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Source code for ding.policy.ppo

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

from ding.torch_utils import Adam, to_device, to_dtype, unsqueeze, ContrastiveLoss
from ding.rl_utils import ppo_data, ppo_error, ppo_policy_error, ppo_policy_data, get_gae_with_default_last_value, \
    v_nstep_td_data, v_nstep_td_error, get_nstep_return_data, get_train_sample, gae, gae_data, ppo_error_continuous, \
    get_gae, ppo_policy_error_continuous
from ding.model import model_wrap
from ding.utils import POLICY_REGISTRY, split_data_generator, RunningMeanStd
from ding.utils.data import default_collate, default_decollate
from .base_policy import Policy
from .common_utils import default_preprocess_learn


[docs]@POLICY_REGISTRY.register('ppo') class PPOPolicy(Policy): """ Overview: Policy class of on-policy version PPO algorithm. Paper link: https://arxiv.org/abs/1707.06347. """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='ppo', # (bool) Whether to use cuda for network. cuda=False, # (bool) Whether the RL algorithm is on-policy or off-policy. (Note: in practice PPO can be off-policy used) on_policy=True, # (bool) Whether to use priority (priority sample, IS weight, update priority). priority=False, # (bool) Whether to use Importance Sampling Weight to correct biased update due to priority. # If True, priority must be True. priority_IS_weight=False, # (bool) Whether to recompurete advantages in each iteration of on-policy PPO. recompute_adv=True, # (str) Which kind of action space used in PPOPolicy, ['discrete', 'continuous', 'hybrid'] action_space='discrete', # (bool) Whether to use nstep return to calculate value target, otherwise, use return = adv + value. nstep_return=False, # (bool) Whether to enable multi-agent training, i.e.: MAPPO. multi_agent=False, # (bool) Whether to need policy ``_forward_collect`` output data in process transition. transition_with_policy_data=True, # learn_mode config learn=dict( # (int) After collecting n_sample/n_episode data, how many epoches to train models. # Each epoch means the one entire passing of training data. epoch_per_collect=10, # (int) How many samples in a training batch. batch_size=64, # (float) The step size of gradient descent. learning_rate=3e-4, # (dict or None) The learning rate decay. # If not None, should contain key 'epoch_num' and 'min_lr_lambda'. # where 'epoch_num' is the total epoch num to decay the learning rate to min value, # 'min_lr_lambda' is the final decayed learning rate. lr_scheduler=None, # (float) The loss weight of value network, policy network weight is set to 1. value_weight=0.5, # (float) The loss weight of entropy regularization, policy network weight is set to 1. entropy_weight=0.0, # (float) PPO clip ratio, defaults to 0.2. clip_ratio=0.2, # (bool) Whether to use advantage norm in a whole training batch. adv_norm=True, # (bool) Whether to use value norm with running mean and std in the whole training process. value_norm=True, # (bool) Whether to enable special network parameters initialization scheme in PPO, such as orthogonal init. ppo_param_init=True, # (str) The gradient clip operation type used in PPO, ['clip_norm', clip_value', 'clip_momentum_norm']. grad_clip_type='clip_norm', # (float) The gradient clip target value used in PPO. # If ``grad_clip_type`` is 'clip_norm', then the maximum of gradient will be normalized to this value. grad_clip_value=0.5, # (bool) Whether ignore done (usually for max step termination env). ignore_done=False, ), # collect_mode config collect=dict( # (int) How many training samples collected in one collection procedure. # Only one of [n_sample, n_episode] should be set. # n_sample=64, # (int) Split episodes or trajectories into pieces with length `unroll_len`. unroll_len=1, # (float) Reward's future discount factor, aka. gamma. discount_factor=0.99, # (float) GAE lambda factor for the balance of bias and variance(1-step td and mc) gae_lambda=0.95, ), eval=dict(), # for compability )
[docs] def default_model(self) -> Tuple[str, List[str]]: """ Overview: Return this algorithm default neural network model setting for demonstration. ``__init__`` method will \ automatically call this method to get the default model setting and create model. Returns: - model_info (:obj:`Tuple[str, List[str]]`): The registered model name and model's 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 example about PPO, its registered name is ``ppo`` and the import_names is \ ``ding.model.template.vac``. .. note:: Because now PPO supports both single-agent and multi-agent usages, so we can implement these functions \ with the same policy and two different default models, which is controled by ``self._cfg.multi_agent``. """ if self._cfg.multi_agent: return 'mavac', ['ding.model.template.mavac'] else: return 'vac', ['ding.model.template.vac']
[docs] def _init_learn(self) -> None: """ Overview: Initialize the learn mode of policy, including related attributes and modules. For PPO, it mainly contains \ optimizer, algorithm-specific arguments such as loss weight, clip_ratio and recompute_adv. This method \ also executes some special network initializations and prepares running mean/std monitor for value. This method will be called in ``__init__`` method if ``learn`` field is in ``enable_field``. .. note:: For the member variables that need to be saved and loaded, please refer to the ``_state_dict_learn`` \ and ``_load_state_dict_learn`` methods. .. note:: For the member variables that need to be monitored, please refer to the ``_monitor_vars_learn`` method. .. note:: If you want to set some spacial member variables in ``_init_learn`` method, you'd better name them \ with prefix ``_learn_`` to avoid conflict with other modes, such as ``self._learn_attr1``. """ 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 PPO" assert self._cfg.action_space in ["continuous", "discrete", "hybrid"] self._action_space = self._cfg.action_space if self._cfg.learn.ppo_param_init: for n, m in self._model.named_modules(): if isinstance(m, torch.nn.Linear): torch.nn.init.orthogonal_(m.weight) torch.nn.init.zeros_(m.bias) if self._action_space in ['continuous', 'hybrid']: # init log sigma if self._action_space == 'continuous': if hasattr(self._model.actor_head, 'log_sigma_param'): torch.nn.init.constant_(self._model.actor_head.log_sigma_param, -0.5) elif self._action_space == 'hybrid': # actor_head[1]: ReparameterizationHead, for action_args if hasattr(self._model.actor_head[1], 'log_sigma_param'): torch.nn.init.constant_(self._model.actor_head[1].log_sigma_param, -0.5) for m in list(self._model.critic.modules()) + list(self._model.actor.modules()): if isinstance(m, torch.nn.Linear): # orthogonal initialization torch.nn.init.orthogonal_(m.weight, gain=np.sqrt(2)) torch.nn.init.zeros_(m.bias) # do last policy layer scaling, this will make initial actions have (close to) # 0 mean and std, and will help boost performances, # see https://arxiv.org/abs/2006.05990, Fig.24 for details for m in self._model.actor.modules(): if isinstance(m, torch.nn.Linear): torch.nn.init.zeros_(m.bias) m.weight.data.copy_(0.01 * m.weight.data) # Optimizer self._optimizer = Adam( self._model.parameters(), lr=self._cfg.learn.learning_rate, grad_clip_type=self._cfg.learn.grad_clip_type, clip_value=self._cfg.learn.grad_clip_value ) # Define linear lr scheduler if self._cfg.learn.lr_scheduler is not None: epoch_num = self._cfg.learn.lr_scheduler['epoch_num'] min_lr_lambda = self._cfg.learn.lr_scheduler['min_lr_lambda'] self._lr_scheduler = torch.optim.lr_scheduler.LambdaLR( self._optimizer, lr_lambda=lambda epoch: max(1.0 - epoch * (1.0 - min_lr_lambda) / epoch_num, min_lr_lambda) ) self._learn_model = model_wrap(self._model, wrapper_name='base') # Algorithm config self._value_weight = self._cfg.learn.value_weight self._entropy_weight = self._cfg.learn.entropy_weight self._clip_ratio = self._cfg.learn.clip_ratio self._adv_norm = self._cfg.learn.adv_norm self._value_norm = self._cfg.learn.value_norm if self._value_norm: self._running_mean_std = RunningMeanStd(epsilon=1e-4, device=self._device) self._gamma = self._cfg.collect.discount_factor self._gae_lambda = self._cfg.collect.gae_lambda self._recompute_adv = self._cfg.recompute_adv # Main model self._learn_model.reset()
