from typing import List, Dict, Any, Tuple from collections import namedtuple import copy import torch from ding.torch_utils import Adam, to_device, ContrastiveLoss from ding.rl_utils import q_nstep_td_data, q_nstep_td_error, get_nstep_return_data, 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 @POLICY_REGISTRY.register('dqn') class DQNPolicy(Policy): """ Overview: Policy class of DQN algorithm, extended by Double DQN/Dueling DQN/PER/multi-step TD. Config: == ===================== ======== ============== ======================================= ======================= ID Symbol Type Default Value Description Other(Shape) == ===================== ======== ============== ======================================= ======================= 1 ``type`` str dqn | RL policy register name, refer to | This arg is optional, | registry ``POLICY_REGISTRY`` | a placeholder 2 ``cuda`` bool False | Whether to use cuda for network | This arg can be diff- | erent from modes 3 ``on_policy`` bool False | Whether the RL algorithm is on-policy | or off-policy 4 ``priority`` bool False | Whether use priority(PER) | Priority sample, | update priority 5 | ``priority_IS`` bool False | Whether use Importance Sampling | ``_weight`` | Weight to correct biased update. If | True, priority must be True. 6 | ``discount_`` float 0.97, | Reward's future discount factor, aka. | May be 1 when sparse | ``factor`` [0.95, 0.999] | gamma | reward env 7 ``nstep`` int 1, | N-step reward discount sum for target [3, 5] | q_value estimation 8 | ``model.dueling`` bool True | dueling head architecture 9 | ``model.encoder`` list [32, 64, | Sequence of ``hidden_size`` of | default kernel_size | ``_hidden`` (int) 64, 128] | subsequent conv layers and the | is [8, 4, 3] | ``_size_list`` | final dense layer. | default stride is | [4, 2 ,1] 10 | ``model.dropout`` float None | Dropout rate for dropout layers. | [0,1] | If set to ``None`` | means no dropout 11 | ``learn.update`` int 3 | How many updates(iterations) to train | This args can be vary | ``per_collect`` | after collector's one collection. | from envs. Bigger val | Only valid in serial training | means more off-policy 12 | ``learn.batch_`` int 64 | The number of samples of an iteration | ``size`` 13 | ``learn.learning`` float 0.001 | Gradient step length of an iteration. | ``_rate`` 14 | ``learn.target_`` int 100 | Frequence of target network update. | Hard(assign) update | ``update_freq`` 15 | ``learn.target_`` float 0.005 | Frequence of target network update. | Soft(assign) update | ``theta`` | Only one of [target_update_freq, | | target_theta] should be set 16 | ``learn.ignore_`` bool False | Whether ignore done for target value | Enable it for some | ``done`` | calculation. | fake termination env 17 ``collect.n_sample`` int [8, 128] | The number of training samples of a | It varies from | call of collector. | different envs 18 ``collect.n_episode`` int 8 | The number of training episodes of a | only one of [n_sample | call of collector | ,n_episode] should | | be set 19 | ``collect.unroll`` int 1 | unroll length of an iteration | In RNN, unroll_len>1 | ``_len`` 20 | ``other.eps.type`` str exp | exploration rate decay type | Support ['exp', | 'linear']. 21 | ``other.eps.`` float 0.95 | start value of exploration rate | [0,1] | ``start`` 22 | ``other.eps.`` float 0.1 | end value of exploration rate | [0,1] | ``end`` 23 | ``other.eps.`` int 10000 | decay length of exploration | greater than 0. set | ``decay`` | decay=10000 means | the exploration rate | decay from start | value to end value | during decay length. == ===================== ======== ============== ======================================= ======================= """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='dqn', # (bool) Whether to use cuda in policy. cuda=False, # (bool) Whether learning policy is the same as collecting data policy(on-policy). 