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Source code for ding.policy.ddpg

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

from ding.torch_utils import Adam, to_device
from ding.rl_utils import v_1step_td_data, v_1step_td_error, get_train_sample
from ding.model import model_wrap
from ding.utils import POLICY_REGISTRY
from ding.utils.data import default_collate, default_decollate
from .base_policy import Policy
from .common_utils import default_preprocess_learn


[docs]@POLICY_REGISTRY.register('ddpg') class DDPGPolicy(Policy): """ Overview: Policy class of DDPG algorithm. Paper link: https://arxiv.org/abs/1509.02971. Config: == ==================== ======== ============= ================================= ======================= ID Symbol Type Default Value Description Other(Shape) == ==================== ======== ============= ================================= ======================= 1 | ``type`` str ddpg | RL policy register name, refer | this arg is optional, | | to registry ``POLICY_REGISTRY`` | a placeholder 2 | ``cuda`` bool False | Whether to use cuda for network | 3 | ``random_`` int 25000 | Number of randomly collected | Default to 25000 for | ``collect_size`` | training samples in replay | DDPG/TD3, 10000 for | | buffer when training starts. | sac. 4 | ``model.twin_`` bool False | Whether to use two critic | Default False for | ``critic`` | networks or only one. | DDPG, Clipped Double | | | Q-learning method in | | | TD3 paper. 5 | ``learn.learning`` float 1e-3 | Learning rate for actor | | ``_rate_actor`` | network(aka. policy). | 6 | ``learn.learning`` float 1e-3 | Learning rates for critic | | ``_rate_critic`` | network (aka. Q-network). | 7 | ``learn.actor_`` int 2 | When critic network updates | Default 1 for DDPG, | ``update_freq`` | once, how many times will actor | 2 for TD3. Delayed | | network update. | Policy Updates method | | | in TD3 paper. 8 | ``learn.noise`` bool False | Whether to add noise on target | Default False for | | network's action. | DDPG, True for TD3. | | | Target Policy Smoo- | | | thing Regularization | | | in TD3 paper. 9 | ``learn.-`` bool False | Determine whether to ignore | Use ignore_done only | ``ignore_done`` | done flag. | in halfcheetah env. 10 | ``learn.-`` float 0.005 | Used for soft update of the | aka. Interpolation | ``target_theta`` | target network. | factor in polyak aver- | | | aging for target | | | networks. 11 | ``collect.-`` float 0.1 | Used for add noise during co- | Sample noise from dis- | ``noise_sigma`` | llection, through controlling | tribution, Ornstein- | | the sigma of distribution | Uhlenbeck process in | | | DDPG paper, Gaussian | | | process in ours. == ==================== ======== ============= ================================= ======================= """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='ddpg', # (bool) Whether to use cuda in policy. cuda=False, # (bool) Whether learning policy is the same as collecting data policy(on-policy). Default False in DDPG. on_policy=False, # (bool) Whether to enable priority experience sample. priority=False, # (bool) Whether to use Importance Sampling Weight to correct biased update. If True, priority must be True. priority_IS_weight=False, # (int) Number of training samples(randomly collected) in replay buffer when training starts. # Default 25000 in DDPG/TD3. random_collect_size=25000, # (bool) Whether to need policy data in process transition. transition_with_policy_data=False, # (str) Action space type, including ['continuous', 'hybrid']. action_space='continuous', # (bool) Whether use batch normalization for reward. reward_batch_norm=False, # (bool) Whether to enable multi-agent training setting. multi_agent=False, # 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=1, # (int) Minibatch size for gradient descent. batch_size=256, # (float) Learning rates for actor network(aka. policy). learning_rate_actor=1e-3, # (float) Learning rates for critic network(aka. Q-network). learning_rate_critic=1e-3, # (bool) Whether ignore done(usually for max step termination env. e.g. pendulum) # Note: Gym wraps the MuJoCo envs by default with TimeLimit environment wrappers. # These limit HalfCheetah, and several other MuJoCo envs, to max length of 1000. # However, interaction with HalfCheetah always gets done with False, # Since we inplace done==True with done==False to keep # TD-error accurate computation(``gamma * (1 - done) * next_v + reward``), # when the episode step is greater than max episode step. ignore_done=False, # (float) target_theta: Used for soft update of the target network, # aka. Interpolation factor in polyak averaging for target networks. # Default to 0.005. target_theta=0.005, # (float) discount factor for the discounted sum of rewards, aka. gamma. discount_factor=0.99, # (int) When critic network updates once, how many times will actor network update. # Delayed Policy Updates in original TD3 paper(https://arxiv.org/pdf/1802.09477.pdf). # Default 1 for DDPG, 2 for TD3. actor_update_freq=1, # (bool) Whether to add noise on target network's action. # Target Policy Smoothing Regularization in original TD3 paper(https://arxiv.org/pdf/1802.09477.pdf). # Default True for TD3, False for DDPG. noise=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=1, # (int) Split episodes or trajectories into pieces with length `unroll_len`. unroll_len=1, # (float) It is a must to add noise during collection. So here omits "noise" and only set "noise_sigma". noise_sigma=0.1, ), eval=dict(), # for compability other=dict( replay_buffer=dict( # (int) Maximum size of replay buffer. Usually, larger buffer size is better. replay_buffer_size=100000, ), ), )