[docs] def _forward_learn(self, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """ Overview: Policy forward function of learn mode (training policy and updating parameters). Forward means \ that the policy inputs some training batch data from the replay buffer and then returns the output \ result, including various training information such as loss, clipfrac, approx_kl. Arguments: - data (:obj:`List[Dict[int, Any]]`): The input data used for policy forward, including the latest \ collected training samples for on-policy algorithms like PPO. For each element in list, the key of the \ dict is the name of data items and the value is the corresponding data. Usually, the value is \ torch.Tensor or np.ndarray or there dict/list combinations. In the ``_forward_learn`` method, data \ often need to first be stacked in the batch dimension by some utility functions such as \ ``default_preprocess_learn``. \ For PPO, each element in list is a dict containing at least the following keys: ``obs``, ``action``, \ ``reward``, ``logit``, ``value``, ``done``. Sometimes, it also contains other keys such as ``weight``. Returns: - return_infos (:obj:`List[Dict[str, Any]]`): The information list that indicated training result, each \ training iteration contains append a information dict into the final list. The list will be precessed \ and recorded in text log and tensorboard. The value of the dict must be python scalar or a list of \ scalars. For the detailed definition of the dict, refer to the code of ``_monitor_vars_learn`` method. .. tip:: The training procedure of PPO is two for loops. The outer loop trains all the collected training samples \ with ``epoch_per_collect`` epochs. The inner loop splits all the data into different mini-batch with \ the length of ``batch_size``. .. note:: The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ For the data type that not supported, the main reason is that the corresponding model does not support it. \ You can implement you own model rather than use the default model. For more information, please raise an \ issue in GitHub repo and we will continue to follow up. .. note:: For more detailed examples, please refer to our unittest for PPOPolicy: ``ding.policy.tests.test_ppo``. """ data = default_preprocess_learn(data, ignore_done=self._cfg.learn.ignore_done, use_nstep=False) if self._cuda: data = to_device(data, self._device) data['obs'] = to_dtype(data['obs'], torch.float32) if 'next_obs' in data: data['next_obs'] = to_dtype(data['next_obs'], torch.float32) # ==================== # PPO forward # ==================== return_infos = [] self._learn_model.train() for epoch in range(self._cfg.learn.epoch_per_collect): if self._recompute_adv: # calculate new value using the new updated value network with torch.no_grad(): value = self._learn_model.forward(data['obs'], mode='compute_critic')['value'] next_value = self._learn_model.forward(data['next_obs'], mode='compute_critic')['value'] if self._value_norm: value *= self._running_mean_std.std next_value *= self._running_mean_std.std traj_flag = data.get('traj_flag', None) # traj_flag indicates termination of trajectory compute_adv_data = gae_data(value, next_value, data['reward'], data['done'], traj_flag) data['adv'] = gae(compute_adv_data, self._gamma, self._gae_lambda) unnormalized_returns = value + data['adv'] if self._value_norm: data['value'] = value / self._running_mean_std.std data['return'] = unnormalized_returns / self._running_mean_std.std self._running_mean_std.update(unnormalized_returns.cpu().numpy()) else: data['value'] = value data['return'] = unnormalized_returns else: # don't recompute adv if self._value_norm: unnormalized_return = data['adv'] + data['value'] * self._running_mean_std.std data['return'] = unnormalized_return / self._running_mean_std.std self._running_mean_std.update(unnormalized_return.cpu().numpy()) else: data['return'] = data['adv'] + data['value'] for batch in split_data_generator(data, self._cfg.learn.batch_size, shuffle=True): output = self._learn_model.forward(batch['obs'], mode='compute_actor_critic') adv = batch['adv'] if self._adv_norm: # Normalize advantage in a train_batch adv = (adv - adv.mean()) / (adv.std() + 1e-8) # Calculate ppo error if self._action_space == 'continuous': ppo_batch = ppo_data( output['logit'], batch['logit'], batch['action'], output['value'], batch['value'], adv, batch['return'], batch['weight'] ) ppo_loss, ppo_info = ppo_error_continuous(ppo_batch, self._clip_ratio) elif self._action_space == 'discrete': ppo_batch = ppo_data( output['logit'], batch['logit'], batch['action'], output['value'], batch['value'], adv, batch['return'], batch['weight'] ) ppo_loss, ppo_info = ppo_error(ppo_batch, self._clip_ratio) elif self._action_space == 'hybrid': # discrete part (discrete policy loss and entropy loss) ppo_discrete_batch = ppo_policy_data( output['logit']['action_type'], batch['logit']['action_type'], batch['action']['action_type'], adv, batch['weight'] ) ppo_discrete_loss, ppo_discrete_info = ppo_policy_error(ppo_discrete_batch, self._clip_ratio) # continuous part (continuous policy loss and entropy loss, value loss) ppo_continuous_batch = ppo_data( output['logit']['action_args'], batch['logit']['action_args'], batch['action']['action_args'], output['value'], batch['value'], adv, batch['return'], batch['weight'] ) ppo_continuous_loss, ppo_continuous_info = ppo_error_continuous( ppo_continuous_batch, self._clip_ratio ) # sum discrete and continuous loss ppo_loss = type(ppo_continuous_loss)( ppo_continuous_loss.policy_loss + ppo_discrete_loss.policy_loss, ppo_continuous_loss.value_loss, ppo_continuous_loss.entropy_loss + ppo_discrete_loss.entropy_loss ) ppo_info = type(ppo_continuous_info)( max(ppo_continuous_info.approx_kl, ppo_discrete_info.approx_kl), max(ppo_continuous_info.clipfrac, ppo_discrete_info.clipfrac) ) wv, we = self._value_weight, self._entropy_weight total_loss = ppo_loss.policy_loss + wv * ppo_loss.value_loss - we * ppo_loss.entropy_loss self._optimizer.zero_grad() total_loss.backward() self._optimizer.step() if self._cfg.learn.lr_scheduler is not None: cur_lr = sum(self._lr_scheduler.get_last_lr()) / len(self._lr_scheduler.get_last_lr()) else: cur_lr = self._optimizer.defaults['lr'] return_info = { 'cur_lr': cur_lr, 'total_loss': total_loss.item(), 'policy_loss': ppo_loss.policy_loss.item(), 'value_loss': ppo_loss.value_loss.item(), 'entropy_loss': ppo_loss.entropy_loss.item(), 'adv_max': adv.max().item(), 'adv_mean': adv.mean().item(), 'value_mean': output['value'].mean().item(), 'value_max': output['value'].max().item(), 'approx_kl': ppo_info.approx_kl, 'clipfrac': ppo_info.clipfrac, } if self._action_space == 'continuous': return_info.update( { 'act': batch['action'].float().mean().item(), 'mu_mean': output['logit']['mu'].mean().item(), 'sigma_mean': output['logit']['sigma'].mean().item(), } ) return_infos.append(return_info) if self._cfg.learn.lr_scheduler is not None: self._lr_scheduler.step() return return_infos
[docs] def _init_collect(self) -> None: """ Overview: Initialize the collect mode of policy, including related attributes and modules. For PPO, it contains the \ collect_model to balance the exploration and exploitation (e.g. the multinomial sample mechanism in \ discrete action space), and other algorithm-specific arguments such as unroll_len and gae_lambda. This method will be called in ``__init__`` method if ``collect`` field is in ``enable_field``. .. note:: If you want to set some spacial member variables in ``_init_collect`` method, you'd better name them \ with prefix ``_collect_`` to avoid conflict with other modes, such as ``self._collect_attr1``. .. tip:: Some variables need to initialize independently in different modes, such as gamma and gae_lambda in PPO. \ This design is for the convenience of parallel execution of different policy modes. """ self._unroll_len = self._cfg.collect.unroll_len assert self._cfg.action_space in ["continuous", "discrete", "hybrid"], self._cfg.action_space self._action_space = self._cfg.action_space if self._action_space == 'continuous': self._collect_model = model_wrap(self._model, wrapper_name='reparam_sample') elif self._action_space == 'discrete': self._collect_model = model_wrap(self._model, wrapper_name='multinomial_sample') elif self._action_space == 'hybrid': self._collect_model = model_wrap(self._model, wrapper_name='hybrid_reparam_multinomial_sample') self._collect_model.reset() self._gamma = self._cfg.collect.discount_factor self._gae_lambda = self._cfg.collect.gae_lambda self._recompute_adv = self._cfg.recompute_adv
[docs] def _forward_collect(self, data: Dict[int, Any]) -> Dict[int, Any]: """ Overview: Policy forward function of collect mode (collecting training data by interacting with envs). Forward means \ that the policy gets some necessary data (mainly observation) from the envs and then returns the output \ data, such as the action to interact with the envs. Arguments: - data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs. The \ key of the dict is environment id and the value is the corresponding data of the env. Returns: - output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action and \ other necessary data (action logit and value) for learn mode defined in ``self._process_transition`` \ method. The key of the dict is the same as the input data, i.e. environment id. .. tip:: If you want to add more tricks on this policy, like temperature factor in multinomial sample, you can pass \ related data as extra keyword arguments of this method. .. note:: The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ For the data type that not supported, the main reason is that the corresponding model does not support it. \ You can implement you own model rather than use the default model. For more information, please raise an \ issue in GitHub repo and we will continue to follow up. .. note:: For more detailed examples, please refer to our unittest for PPOPolicy: ``ding.policy.tests.test_ppo``. """ 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_critic') if self._cuda: output = to_device(output, 'cpu') output = default_decollate(output) return {i: d for i, d in zip(data_id, output)}