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, # (float) Discount factor(gamma) for returns. discount_factor=0.97, # (int) The number of step for calculating target q_value. nstep=1, model=dict( # (list(int)) Sequence of ``hidden_size`` of subsequent conv layers and the final dense layer. encoder_hidden_size_list=[128, 128, 64], ), # 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=3, # (int) How many samples in a training batch. batch_size=64, # (float) The step size of gradient descent. learning_rate=0.001, # (int) Frequence of target network update. # Only one of [target_update_freq, target_theta] should be set. target_update_freq=100, # (float) : Used for soft update of the target network. # aka. Interpolation factor in EMA update for target network. # Only one of [target_update_freq, target_theta] should be set. target_theta=0.005, # (bool) Whether ignore done(usually for max step termination env). # 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 done is 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, ), # 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=8, # (int) Split episodes or trajectories into pieces with length `unroll_len`. unroll_len=1, ), eval=dict(), # for compability # other config other=dict( # Epsilon greedy with decay. eps=dict( # (str) Decay type. Support ['exp', 'linear']. type='exp', # (float) Epsilon start value. start=0.95, # (float) Epsilon end value. end=0.1, # (int) Decay length(env step). decay=10000, ), replay_buffer=dict( # (int) Maximum size of replay buffer. Usually, larger buffer size is better. replay_buffer_size=10000, ), ), ) 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 DQN, its registered name is ``dqn`` and the import_names is \ ``ding.model.template.q_learning``. """ return 'dqn', ['ding.model.template.q_learning'] def _init_learn(self) -> None: """ Overview: Initialize the learn mode of policy, including related attributes and modules. For DQN, it mainly contains \ optimizer, algorithm-specific arguments such as nstep and gamma, 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 # Optimizer self._optimizer = Adam(self._model.parameters(), lr=self._cfg.learn.learning_rate) self._gamma = self._cfg.discount_factor self._nstep = self._cfg.nstep # use model_wrapper for specialized demands of different modes self._target_model = copy.deepcopy(self._model) if 'target_update_freq' in self._cfg.learn: self._target_model = model_wrap( self._target_model, wrapper_name='target', update_type='assign', update_kwargs={'freq': self._cfg.learn.target_update_freq} ) elif 'target_theta' in self._cfg.learn: self._target_model = model_wrap( self._target_model, wrapper_name='target', update_type='momentum', update_kwargs={'theta': self._cfg.learn.target_theta} ) else: raise RuntimeError("DQN needs target network, please either indicate target_update_freq or target_theta") self._learn_model = model_wrap(self._model, wrapper_name='argmax_sample') self._learn_model.reset() self._target_model.reset() 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, q value, 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 DQN, 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 ``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. .. note:: For more detailed examples, please refer to our unittest for DQNPolicy: ``ding.policy.tests.test_dqn``. """ # Data preprocessing operations, such as stack data, cpu to cuda device data = default_preprocess_learn( data, use_priority=self._priority, use_priority_IS_weight=self._cfg.priority_IS_weight, ignore_done=self._cfg.learn.ignore_done, use_nstep=True ) if self._cuda: data = to_device(data, self._device) # Q-learning forward self._learn_model.train() self._target_model.train() # Current q value (main model) q_value = self._learn_model.forward(data['obs'])['logit'] # Target q value with torch.no_grad(): target_q_value = self._target_model.forward(data['next_obs'])['logit'] # Max q value action (main model), i.e. Double DQN target_q_action = self._learn_model.forward(data['next_obs'])['action'] data_n = q_nstep_td_data( q_value, target_q_value, data['action'], target_q_action, data['reward'], data['done'], data['weight'] ) value_gamma = data.get('value_gamma') loss, td_error_per_sample = q_nstep_td_error(data_n, self._gamma, nstep=self._nstep, value_gamma=value_gamma) # Update network parameters self._optimizer.zero_grad() loss.backward() if self._cfg.multi_gpu: self.sync_gradients(self._learn_model) self._optimizer.step() # Postprocessing operations, such as updating target model, return logged values and priority. self._target_model.update(self._learn_model.state_dict()) return { 'cur_lr': self._optimizer.defaults['lr'], 'total_loss': loss.item(), 'q_value': q_value.mean().item(), 'target_q_value': target_q_value.mean().item(), 'priority': td_error_per_sample.abs().tolist(), # Only discrete action satisfying len(data['action'])==1 can return this and draw histogram on tensorboard. # '[histogram]action_distribution': data['action'], } 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 ['cur_lr', 'total_loss', 'q_value', 'target_q_value'] def _state_dict_learn(self) -> Dict[str, Any]: """ Overview: Return the state_dict of learn mode, usually including model, target_model and optimizer. Returns: - state_dict (:obj:`Dict[str, Any]`): The dict of current policy learn state, for saving and restoring. """ return { 'model': self._learn_model.state_dict(), 'target_model': self._target_model.state_dict(), 'optimizer': self._optimizer.state_dict(), } def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None: """ Overview: Load the state_dict variable into policy learn mode. Arguments: - state_dict (:obj:`Dict[str, Any]`): The dict of policy learn state saved before. .. tip:: If you want to only load some parts of model, you can simply set the ``strict`` argument in \ load_state_dict to ``False``, or refer to ``ding.torch_utils.checkpoint_helper`` for more \ complicated operation. """ self._learn_model.load_state_dict(state_dict['model']) self._target_model.load_state_dict(state_dict['target_model']) self._optimizer.load_state_dict(state_dict['optimizer']) def _init_collect(self) -> None: """ Overview: Initialize the collect mode of policy, including related attributes and modules. For DQN, it contains the \ collect_model to balance the exploration and exploitation with epsilon-greedy sample mechanism, and other \ algorithm-specific arguments such as unroll_len and nstep. 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 nstep in DQN. This \ design is for the convenience of parallel execution of different policy modes. """ self._unroll_len = self._cfg.collect.unroll_len self._gamma = self._cfg.discount_factor # necessary for parallel self._nstep = self._cfg.nstep # necessary for parallel self._collect_model = model_wrap(self._model, wrapper_name='eps_greedy_sample') self._collect_model.reset() def _forward_collect(self, data: Dict[int, Any], eps: float) -> 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. Besides, this policy also needs ``eps`` argument for \ exploration, i.e., classic epsilon-greedy exploration strategy. 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. - eps (:obj:`float`): The epsilon value for exploration. 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 DQNPolicy: ``ding.policy.tests.test_dqn``. """ data_id = list(data.keys()) data = default_collate(list(data.values())) if self._cuda: data = to_device(data, self._device) self._collect_model.eval() with torch.no_grad(): output = self._collect_model.forward(data, eps=eps) if self._cuda: output = to_device(output, 'cpu') output = default_decollate(output) return {i: d for i, d in zip(data_id, output)} 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 DQN with nstep TD, a train sample is a processed transition. \ 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 \ in 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 similar in format \ to input transitions, but may contain more data for training, such as nstep reward and target obs. """ transitions = get_nstep_return_data(transitions, self._nstep, gamma=self._gamma) return get_train_sample(transitions, self._unroll_len) 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 DQN, 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 DQN, it contains the action and the logit (q_value) 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, 'next_obs': timestep.obs, 'action': policy_output['action'], 'reward': timestep.reward, 'done': timestep.done, } return transition def _init_eval(self) -> None: """ Overview: Initialize the eval mode of policy, including related attributes and modules. For DQN, it contains the \ eval model to greedily select action with argmax q_value mechanism. 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='argmax_sample') self._eval_model.reset() 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 DQNPolicy: ``ding.policy.tests.test_dqn``. """ 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)} def calculate_priority(self, data: Dict[int, Any], update_target_model: bool = False) -> Dict[str, Any]: """ Overview: Calculate priority for replay buffer. Arguments: - data (:obj:`Dict[str, Any]`): Dict type data, a batch of data for training. - update_target_model (:obj:`bool`): Whether to update target model. Returns: - priority (:obj:`Dict[str, Any]`): Dict type priority data, values are python scalar or a list of scalars. ArgumentsKeys: - necessary: ``obs``, ``action``, ``reward``, ``next_obs``, ``done`` - optional: ``value_gamma`` ReturnsKeys: - necessary: ``priority`` """ if update_target_model: self._target_model.load_state_dict(self._learn_model.state_dict()) data = default_preprocess_learn( data, use_priority=False, use_priority_IS_weight=False, ignore_done=self._cfg.learn.ignore_done, use_nstep=True ) if self._cuda: data = to_device(data, self._device) # ==================== # Q-learning forward # ==================== self._learn_model.eval() self._target_model.eval() with torch.no_grad(): # Current q value (main model) q_value = self._learn_model.forward(data['obs'])['logit'] # Target q value target_q_value = self._target_model.forward(data['next_obs'])['logit'] # Max q value action (main model), i.e. Double DQN target_q_action = self._learn_model.forward(data['next_obs'])['action'] data_n = q_nstep_td_data( q_value, target_q_value, data['action'], target_q_action, data['reward'], data['done'], data['weight'] ) value_gamma = data.get('value_gamma') loss, td_error_per_sample = q_nstep_td_error( data_n, self._gamma, nstep=self._nstep, value_gamma=value_gamma ) return {'priority': td_error_per_sample.abs().tolist()} @POLICY_REGISTRY.register('dqn_stdim') class DQNSTDIMPolicy(DQNPolicy): """ Overview: Policy class of DQN algorithm, extended by ST-DIM auxiliary objectives. ST-DIM paper link: https://arxiv.org/abs/1906.08226. Config: == ==================== ======== ============== ======================================== ======================= ID Symbol Type Default Value Description Other(Shape) == ==================== ======== ============== ======================================== ======================= 1 ``type`` str dqn_stdim | RL policy register name, refer to | This arg is optional, | registry ``POLICY_REGISTRY`` | a placeholder 2 ``cuda`` bool False | Whether to use cuda for network | This arg can be diff- | erent from modes 3 ``on_policy`` bool False | Whether the RL algorithm is on-policy | or off-policy 4 ``priority`` bool False | Whether use priority(PER) | Priority sample, | update priority 5 | ``priority_IS`` bool False | Whether use Importance Sampling Weight | ``_weight`` | to correct biased update. If True, | priority must be True. 6 | ``discount_`` float 0.97, | Reward's future discount factor, aka. | May be 1 when sparse | ``factor`` [0.95, 0.999] | gamma | reward env 7 ``nstep`` int 1, | N-step reward discount sum for target [3, 5] | q_value estimation 8 | ``learn.update`` int 3 | How many updates(iterations) to train | This args can be vary | ``per_collect`` | after collector's one collection. Only | from envs. Bigger val | valid in serial training | means more off-policy | ``_gpu`` 10 | ``learn.batch_`` int 64 | The number of samples of an iteration | ``size`` 11 | ``learn.learning`` float 0.001 | Gradient step length of an iteration. | ``_rate`` 12 | ``learn.target_`` int 100 | Frequence of target network update. | Hard(assign) update | ``update_freq`` 13 | ``learn.ignore_`` bool False | Whether ignore done for target value | Enable it for some | ``done`` | calculation. | fake termination env 14 ``collect.n_sample`` int [8, 128] | The number of training samples of a | It varies from | call of collector. | different envs 15 | ``collect.unroll`` int 1 | unroll length of an iteration | In RNN, unroll_len>1 | ``_len`` 16 | ``other.eps.type`` str exp | exploration rate decay type | Support ['exp', | 'linear']. 17 | ``other.eps.`` float 0.95 | start value of exploration rate | [0,1] | ``start`` 18 | ``other.eps.`` float 0.1 | end value of exploration rate | [0,1] | ``end`` 19 | ``other.eps.`` int 10000 | decay length of exploration | greater than 0. set | ``decay`` | decay=10000 means | the exploration rate | decay from start | value to end value | during decay length. 20 | ``aux_loss`` float 0.001 | the ratio of the auxiliary loss to | any real value, | ``_weight`` | the TD loss | typically in | [-0.1, 0.1]. == ==================== ======== ============== ======================================== ======================= """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='dqn_stdim', # (bool) Whether to use cuda in policy. cuda=False, # (bool) Whether to learning policy is the same as collecting data policy (on-policy). 