[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. """ if self._cfg.multi_agent: return 'continuous_maqac', ['ding.model.template.maqac'] else: return 'continuous_qac', ['ding.model.template.qac']
[docs] def _init_learn(self) -> None: """ Overview: Initialize the learn mode of policy, including related attributes and modules. For DDPG, it mainly \ contains two optimizers, algorithm-specific arguments such as gamma and twin_critic, main and target model. 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 # actor and critic optimizer self._optimizer_actor = Adam( self._model.actor.parameters(), lr=self._cfg.learn.learning_rate_actor, ) self._optimizer_critic = Adam( self._model.critic.parameters(), lr=self._cfg.learn.learning_rate_critic, ) self._reward_batch_norm = self._cfg.reward_batch_norm self._gamma = self._cfg.learn.discount_factor self._actor_update_freq = self._cfg.learn.actor_update_freq self._twin_critic = self._cfg.model.twin_critic # True for TD3, False for DDPG # main and target models self._target_model = copy.deepcopy(self._model) self._learn_model = model_wrap(self._model, wrapper_name='base') if self._cfg.action_space == 'hybrid': self._learn_model = model_wrap(self._learn_model, wrapper_name='hybrid_argmax_sample') self._target_model = model_wrap(self._target_model, wrapper_name='hybrid_argmax_sample') self._target_model = model_wrap( self._target_model, wrapper_name='target', update_type='momentum', update_kwargs={'theta': self._cfg.learn.target_theta} ) if self._cfg.learn.noise: self._target_model = model_wrap( self._target_model, wrapper_name='action_noise', noise_type='gauss', noise_kwargs={ 'mu': 0.0, 'sigma': self._cfg.learn.noise_sigma }, noise_range=self._cfg.learn.noise_range ) self._learn_model.reset() self._target_model.reset() self._forward_learn_cnt = 0 # count iterations
[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, action, priority. 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 DDPG, each element in list is a dict containing at least the following keys: ``obs``, ``action``, \ ``reward``, ``next_obs``, ``done``. Sometimes, it also contains other keys such as ``weight`` \ and ``logit`` which is used for hybrid action space. 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. .. note:: For more detailed examples, please refer to our unittest for DDPGPolicy: ``ding.policy.tests.test_ddpg``. """ loss_dict = {} data = default_preprocess_learn( data, use_priority=self._cfg.priority, use_priority_IS_weight=self._cfg.priority_IS_weight, ignore_done=self._cfg.learn.ignore_done, use_nstep=False ) if self._cuda: data = to_device(data, self._device) # ==================== # critic learn forward # ==================== self._learn_model.train() self._target_model.train() next_obs = data['next_obs'] reward = data['reward'] if self._reward_batch_norm: reward = (reward - reward.mean()) / (reward.std() + 1e-8) # current q value q_value = self._learn_model.forward(data, mode='compute_critic')['q_value'] # target q value. with torch.no_grad(): next_actor_data = self._target_model.forward(next_obs, mode='compute_actor') next_actor_data['obs'] = next_obs target_q_value = self._target_model.forward(next_actor_data, mode='compute_critic')['q_value'] q_value_dict = {} target_q_value_dict = {} if self._twin_critic: # TD3: two critic networks target_q_value = torch.min(target_q_value[0], target_q_value[1]) # find min one as target q value q_value_dict['q_value'] = q_value[0].mean().data.item() q_value_dict['q_value_twin'] = q_value[1].mean().data.item() target_q_value_dict['target q_value'] = target_q_value.mean().data.item() # critic network1 td_data = v_1step_td_data(q_value[0], target_q_value, reward, data['done'], data['weight']) critic_loss, td_error_per_sample1 = v_1step_td_error(td_data, self._gamma) loss_dict['critic_loss'] = critic_loss # critic network2(twin network) td_data_twin = v_1step_td_data(q_value[1], target_q_value, reward, data['done'], data['weight']) critic_twin_loss, td_error_per_sample2 = v_1step_td_error(td_data_twin, self._gamma) loss_dict['critic_twin_loss'] = critic_twin_loss td_error_per_sample = (td_error_per_sample1 + td_error_per_sample2) / 2 else: # DDPG: single critic network q_value_dict['q_value'] = q_value.mean().data.item() target_q_value_dict['target q_value'] = target_q_value.mean().data.item() td_data = v_1step_td_data(q_value, target_q_value, reward, data['done'], data['weight']) critic_loss, td_error_per_sample = v_1step_td_error(td_data, self._gamma) loss_dict['critic_loss'] = critic_loss # ================ # critic update # ================ self._optimizer_critic.zero_grad() for k in loss_dict: if 'critic' in k: loss_dict[k].backward() self._optimizer_critic.step() # =============================== # actor learn forward and update # =============================== # actor updates every ``self._actor_update_freq`` iters if (self._forward_learn_cnt + 1) % self._actor_update_freq == 0: actor_data = self._learn_model.forward(data['obs'], mode='compute_actor') actor_data['obs'] = data['obs'] if self._twin_critic: actor_loss = -self._learn_model.forward(actor_data, mode='compute_critic')['q_value'][0].mean() else: actor_loss = -self._learn_model.forward(actor_data, mode='compute_critic')['q_value'].mean() loss_dict['actor_loss'] = actor_loss # actor update self._optimizer_actor.zero_grad() actor_loss.backward() self._optimizer_actor.step() # ============= # after update # ============= loss_dict['total_loss'] = sum(loss_dict.values()) self._forward_learn_cnt += 1 self._target_model.update(self._learn_model.state_dict()) if self._cfg.action_space == 'hybrid': action_log_value = -1. # TODO(nyz) better way to viz hybrid action else: action_log_value = data['action'].mean() return { 'cur_lr_actor': self._optimizer_actor.defaults['lr'], 'cur_lr_critic': self._optimizer_critic.defaults['lr'], # 'q_value': np.array(q_value).mean(), 'action': action_log_value, 'priority': td_error_per_sample.abs().tolist(), 'td_error': td_error_per_sample.abs().mean(), **loss_dict, **q_value_dict, **target_q_value_dict, }
[docs] def _state_dict_learn(self) -> Dict[str, Any]: """ Overview: Return the state_dict of learn mode, usually including model, target_model and optimizers. Returns: - state_dict (:obj:`Dict[str, Any]`): The dict of current policy learn state, for saving and restoring. """ return { 'model': self._learn_model.state_dict(), 'target_model': self._target_model.state_dict(), 'optimizer_actor': self._optimizer_actor.state_dict(), 'optimizer_critic': self._optimizer_critic.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._target_model.load_state_dict(state_dict['target_model']) self._optimizer_actor.load_state_dict(state_dict['optimizer_actor']) self._optimizer_critic.load_state_dict(state_dict['optimizer_critic'])
[docs] def _init_collect(self) -> None: """ Overview: Initialize the collect mode of policy, including related attributes and modules. For DDPG, it contains the \ collect_model to balance the exploration and exploitation with the perturbed noise mechanism, and other \ algorithm-specific arguments such as unroll_len. \ 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``. """ self._unroll_len = self._cfg.collect.unroll_len # collect model self._collect_model = model_wrap( self._model, wrapper_name='action_noise', noise_type='gauss', noise_kwargs={ 'mu': 0.0, 'sigma': self._cfg.collect.noise_sigma }, noise_range=None ) if self._cfg.action_space == 'hybrid': self._collect_model = model_wrap(self._collect_model, wrapper_name='hybrid_eps_greedy_multinomial_sample') self._collect_model.reset()
[docs] def _forward_collect(self, data: Dict[int, Any], **kwargs) -> 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 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. .. 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 DDPGPolicy: ``ding.policy.tests.test_ddpg``. """ 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', **kwargs) 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 DDPG, it contains obs, next_obs, action, reward, done. 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 DDPG, it contains the action and the logit of the action (in hybrid action space). - 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, 'next_obs': timestep.obs, 'action': policy_output['action'], 'reward': timestep.reward, 'done': timestep.done, } if self._cfg.action_space == 'hybrid': transition['logit'] = policy_output['logit'] 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 DDPG, a train sample is a processed transition (unroll_len=1). 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. """ return get_train_sample(transitions, self._unroll_len)
[docs] def _init_eval(self) -> None: """ Overview: Initialize the eval mode of policy, including related attributes and modules. For DDPG, it contains the \ eval model to greedily select action type with argmax q_value mechanism for hybrid action space. \ 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``. """ self._eval_model = model_wrap(self._model, wrapper_name='base') if self._cfg.action_space == 'hybrid': self._eval_model = model_wrap(self._eval_model, wrapper_name='hybrid_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. 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 DDPGPolicy: ``ding.policy.tests.test_ddpg``. """ 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. """ ret = [ 'cur_lr_actor', 'cur_lr_critic', 'critic_loss', 'actor_loss', 'total_loss', 'q_value', 'q_value_twin', 'action', 'td_error' ] if self._twin_critic: ret += ['critic_twin_loss'] return ret