[docs] def _process_transition(self, obs: torch.Tensor, policy_output: Dict[str, torch.Tensor], timestep: namedtuple) -> Dict[str, torch.Tensor]: """ Overview: Process and pack one timestep transition data into a dict, which can be directly used for training and \ saved in replay buffer. For PPO, it contains obs, next_obs, action, reward, done, logit, value. Arguments: - obs (:obj:`torch.Tensor`): The env observation of current timestep, such as stacked 2D image in Atari. - policy_output (:obj:`Dict[str, torch.Tensor]`): The output of the policy network with the observation \ as input. For PPO, it contains the state value, action and the logit of the action. - timestep (:obj:`namedtuple`): The execution result namedtuple returned by the environment step method, \ except all the elements have been transformed into tensor data. Usually, it contains the next obs, \ reward, done, info, etc. Returns: - transition (:obj:`Dict[str, torch.Tensor]`): The processed transition data of the current timestep. .. note:: ``next_obs`` is used to calculate nstep return when necessary, so we place in into transition by default. \ You can delete this field to save memory occupancy if you do not need nstep return. """ transition = { 'obs': obs, 'next_obs': timestep.obs, 'action': policy_output['action'], 'logit': policy_output['logit'], 'value': policy_output['value'], 'reward': timestep.reward, 'done': timestep.done, } return transition
[docs] def _get_train_sample(self, transitions: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """ 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. In PPO, a train sample is a processed transition with new computed \ ``traj_flag`` and ``adv`` field. This method is usually used in collectors to execute necessary \ RL data preprocessing before training, which can help learner amortize revelant time consumption. \ In addition, you can also implement this method as an identity function and do the data processing \ in ``self._forward_learn`` method. Arguments: - transitions (: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:`List[Dict[str, Any]]`): The processed train samples, each element is the similar format \ as input transitions, but may contain more data for training, such as GAE advantage. """ data = transitions data = to_device(data, self._device) for transition in data: transition['traj_flag'] = copy.deepcopy(transition['done']) data[-1]['traj_flag'] = True if self._cfg.learn.ignore_done: data[-1]['done'] = False if data[-1]['done']: last_value = torch.zeros_like(data[-1]['value']) else: with torch.no_grad(): last_value = self._collect_model.forward( unsqueeze(data[-1]['next_obs'], 0), mode='compute_actor_critic' )['value'] if len(last_value.shape) == 2: # multi_agent case: last_value = last_value.squeeze(0) if self._value_norm: last_value *= self._running_mean_std.std for i in range(len(data)): data[i]['value'] *= self._running_mean_std.std data = get_gae( data, to_device(last_value, self._device), gamma=self._gamma, gae_lambda=self._gae_lambda, cuda=False, ) if self._value_norm: for i in range(len(data)): data[i]['value'] /= self._running_mean_std.std # remove next_obs for save memory when not recompute adv if not self._recompute_adv: for i in range(len(data)): data[i].pop('next_obs') return get_train_sample(data, self._unroll_len)
[docs] def _init_eval(self) -> None: """ Overview: Initialize the eval mode of policy, including related attributes and modules. For PPO, it contains the \ eval model to select optimial action (e.g. greedily select action with argmax mechanism in discrete action). This method will be called in ``__init__`` method if ``eval`` field is in ``enable_field``. .. note:: If you want to set some spacial member variables in ``_init_eval`` method, you'd better name them \ with prefix ``_eval_`` to avoid conflict with other modes, such as ``self._eval_attr1``. """ assert self._cfg.action_space in ["continuous", "discrete", "hybrid"] self._action_space = self._cfg.action_space if self._action_space == 'continuous': self._eval_model = model_wrap(self._model, wrapper_name='deterministic_sample') elif self._action_space == 'discrete': self._eval_model = model_wrap(self._model, wrapper_name='argmax_sample') elif self._action_space == 'hybrid': self._eval_model = model_wrap(self._model, wrapper_name='hybrid_reparam_multinomial_sample') self._eval_model.reset()
[docs] def _forward_eval(self, data: Dict[int, Any]) -> Dict[int, Any]: """ Overview: Policy forward function of eval mode (evaluation policy performance by interacting with envs). Forward \ means that the policy gets some necessary data (mainly observation) from the envs and then returns the \ action to interact with the envs. ``_forward_eval`` in PPO often uses deterministic sample method to get \ actions while ``_forward_collect`` usually uses stochastic sample method for balance exploration and \ exploitation. Arguments: - data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs. The \ key of the dict is environment id and the value is the corresponding data of the env. Returns: - output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action. The \ key of the dict is the same as the input data, i.e. environment id. .. note:: The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ For the data type that not supported, the main reason is that the corresponding model does not support it. \ You can implement you own model rather than use the default model. For more information, please raise an \ issue in GitHub repo and we will continue to follow up. .. note:: For more detailed examples, please refer to our unittest for PPOPolicy: ``ding.policy.tests.test_ppo``. """ 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)}
[docs] def _monitor_vars_learn(self) -> List[str]: """ Overview: Return the necessary keys for logging the return dict of ``self._forward_learn``. The logger module, such \ as text logger, tensorboard logger, will use these keys to save the corresponding data. Returns: - necessary_keys (:obj:`List[str]`): The list of the necessary keys to be logged. """ variables = super()._monitor_vars_learn() + [ 'policy_loss', 'value_loss', 'entropy_loss', 'adv_max', 'adv_mean', 'approx_kl', 'clipfrac', 'value_max', 'value_mean', ] if self._action_space == 'continuous': variables += ['mu_mean', 'sigma_mean', 'sigma_grad', 'act'] return variables
[docs]@POLICY_REGISTRY.register('ppo_pg') class PPOPGPolicy(Policy): """ Overview: Policy class of on policy version PPO algorithm (pure policy gradient without value network). Paper link: https://arxiv.org/abs/1707.06347. """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='ppo_pg', # (bool) Whether to use cuda for network. cuda=False, # (bool) Whether the RL algorithm is on-policy or off-policy. (Note: in practice PPO can be off-policy used) on_policy=True, # (str) Which kind of action space used in PPOPolicy, ['discrete', 'continuous', 'hybrid'] action_space='discrete', # (bool) Whether to enable multi-agent training, i.e.: MAPPO. multi_agent=False, # (bool) Whether to need policy data in process transition. transition_with_policy_data=True, # learn_mode config learn=dict( # (int) After collecting n_sample/n_episode data, how many epoches to train models. # Each epoch means the one entire passing of training data. epoch_per_collect=10, # (int) How many samples in a training batch. batch_size=64, # (float) The step size of gradient descent. learning_rate=3e-4, # (float) The loss weight of entropy regularization, policy network weight is set to 1. entropy_weight=0.0, # (float) PPO clip ratio, defaults to 0.2. clip_ratio=0.2, # (bool) Whether to enable special network parameters initialization scheme in PPO, such as orthogonal init. ppo_param_init=True, # (str) The gradient clip operation type used in PPO, ['clip_norm', clip_value', 'clip_momentum_norm']. grad_clip_type='clip_norm', # (float) The gradient clip target value used in PPO. # If ``grad_clip_type`` is 'clip_norm', then the maximum of gradient will be normalized to this value. grad_clip_value=0.5, # (bool) Whether ignore done (usually for max step termination env). ignore_done=False, ), # collect_mode config collect=dict( # (int) How many training episodes collected in one collection process. Only one of n_episode shoule be set. # n_episode=8, # (int) Cut trajectories into pieces with length "unroll_len". unroll_len=1, # (float) Reward's future discount factor, aka. gamma. discount_factor=0.99, ), eval=dict(), # for compability )
[docs] def default_model(self) -> Tuple[str, List[str]]: """ Overview: Return this algorithm default neural network model setting for demonstration. ``__init__`` method will \ automatically call this method to get the default model setting and create model. Returns: - model_info (:obj:`Tuple[str, List[str]]`): The registered model name and model's import_names. """ return 'pg', ['ding.model.template.pg']