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, # (float) Discount factor(gamma) for returns. discount_factor=0.97, # (int) The number of step for calculating target q_value. nstep=1, # (float) The weight of auxiliary loss to main loss. aux_loss_weight=0.001, # learn_mode config learn=dict( # 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=3, # (int) How many samples in a training batch. batch_size=64, # (float) The step size of gradient descent. learning_rate=0.001, # (int) Frequence of target network update. target_update_freq=100, # (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=8, # (int) Cut trajectories into pieces with length "unroll_len". unroll_len=1, ), eval=dict(), # for compability # other config other=dict( # Epsilon greedy with decay. eps=dict( # (str) Decay type. Support ['exp', 'linear']. type='exp', # (float) Epsilon start value. start=0.95, # (float) Epsilon end value. end=0.1, # (int) Decay length (env step). decay=10000, ), replay_buffer=dict( # (int) Maximum size of replay buffer. Usually, larger buffer size is better. replay_buffer_size=10000, ), ), ) def _init_learn(self) -> None: """ Overview: Initialize the learn mode of policy, including related attributes and modules. For DQNSTDIM, it first \ call super class's ``_init_learn`` method, then initialize extra auxiliary model, its optimizer, and the \ loss weight. 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``. """ 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) -> Tuple[Tuple[int], Tuple[int]]: """ Overview: Get the input encoding size of the ST-DIM axuiliary model. Returns: - info_dict (:obj:`Tuple[Tuple[int], Tuple[int]]`): 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:] def _model_encode(self, data: dict) -> Tuple[torch.Tensor]: """ 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[torch.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 def _forward_learn(self, data: 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, q value, priority, aux_loss. 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 DQNSTDIM, 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 ``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, use_priority=self._priority, use_priority_IS_weight=self._cfg.priority_IS_weight, ignore_done=self._cfg.learn.ignore_done, use_nstep=True ) if self._cuda: data = to_device(data, self._device) # ====================== # 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(data) # 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() # ==================== # Q-learning forward # ==================== self._learn_model.train() self._target_model.train() # Current q value (main model) q_value = self._learn_model.forward(data['obs'])['logit'] # Target q value with torch.no_grad(): target_q_value = self._target_model.forward(data['next_obs'])['logit'] # Max q value action (main model) target_q_action = self._learn_model.forward(data['next_obs'])['action'] data_n = q_nstep_td_data( q_value, target_q_value, data['action'], target_q_action, data['reward'], data['done'], data['weight'] ) value_gamma = data.get('value_gamma') bellman_loss, td_error_per_sample = q_nstep_td_error( data_n, self._gamma, nstep=self._nstep, value_gamma=value_gamma ) # ====================== # Compute auxiliary loss # ====================== x, y = self._model_encode(data) self._aux_model.eval() aux_loss_eval = self._aux_model.forward(x, y) * self._aux_loss_weight loss = aux_loss_eval + bellman_loss # ==================== # Q-learning update # ==================== self._optimizer.zero_grad() loss.backward() if self._cfg.multi_gpu: self.sync_gradients(self._learn_model) self._optimizer.step() # ============= # after update # ============= self._target_model.update(self._learn_model.state_dict()) return { 'cur_lr': self._optimizer.defaults['lr'], 'bellman_loss': bellman_loss.item(), 'aux_loss_learn': aux_loss_learn.item(), 'aux_loss_eval': aux_loss_eval.item(), 'total_loss': loss.item(), 'q_value': q_value.mean().item(), 'priority': td_error_per_sample.abs().tolist(), # Only discrete action satisfying len(data['action'])==1 can return this and draw histogram on tensorboard. # '[histogram]action_distribution': data['action'], } 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 ['cur_lr', 'bellman_loss', 'aux_loss_learn', 'aux_loss_eval', 'total_loss', 'q_value'] def _state_dict_learn(self) -> Dict[str, Any]: """ Overview: Return the state_dict of learn mode, usually including model and optimizer. Returns: - state_dict (:obj:`Dict[str, Any]`): the dict of current policy learn state, for saving and restoring. """ return { 'model': self._learn_model.state_dict(), 'target_model': self._target_model.state_dict(), 'optimizer': self._optimizer.state_dict(), 'aux_optimizer': self._aux_optimizer.state_dict(), } 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.load_state_dict(state_dict['optimizer']) self._aux_optimizer.load_state_dict(state_dict['aux_optimizer'])