs', 'logit',\ 'action','reward', 'done'] """ transition = { 'obs': obs, 'next_obs': timestep.obs, 'logit': policy_output['logit'], 'action': policy_output['action'], 'reward': timestep.reward, 'done': timestep.done, } return transition def _init_eval(self) -> None: r""" Overview: Evaluate mode init method. Called by ``self.__init__``, initialize eval_model, and use argmax_sample to choose action. """ self._eval_model = model_wrap(self._model, wrapper_name='argmax_sample') self._eval_model.reset() def _forward_eval(self, data: Dict[int, Any]) -> Dict[int, Any]: r""" Overview: Forward computation graph of eval mode(evaluate policy performance), at most cases, it is 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`` - optional: ``logit`` """ 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) output = {i: d for i, d in zip(data_id, output)} return output def _monitor_vars_learn(self) -> List[str]: r""" 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 interface definition indicated \ by import_names path. For IMPALA, ``ding.model.interface.IMPALA`` """ return ['actor_loss', 'bc_loss', 'policy_loss', 'critic_loss', 'entropy_loss', 'kl_div']