[docs] def _init_learn(self) -> None: """ Overview: Initialize the learn mode of policy, including related attributes and modules. For PPOPG, it mainly \ contains optimizer, algorithm-specific arguments such as loss weight and clip_ratio. This method \ also executes some special network initializations. This method will be called in ``__init__`` method if ``learn`` field is in ``enable_field``. .. note:: For the member variables that need to be saved and loaded, please refer to the ``_state_dict_learn`` \ and ``_load_state_dict_learn`` methods. .. note:: For the member variables that need to be monitored, please refer to the ``_monitor_vars_learn`` method. .. note:: If you want to set some spacial member variables in ``_init_learn`` method, you'd better name them \ with prefix ``_learn_`` to avoid conflict with other modes, such as ``self._learn_attr1``. """ assert self._cfg.action_space in ["continuous", "discrete"] self._action_space = self._cfg.action_space if self._cfg.learn.ppo_param_init: for n, m in self._model.named_modules(): if isinstance(m, torch.nn.Linear): torch.nn.init.orthogonal_(m.weight) torch.nn.init.zeros_(m.bias) if self._action_space == 'continuous': if hasattr(self._model.head, 'log_sigma_param'): torch.nn.init.constant_(self._model.head.log_sigma_param, -0.5) for m in self._model.modules(): if isinstance(m, torch.nn.Linear): torch.nn.init.zeros_(m.bias) m.weight.data.copy_(0.01 * m.weight.data) # Optimizer self._optimizer = Adam( self._model.parameters(), lr=self._cfg.learn.learning_rate, grad_clip_type=self._cfg.learn.grad_clip_type, clip_value=self._cfg.learn.grad_clip_value ) self._learn_model = model_wrap(self._model, wrapper_name='base') # Algorithm config self._entropy_weight = self._cfg.learn.entropy_weight self._clip_ratio = self._cfg.learn.clip_ratio self._gamma = self._cfg.collect.discount_factor # Main model self._learn_model.reset()
[docs] def _forward_learn(self, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """ Overview: Policy forward function of learn mode (training policy and updating parameters). Forward means \ that the policy inputs some training batch data from the replay buffer and then returns the output \ result, including various training information such as loss, clipfrac, approx_kl. Arguments: - data (:obj:`List[Dict[int, Any]]`): The input data used for policy forward, including the latest \ collected training samples for on-policy algorithms like PPO. For each element in list, the key of the \ dict is the name of data items and the value is the corresponding data. Usually, the value is \ torch.Tensor or np.ndarray or there dict/list combinations. In the ``_forward_learn`` method, data \ often need to first be stacked in the batch dimension by some utility functions such as \ ``default_preprocess_learn``. \ For PPOPG, each element in list is a dict containing at least the following keys: ``obs``, ``action``, \ ``return``, ``logit``, ``done``. Sometimes, it also contains other keys such as ``weight``. Returns: - return_infos (:obj:`List[Dict[str, Any]]`): The information list that indicated training result, each \ training iteration contains append a information dict into the final list. The list will be precessed \ and recorded in text log and tensorboard. The value of the dict must be python scalar or a list of \ scalars. For the detailed definition of the dict, refer to the code of ``_monitor_vars_learn`` method. .. tip:: The training procedure of PPOPG is two for loops. The outer loop trains all the collected training samples \ with ``epoch_per_collect`` epochs. The inner loop splits all the data into different mini-batch with \ the length of ``batch_size``. .. note:: The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ For the data type that not supported, the main reason is that the corresponding model does not support it. \ You can implement you own model rather than use the default model. For more information, please raise an \ issue in GitHub repo and we will continue to follow up. """ data = default_preprocess_learn(data) if self._cuda: data = to_device(data, self._device) return_infos = [] self._learn_model.train() for epoch in range(self._cfg.learn.epoch_per_collect): for batch in split_data_generator(data, self._cfg.learn.batch_size, shuffle=True): output = self._learn_model.forward(batch['obs']) ppo_batch = ppo_policy_data( output['logit'], batch['logit'], batch['action'], batch['return'], batch['weight'] ) if self._action_space == 'continuous': ppo_loss, ppo_info = ppo_policy_error_continuous(ppo_batch, self._clip_ratio) elif self._action_space == 'discrete': ppo_loss, ppo_info = ppo_policy_error(ppo_batch, self._clip_ratio) total_loss = ppo_loss.policy_loss - self._entropy_weight * ppo_loss.entropy_loss self._optimizer.zero_grad() total_loss.backward() self._optimizer.step() return_info = { 'cur_lr': self._optimizer.defaults['lr'], 'total_loss': total_loss.item(), 'policy_loss': ppo_loss.policy_loss.item(), 'entropy_loss': ppo_loss.entropy_loss.item(), 'approx_kl': ppo_info.approx_kl, 'clipfrac': ppo_info.clipfrac, } if self._action_space == 'continuous': return_info.update( { 'act': batch['action'].float().mean().item(), 'mu_mean': output['logit']['mu'].mean().item(), 'sigma_mean': output['logit']['sigma'].mean().item(), } ) return_infos.append(return_info) return return_infos
[docs] def _init_collect(self) -> None: """ Overview: Initialize the collect mode of policy, including related attributes and modules. For PPOPG, it contains \ the collect_model to balance the exploration and exploitation (e.g. the multinomial sample mechanism in \ discrete action space), and other algorithm-specific arguments such as unroll_len and gae_lambda. This method will be called in ``__init__`` method if ``collect`` field is in ``enable_field``. .. note:: If you want to set some spacial member variables in ``_init_collect`` method, you'd better name them \ with prefix ``_collect_`` to avoid conflict with other modes, such as ``self._collect_attr1``. .. tip:: Some variables need to initialize independently in different modes, such as gamma and gae_lambda in PPO. \ This design is for the convenience of parallel execution of different policy modes. """ assert self._cfg.action_space in ["continuous", "discrete"], self._cfg.action_space self._action_space = self._cfg.action_space self._unroll_len = self._cfg.collect.unroll_len if self._action_space == 'continuous': self._collect_model = model_wrap(self._model, wrapper_name='reparam_sample') elif self._action_space == 'discrete': self._collect_model = model_wrap(self._model, wrapper_name='multinomial_sample') self._collect_model.reset() self._gamma = self._cfg.collect.discount_factor
[docs] def _forward_collect(self, data: Dict[int, Any]) -> Dict[int, Any]: """ Overview: Policy forward function of collect mode (collecting training data by interacting with envs). Forward means \ that the policy gets some necessary data (mainly observation) from the envs and then returns the output \ data, such as the action to interact with the envs. Arguments: - data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs. The \ key of the dict is environment id and the value is the corresponding data of the env. Returns: - output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action and \ other necessary data (action logit) for learn mode defined in ``self._process_transition`` \ method. The key of the dict is the same as the input data, i.e. environment id. .. tip:: If you want to add more tricks on this policy, like temperature factor in multinomial sample, you can pass \ related data as extra keyword arguments of this method. .. note:: The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ For the data type that not supported, the main reason is that the corresponding model does not support it. \ You can implement you own model rather than use the default model. For more information, please raise an \ issue in GitHub repo and we will continue to follow up. """ 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) if self._cuda: output = to_device(output, 'cpu') output = default_decollate(output) return {i: d for i, d in zip(data_id, output)}
[docs] def _process_transition(self, obs: torch.Tensor, policy_output: Dict[str, torch.Tensor], timestep: namedtuple) -> Dict[str, torch.Tensor]: """ Overview: Process and pack one timestep transition data into a dict, which can be directly used for training and \ saved in replay buffer. For PPOPG, it contains obs, action, reward, done, logit. Arguments: - obs (:obj:`torch.Tensor`): The env observation of current timestep, such as stacked 2D image in Atari. - policy_output (:obj:`Dict[str, torch.Tensor]`): The output of the policy network with the observation \ as input. For PPOPG, it contains the action and the logit of the action. - timestep (:obj:`namedtuple`): The execution result namedtuple returned by the environment step method, \ except all the elements have been transformed into tensor data. Usually, it contains the next obs, \ reward, done, info, etc. Returns: - transition (:obj:`Dict[str, torch.Tensor]`): The processed transition data of the current timestep. """ transition = { 'obs': obs, 'action': policy_output['action'], 'logit': policy_output['logit'], 'reward': timestep.reward, 'done': timestep.done, } return transition
[docs] def _get_train_sample(self, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """ Overview: For a given entire episode data (a list of transition), process it into a list of sample that \ can be used for training directly. In PPOPG, a train sample is a processed transition with new computed \ ``return`` field. This method is usually used in collectors to execute necessary \ RL data preprocessing before training, which can help learner amortize revelant time consumption. \ In addition, you can also implement this method as an identity function and do the data processing \ in ``self._forward_learn`` method. Arguments: - data (:obj:`List[Dict[str, Any]`): The episode data (a list of transition), each element is \ the same format as the return value of ``self._process_transition`` method. Returns: - samples (:obj:`List[Dict[str, Any]]`): The processed train samples, each element is the similar format \ as input transitions, but may contain more data for training, such as discounted episode return. """ assert data[-1]['done'] is True, "PPO-PG needs a complete epsiode" if self._cfg.learn.ignore_done: raise NotImplementedError R = 0. for i in reversed(range(len(data))): R = self._gamma * R + data[i]['reward'] data[i]['return'] = R return get_train_sample(data, self._unroll_len)
[docs] def _init_eval(self) -> None: """ Overview: Initialize the eval mode of policy, including related attributes and modules. For PPOPG, it contains the \ eval model to select optimial action (e.g. greedily select action with argmax mechanism in discrete action). This method will be called in ``__init__`` method if ``eval`` field is in ``enable_field``. .. note:: If you want to set some spacial member variables in ``_init_eval`` method, you'd better name them \ with prefix ``_eval_`` to avoid conflict with other modes, such as ``self._eval_attr1``. """ assert self._cfg.action_space in ["continuous", "discrete"] self._action_space = self._cfg.action_space if self._action_space == 'continuous': self._eval_model = model_wrap(self._model, wrapper_name='deterministic_sample') elif self._action_space == 'discrete': self._eval_model = model_wrap(self._model, wrapper_name='argmax_sample') self._eval_model.reset()
[docs] def _forward_eval(self, data: Dict[int, Any]) -> Dict[int, Any]: """ Overview: Policy forward function of eval mode (evaluation policy performance by interacting with envs). Forward \ means that the policy gets some necessary data (mainly observation) from the envs and then returns the \ action to interact with the envs. ``_forward_eval`` in PPO often uses deterministic sample method to get \ actions while ``_forward_collect`` usually uses stochastic sample method for balance exploration and \ exploitation. Arguments: - data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs. The \ key of the dict is environment id and the value is the corresponding data of the env. Returns: - output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action. The \ key of the dict is the same as the input data, i.e. environment id. .. note:: The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ For the data type that not supported, the main reason is that the corresponding model does not support it. \ You can implement you own model rather than use the default model. For more information, please raise an \ issue in GitHub repo and we will continue to follow up. .. note:: For more detailed examples, please refer to our unittest for PPOPGPolicy: ``ding.policy.tests.test_ppo``. """ 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) if self._cuda: output = to_device(output, 'cpu') output = default_decollate(output) return {i: d for i, d in zip(data_id, output)}
[docs] def _monitor_vars_learn(self) -> List[str]: """ Overview: Return the necessary keys for logging the return dict of ``self._forward_learn``. The logger module, such \ as text logger, tensorboard logger, will use these keys to save the corresponding data. Returns: - necessary_keys (:obj:`List[str]`): The list of the necessary keys to be logged. """ return super()._monitor_vars_learn() + [ 'policy_loss', 'entropy_loss', 'approx_kl', 'clipfrac', ]
[docs]@POLICY_REGISTRY.register('ppo_offpolicy') class PPOOffPolicy(Policy): """ Overview: Policy class of off-policy version PPO algorithm. Paper link: https://arxiv.org/abs/1707.06347. This version is more suitable for large-scale distributed training. """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='ppo', # (bool) Whether to use cuda for network. cuda=False, on_policy=False, # (bool) Whether to 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, # (str) Which kind of action space used in PPOPolicy, ["continuous", "discrete", "hybrid"]. action_space='discrete', # (bool) Whether to use nstep_return for value loss. nstep_return=False, # (int) The timestep of TD (temporal-difference) loss. nstep=3, # (bool) Whether to need policy data in process transition. transition_with_policy_data=True, # learn_mode config learn=dict( # (int) How many updates(iterations) to train after collector's one collection. # Bigger "update_per_collect" means bigger off-policy. # collect data -> update policy-> collect data -> ... update_per_collect=5, # (int) How many samples in a training batch. batch_size=64, # (float) The step size of gradient descent. learning_rate=0.001, # (float) The loss weight of value network, policy network weight is set to 1. value_weight=0.5, # (float) The loss weight of entropy regularization, policy network weight is set to 1. entropy_weight=0.01, # (float) PPO clip ratio, defaults to 0.2. clip_ratio=0.2, # (bool) Whether to use advantage norm in a whole training batch. adv_norm=False, # (bool) Whether to use value norm with running mean and std in the whole training process. value_norm=True, # (bool) Whether to enable special network parameters initialization scheme in PPO, such as orthogonal init. ppo_param_init=True, # (str) The gradient clip operation type used in PPO, ['clip_norm', clip_value', 'clip_momentum_norm']. grad_clip_type='clip_norm', # (float) The gradient clip target value used in PPO. # If ``grad_clip_type`` is 'clip_norm', then the maximum of gradient will be normalized to this value. grad_clip_value=0.5, # (bool) Whether ignore done (usually for max step termination env). ignore_done=False, # (float) The weight decay (L2 regularization) loss weight, defaults to 0.0. weight_decay=0.0, ), # collect_mode config collect=dict( # (int) How many training samples collected in one collection procedure. # Only one of [n_sample, n_episode] shoule be set. # n_sample=64, # (int) Cut trajectories into pieces with length "unroll_len". unroll_len=1, # (float) Reward's future discount factor, aka. gamma. discount_factor=0.99, # (float) GAE lambda factor for the balance of bias and variance (1-step td and mc). gae_lambda=0.95, ), eval=dict(), # for compability other=dict( replay_buffer=dict( # (int) Maximum size of replay buffer. Usually, larger buffer size is better. replay_buffer_size=10000, ), ), )
[docs] def default_model(self) -> Tuple[str, List[str]]: """ Overview: Return this algorithm default neural network model setting for demonstration. ``__init__`` method will \ automatically call this method to get the default model setting and create model. Returns: - model_info (:obj:`Tuple[str, List[str]]`): The registered model name and model's import_names. """ return 'vac', ['ding.model.template.vac']
[docs] def _init_learn(self) -> None: """ Overview: Initialize the learn mode of policy, including related attributes and modules. For PPOOff, it mainly \ contains optimizer, algorithm-specific arguments such as loss weight and clip_ratio. This method \ also executes some special network initializations and prepares running mean/std monitor for value. This method will be called in ``__init__`` method if ``learn`` field is in ``enable_field``. .. note:: For the member variables that need to be saved and loaded, please refer to the ``_state_dict_learn`` \ and ``_load_state_dict_learn`` methods. .. note:: For the member variables that need to be monitored, please refer to the ``_monitor_vars_learn`` method. .. note:: If you want to set some spacial member variables in ``_init_learn`` method, you'd better name them \ with prefix ``_learn_`` to avoid conflict with other modes, such as ``self._learn_attr1``. """ 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 PPOOff" assert self._cfg.action_space in ["continuous", "discrete", "hybrid"] self._action_space = self._cfg.action_space if self._cfg.learn.ppo_param_init: for n, m in self._model.named_modules(): if isinstance(m, torch.nn.Linear): torch.nn.init.orthogonal_(m.weight) torch.nn.init.zeros_(m.bias) if self._action_space in ['continuous', 'hybrid']: # init log sigma if self._action_space == 'continuous': if hasattr(self._model.actor_head, 'log_sigma_param'): torch.nn.init.constant_(self._model.actor_head.log_sigma_param, -2.0) elif self._action_space == 'hybrid': # actor_head[1]: ReparameterizationHead, for action_args if hasattr(self._model.actor_head[1], 'log_sigma_param'): torch.nn.init.constant_(self._model.actor_head[1].log_sigma_param, -0.5) for m in list(self._model.critic.modules()) + list(self._model.actor.modules()): if isinstance(m, torch.nn.Linear): # orthogonal initialization torch.nn.init.orthogonal_(m.weight, gain=np.sqrt(2)) torch.nn.init.zeros_(m.bias) # do last policy layer scaling, this will make initial actions have (close to) # 0 mean and std, and will help boost performances, # see https://arxiv.org/abs/2006.05990, Fig.24 for details for m in self._model.actor.modules(): if isinstance(m, torch.nn.Linear): torch.nn.init.zeros_(m.bias) m.weight.data.copy_(0.01 * m.weight.data) # Optimizer self._optimizer = Adam( self._model.parameters(), lr=self._cfg.learn.learning_rate, grad_clip_type=self._cfg.learn.grad_clip_type, clip_value=self._cfg.learn.grad_clip_value ) self._learn_model = model_wrap(self._model, wrapper_name='base') # Algorithm config self._value_weight = self._cfg.learn.value_weight self._entropy_weight = self._cfg.learn.entropy_weight self._clip_ratio = self._cfg.learn.clip_ratio self._adv_norm = self._cfg.learn.adv_norm self._value_norm = self._cfg.learn.value_norm if self._value_norm: self._running_mean_std = RunningMeanStd(epsilon=1e-4, device=self._device) self._gamma = self._cfg.collect.discount_factor self._gae_lambda = self._cfg.collect.gae_lambda self._nstep = self._cfg.nstep self._nstep_return = self._cfg.nstep_return # Main model self._learn_model.reset()
[docs] def _forward_learn(self, data: List[Dict[str, Any]]) -> Dict[str, Any]: """ Overview: Policy forward function of learn mode (training policy and updating parameters). Forward means \ that the policy inputs some training batch data from the replay buffer and then returns the output \ result, including various training information such as loss, clipfrac and approx_kl. Arguments: - data (:obj:`List[Dict[int, Any]]`): The input data used for policy forward, including a batch of \ training samples. For each element in list, the key of the dict is the name of data items and the \ value is the corresponding data. Usually, the value is torch.Tensor or np.ndarray or there dict/list \ combinations. In the ``_forward_learn`` method, data often need to first be stacked in the batch \ dimension by some utility functions such as ``default_preprocess_learn``. \ For PPOOff, each element in list is a dict containing at least the following keys: ``obs``, ``adv``, \ ``action``, ``logit``, ``value``, ``done``. Sometimes, it also contains other keys such as ``weight`` \ and ``value_gamma``. Returns: - info_dict (:obj:`Dict[str, Any]`): The information dict that indicated training result, which will be \ recorded in text log and tensorboard, values must be python scalar or a list of scalars. For the \ detailed definition of the dict, refer to the code of ``_monitor_vars_learn`` method. .. note:: The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ For the data type that not supported, the main reason is that the corresponding model does not support it. \ You can implement you own model rather than use the default model. For more information, please raise an \ issue in GitHub repo and we will continue to follow up. """ data = default_preprocess_learn(data, ignore_done=self._cfg.learn.ignore_done, use_nstep=self._nstep_return) if self._cuda: data = to_device(data, self._device) data['obs'] = to_dtype(data['obs'], torch.float32) if 'next_obs' in data: data['next_obs'] = to_dtype(data['next_obs'], torch.float32) # ==================== # PPO forward # ==================== self._learn_model.train() with torch.no_grad(): if self._value_norm: unnormalized_return = data['adv'] + data['value'] * self._running_mean_std.std data['return'] = unnormalized_return / self._running_mean_std.std self._running_mean_std.update(unnormalized_return.cpu().numpy()) else: data['return'] = data['adv'] + data['value'] # normal ppo if not self._nstep_return: output = self._learn_model.forward(data['obs'], mode='compute_actor_critic') adv = data['adv'] if self._adv_norm: # Normalize advantage in a total train_batch adv = (adv - adv.mean()) / (adv.std() + 1e-8) # Calculate ppo loss if self._action_space == 'continuous': ppodata = ppo_data( output['logit'], data['logit'], data['action'], output['value'], data['value'], adv, data['return'], data['weight'] ) ppo_loss, ppo_info = ppo_error_continuous(ppodata, self._clip_ratio) elif self._action_space == 'discrete': ppodata = ppo_data( output['logit'], data['logit'], data['action'], output['value'], data['value'], adv, data['return'], data['weight'] ) ppo_loss, ppo_info = ppo_error(ppodata, self._clip_ratio) elif self._action_space == 'hybrid': # discrete part (discrete policy loss and entropy loss) ppo_discrete_batch = ppo_policy_data( output['logit']['action_type'], data['logit']['action_type'], data['action']['action_type'], adv, data['weight'] ) ppo_discrete_loss, ppo_discrete_info = ppo_policy_error(ppo_discrete_batch, self._clip_ratio) # continuous part (continuous policy loss and entropy loss, value loss) ppo_continuous_batch = ppo_data( output['logit']['action_args'], data['logit']['action_args'], data['action']['action_args'], output['value'], data['value'], adv, data['return'], data['weight'] ) ppo_continuous_loss, ppo_continuous_info = ppo_error_continuous(ppo_continuous_batch, self._clip_ratio) # sum discrete and continuous loss ppo_loss = type(ppo_continuous_loss)( ppo_continuous_loss.policy_loss + ppo_discrete_loss.policy_loss, ppo_continuous_loss.value_loss, ppo_continuous_loss.entropy_loss + ppo_discrete_loss.entropy_loss ) ppo_info = type(ppo_continuous_info)( max(ppo_continuous_info.approx_kl, ppo_discrete_info.approx_kl), max(ppo_continuous_info.clipfrac, ppo_discrete_info.clipfrac) ) wv, we = self._value_weight, self._entropy_weight total_loss = ppo_loss.policy_loss + wv * ppo_loss.value_loss - we * ppo_loss.entropy_loss else: output = self._learn_model.forward(data['obs'], mode='compute_actor') adv = data['adv'] if self._adv_norm: # Normalize advantage in a total train_batch adv = (adv - adv.mean()) / (adv.std() + 1e-8) # Calculate ppo loss if self._action_space == 'continuous': ppodata = ppo_policy_data(output['logit'], data['logit'], data['action'], adv, data['weight']) ppo_policy_loss, ppo_info = ppo_policy_error_continuous(ppodata, self._clip_ratio) elif self._action_space == 'discrete': ppodata = ppo_policy_data(output['logit'], data['logit'], data['action'], adv, data['weight']) ppo_policy_loss, ppo_info = ppo_policy_error(ppodata, self._clip_ratio) elif self._action_space == 'hybrid': # discrete part (discrete policy loss and entropy loss) ppo_discrete_data = ppo_policy_data( output['logit']['action_type'], data['logit']['action_type'], data['action']['action_type'], adv, data['weight'] ) ppo_discrete_loss, ppo_discrete_info = ppo_policy_error(ppo_discrete_data, self._clip_ratio) # continuous part (continuous policy loss and entropy loss, value loss) ppo_continuous_data = ppo_policy_data( output['logit']['action_args'], data['logit']['action_args'], data['action']['action_args'], adv, data['weight'] ) ppo_continuous_loss, ppo_continuous_info = ppo_policy_error_continuous( ppo_continuous_data, self._clip_ratio ) # sum discrete and continuous loss ppo_policy_loss = type(ppo_continuous_loss)( ppo_continuous_loss.policy_loss + ppo_discrete_loss.policy_loss, ppo_continuous_loss.entropy_loss + ppo_discrete_loss.entropy_loss ) ppo_info = type(ppo_continuous_info)( max(ppo_continuous_info.approx_kl, ppo_discrete_info.approx_kl), max(ppo_continuous_info.clipfrac, ppo_discrete_info.clipfrac) ) wv, we = self._value_weight, self._entropy_weight next_obs = data.get('next_obs') value_gamma = data.get('value_gamma') reward = data.get('reward') # current value value = self._learn_model.forward(data['obs'], mode='compute_critic') # target value next_data = {'obs': next_obs} target_value = self._learn_model.forward(next_data['obs'], mode='compute_critic') # TODO what should we do here to keep shape assert self._nstep > 1 td_data = v_nstep_td_data( value['value'], target_value['value'], reward, data['done'], data['weight'], value_gamma ) # calculate v_nstep_td critic_loss critic_loss, td_error_per_sample = v_nstep_td_error(td_data, self._gamma, self._nstep) ppo_loss_data = namedtuple('ppo_loss', ['policy_loss', 'value_loss', 'entropy_loss']) ppo_loss = ppo_loss_data(ppo_policy_loss.policy_loss, critic_loss, ppo_policy_loss.entropy_loss) total_loss = ppo_policy_loss.policy_loss + wv * critic_loss - we * ppo_policy_loss.entropy_loss # ==================== # PPO update # ==================== self._optimizer.zero_grad() total_loss.backward() self._optimizer.step() return_info = { 'cur_lr': self._optimizer.defaults['lr'], 'total_loss': total_loss.item(), 'policy_loss': ppo_loss.policy_loss.item(), 'value': data['value'].mean().item(), 'value_loss': ppo_loss.value_loss.item(), 'entropy_loss': ppo_loss.entropy_loss.item(), 'adv_abs_max': adv.abs().max().item(), 'approx_kl': ppo_info.approx_kl, 'clipfrac': ppo_info.clipfrac, } if self._action_space == 'continuous': return_info.update( { 'act': data['action'].float().mean().item(), 'mu_mean': output['logit']['mu'].mean().item(), 'sigma_mean': output['logit']['sigma'].mean().item(), } ) return return_info
[docs] def _init_collect(self) -> None: """ Overview: Initialize the collect mode of policy, including related attributes and modules. For PPOOff, it contains \ collect_model to balance the exploration and exploitation (e.g. the multinomial sample mechanism in \ discrete action space), and other algorithm-specific arguments such as unroll_len and gae_lambda. This method will be called in ``__init__`` method if ``collect`` field is in ``enable_field``. .. note:: If you want to set some spacial member variables in ``_init_collect`` method, you'd better name them \ with prefix ``_collect_`` to avoid conflict with other modes, such as ``self._collect_attr1``. .. tip:: Some variables need to initialize independently in different modes, such as gamma and gae_lambda in PPOOff. This design is for the convenience of parallel execution of different policy modes. """ self._unroll_len = self._cfg.collect.unroll_len assert self._cfg.action_space in ["continuous", "discrete", "hybrid"] self._action_space = self._cfg.action_space if self._action_space == 'continuous': self._collect_model = model_wrap(self._model, wrapper_name='reparam_sample') elif self._action_space == 'discrete': self._collect_model = model_wrap(self._model, wrapper_name='multinomial_sample') elif self._action_space == 'hybrid': self._collect_model = model_wrap(self._model, wrapper_name='hybrid_reparam_multinomial_sample') self._collect_model.reset() self._gamma = self._cfg.collect.discount_factor self._gae_lambda = self._cfg.collect.gae_lambda self._nstep = self._cfg.nstep self._nstep_return = self._cfg.nstep_return self._value_norm = self._cfg.learn.value_norm if self._value_norm: self._running_mean_std = RunningMeanStd(epsilon=1e-4, device=self._device)
[docs] def _forward_collect(self, data: Dict[int, Any]) -> Dict[int, Any]: """ Overview: Policy forward function of collect mode (collecting training data by interacting with envs). Forward means \ that the policy gets some necessary data (mainly observation) from the envs and then returns the output \ data, such as the action to interact with the envs. Arguments: - data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs. The \ key of the dict is environment id and the value is the corresponding data of the env. Returns: - output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action and \ other necessary data (action logit and value) for learn mode defined in ``self._process_transition`` \ method. The key of the dict is the same as the input data, i.e. environment id. .. tip:: If you want to add more tricks on this policy, like temperature factor in multinomial sample, you can pass \ related data as extra keyword arguments of this method. .. note:: The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ For the data type that not supported, the main reason is that the corresponding model does not support it. \ You can implement you own model rather than use the default model. For more information, please raise an \ issue in GitHub repo and we will continue to follow up. .. note:: For more detailed examples, please refer to our unittest for PPOOffPolicy: ``ding.policy.tests.test_ppo``. """ 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_critic') if self._cuda: output = to_device(output, 'cpu') output = default_decollate(output) return {i: d for i, d in zip(data_id, output)}
[docs] def _process_transition(self, obs: torch.Tensor, policy_output: Dict[str, torch.Tensor], timestep: namedtuple) -> Dict[str, torch.Tensor]: """ Overview: Process and pack one timestep transition data into a dict, which can be directly used for training and \ saved in replay buffer. For PPO, it contains obs, next_obs, action, reward, done, logit, value. Arguments: - obs (:obj:`torch.Tensor`): The env observation of current timestep, such as stacked 2D image in Atari. - policy_output (:obj:`Dict[str, torch.Tensor]`): The output of the policy network with the observation \ as input. For PPO, it contains the state value, action and the logit of the action. - timestep (:obj:`namedtuple`): The execution result namedtuple returned by the environment step method, \ except all the elements have been transformed into tensor data. Usually, it contains the next obs, \ reward, done, info, etc. Returns: - transition (:obj:`Dict[str, torch.Tensor]`): The processed transition data of the current timestep. .. note:: ``next_obs`` is used to calculate nstep return when necessary, so we place in into transition by default. \ You can delete this field to save memory occupancy if you do not need nstep return. """ transition = { 'obs': obs, 'next_obs': timestep.obs, 'logit': policy_output['logit'], 'action': policy_output['action'], 'value': policy_output['value'], 'reward': timestep.reward, 'done': timestep.done, } return transition
[docs] def _get_train_sample(self, transitions: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """ 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. In PPO, a train sample is a processed transition with new computed \ ``traj_flag`` and ``adv`` field. This method is usually used in collectors to execute necessary \ RL data preprocessing before training, which can help learner amortize revelant time consumption. \ In addition, you can also implement this method as an identity function and do the data processing \ in ``self._forward_learn`` method. Arguments: - transitions (: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:`List[Dict[str, Any]]`): The processed train samples, each element is the similar format \ as input transitions, but may contain more data for training, such as GAE advantage. """ data = transitions data = to_device(data, self._device) for transition in data: transition['traj_flag'] = copy.deepcopy(transition['done']) data[-1]['traj_flag'] = True if self._cfg.learn.ignore_done: data[-1]['done'] = False if data[-1]['done']: last_value = torch.zeros_like(data[-1]['value']) else: with torch.no_grad(): last_value = self._collect_model.forward( unsqueeze(data[-1]['next_obs'], 0), mode='compute_actor_critic' )['value'] if len(last_value.shape) == 2: # multi_agent case: last_value = last_value.squeeze(0) if self._value_norm: last_value *= self._running_mean_std.std for i in range(len(data)): data[i]['value'] *= self._running_mean_std.std data = get_gae( data, to_device(last_value, self._device), gamma=self._gamma, gae_lambda=self._gae_lambda, cuda=False, ) if self._value_norm: for i in range(len(data)): data[i]['value'] /= self._running_mean_std.std if not self._nstep_return: return get_train_sample(data, self._unroll_len) else: return get_nstep_return_data(data, self._nstep)
[docs] def _init_eval(self) -> None: """ Overview: Initialize the eval mode of policy, including related attributes and modules. For PPOOff, it contains the \ eval model to select optimial action (e.g. greedily select action with argmax mechanism in discrete action). This method will be called in ``__init__`` method if ``eval`` field is in ``enable_field``. .. note:: If you want to set some spacial member variables in ``_init_eval`` method, you'd better name them \ with prefix ``_eval_`` to avoid conflict with other modes, such as ``self._eval_attr1``. """ assert self._cfg.action_space in ["continuous", "discrete", "hybrid"] self._action_space = self._cfg.action_space if self._action_space == 'continuous': self._eval_model = model_wrap(self._model, wrapper_name='deterministic_sample') elif self._action_space == 'discrete': self._eval_model = model_wrap(self._model, wrapper_name='argmax_sample') elif self._action_space == 'hybrid': self._eval_model = model_wrap(self._model, wrapper_name='hybrid_deterministic_argmax_sample') self._eval_model.reset()
[docs] def _forward_eval(self, data: Dict[int, Any]) -> Dict[int, Any]: """ Overview: Policy forward function of eval mode (evaluation policy performance by interacting with envs). Forward \ means that the policy gets some necessary data (mainly observation) from the envs and then returns the \ action to interact with the envs. ``_forward_eval`` in PPO often uses deterministic sample method to get \ actions while ``_forward_collect`` usually uses stochastic sample method for balance exploration and \ exploitation. Arguments: - data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs. The \ key of the dict is environment id and the value is the corresponding data of the env. Returns: - output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action. The \ key of the dict is the same as the input data, i.e. environment id. .. note:: The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ For the data type that not supported, the main reason is that the corresponding model does not support it. \ You can implement you own model rather than use the default model. For more information, please raise an \ issue in GitHub repo and we will continue to follow up. .. note:: For more detailed examples, please refer to our unittest for PPOOffPolicy: ``ding.policy.tests.test_ppo``. """ 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)}
[docs] def _monitor_vars_learn(self) -> List[str]: """ Overview: Return the necessary keys for logging the return dict of ``self._forward_learn``. The logger module, such \ as text logger, tensorboard logger, will use these keys to save the corresponding data. Returns: - necessary_keys (:obj:`List[str]`): The list of the necessary keys to be logged. """ variables = super()._monitor_vars_learn() + [ 'policy_loss', 'value', 'value_loss', 'entropy_loss', 'adv_abs_max', 'approx_kl', 'clipfrac' ] if self._action_space == 'continuous': variables += ['mu_mean', 'sigma_mean', 'sigma_grad', 'act'] return variables
[docs]@POLICY_REGISTRY.register('ppo_stdim') class PPOSTDIMPolicy(PPOPolicy): """ Overview: Policy class of on policy version PPO algorithm with ST-DIM auxiliary model. PPO paper link: https://arxiv.org/abs/1707.06347. ST-DIM paper link: https://arxiv.org/abs/1906.08226. """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='ppo_stdim', # (bool) Whether to use cuda for network. cuda=False, # (bool) Whether the RL algorithm is on-policy or off-policy. (Note: in practice PPO can be off-policy used) on_policy=True, # (bool) Whether to use priority(priority sample, IS weight, update priority) priority=False, # (bool) Whether to use Importance Sampling Weight to correct biased update due to priority. # If True, priority must be True. priority_IS_weight=False, # (bool) Whether to recompurete advantages in each iteration of on-policy PPO recompute_adv=True, # (str) Which kind of action space used in PPOPolicy, ['discrete', 'continuous'] action_space='discrete', # (bool) Whether to use nstep return to calculate value target, otherwise, use return = adv + value nstep_return=False, # (bool) Whether to enable multi-agent training, i.e.: MAPPO multi_agent=False, # (bool) Whether to need policy data in process transition transition_with_policy_data=True, # (float) The loss weight of the auxiliary model to the main loss. aux_loss_weight=0.001, aux_model=dict( # (int) the encoding size (of each head) to apply contrastive loss. encode_shape=64, # ([int, int]) the heads number of the obs encoding and next_obs encoding respectively. heads=[1, 1], # (str) the contrastive loss type. loss_type='infonce', # (float) a parameter to adjust the polarity between positive and negative samples. temperature=1.0, ), # learn_mode config learn=dict( # (int) After collecting n_sample/n_episode data, how many epoches to train models. # Each epoch means the one entire passing of training data. epoch_per_collect=10, # (int) How many samples in a training batch. batch_size=64, # (float) The step size of gradient descent. learning_rate=3e-4, # (float) The loss weight of value network, policy network weight is set to 1. value_weight=0.5, # (float) The loss weight of entropy regularization, policy network weight is set to 1. entropy_weight=0.0, # (float) PPO clip ratio, defaults to 0.2. clip_ratio=0.2, # (bool) Whether to use advantage norm in a whole training batch. adv_norm=True, # (bool) Whether to use value norm with running mean and std in the whole training process. value_norm=True, # (bool) Whether to enable special network parameters initialization scheme in PPO, such as orthogonal init. ppo_param_init=True, # (str) The gradient clip operation type used in PPO, ['clip_norm', clip_value', 'clip_momentum_norm']. grad_clip_type='clip_norm', # (float) The gradient clip target value used in PPO. # If ``grad_clip_type`` is 'clip_norm', then the maximum of gradient will be normalized to this value. grad_clip_value=0.5, # (bool) Whether ignore done (usually for max step termination env). ignore_done=False, ), # collect_mode config collect=dict( # (int) How many training samples collected in one collection procedure. # Only one of [n_sample, n_episode] shoule be set. # n_sample=64, # (int) Cut trajectories into pieces with length "unroll_len". unroll_len=1, # (float) Reward's future discount factor, aka. gamma. discount_factor=0.99, # (float) GAE lambda factor for the balance of bias and variance (1-step td and mc). gae_lambda=0.95, ), eval=dict(), # for compability )
[docs] def _init_learn(self) -> None: """ Overview: Learn mode init method. Called by ``self.__init__``. Init the auxiliary model, its optimizer, and the axuliary loss weight to the main loss. """ super()._init_learn() x_size, y_size = self._get_encoding_size() self._aux_model = ContrastiveLoss(x_size, y_size, **self._cfg.aux_model) if self._cuda: self._aux_model.cuda() self._aux_optimizer = Adam(self._aux_model.parameters(), lr=self._cfg.learn.learning_rate) self._aux_loss_weight = self._cfg.aux_loss_weight
def _get_encoding_size(self): """ Overview: Get the input encoding size of the ST-DIM axuiliary model. Returns: - info_dict (:obj:`[Tuple, Tuple]`): The encoding size without the first (Batch) dimension. """ obs = self._cfg.model.obs_shape if isinstance(obs, int): obs = [obs] test_data = { "obs": torch.randn(1, *obs), "next_obs": torch.randn(1, *obs), } if self._cuda: test_data = to_device(test_data, self._device) with torch.no_grad(): x, y = self._model_encode(test_data) return x.size()[1:], y.size()[1:]
[docs] def _model_encode(self, data): """ Overview: Get the encoding of the main model as input for the auxiliary model. Arguments: - data (:obj:`dict`): Dict type data, same as the _forward_learn input. Returns: - (:obj:`Tuple[Tensor]`): the tuple of two tensors to apply contrastive embedding learning. In ST-DIM algorithm, these two variables are the dqn encoding of `obs` and `next_obs`\ respectively. """ assert hasattr(self._model, "encoder") x = self._model.encoder(data["obs"]) y = self._model.encoder(data["next_obs"]) return x, y
[docs] def _forward_learn(self, data: Dict[str, Any]) -> Dict[str, Any]: """ Overview: Forward and backward function of learn mode. Arguments: - data (:obj:`dict`): Dict type data Returns: - info_dict (:obj:`Dict[str, Any]`): Including current lr, total_loss, policy_loss, value_loss, entropy_loss, \ adv_abs_max, approx_kl, clipfrac """ data = default_preprocess_learn(data, ignore_done=self._cfg.learn.ignore_done, use_nstep=False) if self._cuda: data = to_device(data, self._device) # ==================== # PPO forward # ==================== return_infos = [] self._learn_model.train() for epoch in range(self._cfg.learn.epoch_per_collect): if self._recompute_adv: # calculate new value using the new updated value network with torch.no_grad(): value = self._learn_model.forward(data['obs'], mode='compute_critic')['value'] next_value = self._learn_model.forward(data['next_obs'], mode='compute_critic')['value'] if self._value_norm: value *= self._running_mean_std.std next_value *= self._running_mean_std.std traj_flag = data.get('traj_flag', None) # traj_flag indicates termination of trajectory compute_adv_data = gae_data(value, next_value, data['reward'], data['done'], traj_flag) data['adv'] = gae(compute_adv_data, self._gamma, self._gae_lambda) unnormalized_returns = value + data['adv'] if self._value_norm: data['value'] = value / self._running_mean_std.std data['return'] = unnormalized_returns / self._running_mean_std.std self._running_mean_std.update(unnormalized_returns.cpu().numpy()) else: data['value'] = value data['return'] = unnormalized_returns else: # don't recompute adv if self._value_norm: unnormalized_return = data['adv'] + data['value'] * self._running_mean_std.std data['return'] = unnormalized_return / self._running_mean_std.std self._running_mean_std.update(unnormalized_return.cpu().numpy()) else: data['return'] = data['adv'] + data['value'] for batch in split_data_generator(data, self._cfg.learn.batch_size, shuffle=True): # ====================== # Auxiliary model update # ====================== # RL network encoding # To train the auxiliary network, the gradients of x, y should be 0. with torch.no_grad(): x_no_grad, y_no_grad = self._model_encode(batch) # the forward function of the auxiliary network self._aux_model.train() aux_loss_learn = self._aux_model.forward(x_no_grad, y_no_grad) # the BP process of the auxiliary network self._aux_optimizer.zero_grad() aux_loss_learn.backward() if self._cfg.multi_gpu: self.sync_gradients(self._aux_model) self._aux_optimizer.step() output = self._learn_model.forward(batch['obs'], mode='compute_actor_critic') adv = batch['adv'] if self._adv_norm: # Normalize advantage in a train_batch adv = (adv - adv.mean()) / (adv.std() + 1e-8) # Calculate ppo loss if self._action_space == 'continuous': ppo_batch = ppo_data( output['logit'], batch['logit'], batch['action'], output['value'], batch['value'], adv, batch['return'], batch['weight'] ) ppo_loss, ppo_info = ppo_error_continuous(ppo_batch, self._clip_ratio) elif self._action_space == 'discrete': ppo_batch = ppo_data( output['logit'], batch['logit'], batch['action'], output['value'], batch['value'], adv, batch['return'], batch['weight'] ) ppo_loss, ppo_info = ppo_error(ppo_batch, self._clip_ratio) # ====================== # Compute auxiliary loss # ====================== # In total_loss BP, the gradients of x, y are required to update the encoding network. # The auxiliary network won't be updated since the self._optimizer does not contain # its weights. x, y = self._model_encode(data) self._aux_model.eval() aux_loss_eval = self._aux_model.forward(x, y) * self._aux_loss_weight wv, we = self._value_weight, self._entropy_weight total_loss = ppo_loss.policy_loss + wv * ppo_loss.value_loss - we * ppo_loss.entropy_loss\ + aux_loss_eval self._optimizer.zero_grad() total_loss.backward() self._optimizer.step() return_info = { 'cur_lr': self._optimizer.defaults['lr'], 'total_loss': total_loss.item(), 'aux_loss_learn': aux_loss_learn.item(), 'aux_loss_eval': aux_loss_eval.item(), 'policy_loss': ppo_loss.policy_loss.item(), 'value_loss': ppo_loss.value_loss.item(), 'entropy_loss': ppo_loss.entropy_loss.item(), 'adv_max': adv.max().item(), 'adv_mean': adv.mean().item(), 'value_mean': output['value'].mean().item(), 'value_max': output['value'].max().item(), 'approx_kl': ppo_info.approx_kl, 'clipfrac': ppo_info.clipfrac, } if self._action_space == 'continuous': return_info.update( { 'act': batch['action'].float().mean().item(), 'mu_mean': output['logit']['mu'].mean().item(), 'sigma_mean': output['logit']['sigma'].mean().item(), } ) return_infos.append(return_info) return return_infos
[docs] def _state_dict_learn(self) -> Dict[str, Any]: """ Overview: Return the state_dict of learn mode, usually including model, optimizer and aux_optimizer for \ representation learning. 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(), 'optimizer': self._optimizer.state_dict(), 'aux_optimizer': self._aux_optimizer.state_dict(), }
[docs] 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._optimizer.load_state_dict(state_dict['optimizer']) self._aux_optimizer.load_state_dict(state_dict['aux_optimizer'])
[docs] def _monitor_vars_learn(self) -> List[str]: """ Overview: Return the necessary keys for logging the return dict of ``self._forward_learn``. The logger module, such \ as text logger, tensorboard logger, will use these keys to save the corresponding data. Returns: - necessary_keys (:obj:`List[str]`): The list of the necessary keys to be logged. """ return super()._monitor_vars_learn() + ["aux_loss_learn", "aux_loss_eval"]