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Source code for ding.policy.r2d2

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

import torch

from ding.model import model_wrap
from ding.rl_utils import q_nstep_td_data, q_nstep_td_error, q_nstep_td_error_with_rescale, get_nstep_return_data, \
    get_train_sample
from ding.torch_utils import Adam, to_device
from ding.utils import POLICY_REGISTRY
from ding.utils.data import timestep_collate, default_collate, default_decollate
from .base_policy import Policy


[docs]@POLICY_REGISTRY.register('r2d2') class R2D2Policy(Policy): """ Overview: Policy class of R2D2, from paper `Recurrent Experience Replay in Distributed Reinforcement Learning` . R2D2 proposes that several tricks should be used to improve upon DRQN, namely some recurrent experience replay \ tricks and the burn-in mechanism for off-policy training. Config: == ==================== ======== ============== ======================================== ======================= ID Symbol Type Default Value Description Other(Shape) == ==================== ======== ============== ======================================== ======================= 1 ``type`` str r2d2 | 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 0.997, | Reward's future discount factor, aka. | May be 1 when sparse | ``factor`` [0.95, 0.999] | gamma | reward env 7 ``nstep`` int 3, | N-step reward discount sum for target [3, 5] | q_value estimation 8 ``burnin_step`` int 2 | The timestep of burnin operation, | which is designed to RNN hidden state | difference caused by off-policy 9 | ``learn.update`` int 1 | How many updates(iterations) to train | This args can be vary | ``per_collect`` | after collector's one collection. Only | from envs. Bigger val | valid in serial training | means more off-policy 10 | ``learn.batch_`` int 64 | The number of samples of an iteration | ``size`` 11 | ``learn.learning`` float 0.001 | Gradient step length of an iteration. | ``_rate`` 12 | ``learn.value_`` bool True | Whether use value_rescale function for | ``rescale`` | predicted value 13 | ``learn.target_`` int 100 | Frequence of target network update. | Hard(assign) update | ``update_freq`` 14 | ``learn.ignore_`` bool False | Whether ignore done for target value | Enable it for some | ``done`` | calculation. | fake termination env 15 ``collect.n_sample`` int [8, 128] | The number of training samples of a | It varies from | call of collector. | different envs 16 | ``collect.unroll`` int 1 | unroll length of an iteration | In RNN, unroll_len>1 | ``_len`` == ==================== ======== ============== ======================================== ======================= """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='r2d2', # (bool) Whether to use cuda for network. cuda=False, # (bool) Whether the RL algorithm is on-policy or off-policy. on_policy=False, # (bool) Whether to use priority(priority sample, IS weight, update priority) priority=True, # (bool) Whether to use Importance Sampling Weight to correct biased update. If True, priority must be True. priority_IS_weight=True, # (float) Reward's future discount factor, aka. gamma. discount_factor=0.997, # (int) N-step reward for target q_value estimation nstep=5, # (int) the timestep of burnin operation, which is designed to RNN hidden state difference # caused by off-policy burnin_step=20, # (int) the trajectory length to unroll the RNN network minus # the timestep of burnin operation learn_unroll_len=80, # learn_mode config learn=dict( # (int) The number of training updates (iterations) to perform after each data collection by the collector. # A larger "update_per_collect" value implies a more off-policy approach. # The whole pipeline process follows this cycle: collect data -> update policy -> collect data -> ... update_per_collect=1, # (int) The number of samples in a training batch. batch_size=64, # (float) The step size of gradient descent, determining the rate of learning. learning_rate=0.0001, # (int) Frequence of target network update. # target_update_freq=100, target_update_theta=0.001, # (bool) whether use value_rescale function for predicted value value_rescale=True, # (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. # In each collect phase, we collect a total of <n_sample> sequence samples. n_sample=32, # (bool) It is important that set key traj_len_inf=True here, # to make sure self._traj_len=INF in serial_sample_collector.py. # In R2D2 policy, for each collect_env, we want to collect data of length self._traj_len=INF # unless the episode enters the 'done' state. traj_len_inf=True, # (int) `env_num` is used in hidden state, should equal to that one in env config (e.g. collector_env_num). # User should specify this value in user config. `None` is a placeholder. env_num=None, ), # eval_mode config eval=dict( # (int) `env_num` is used in hidden state, should equal to that one in env config (e.g. evaluator_env_num). # User should specify this value in user config. env_num=None, ), other=dict( # Epsilon greedy with decay. eps=dict( # (str) Type of decay. Supports either 'exp' (exponential) or 'linear'. type='exp', # (float) Initial value of epsilon at the start. start=0.95, # (float) Final value of epsilon after decay. end=0.05, # (int) The number of environment steps over which epsilon should decay. decay=10000, ), 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. .. 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 R2D2, its registered name is ``drqn`` and the import_names is \ ``ding.model.template.q_learning``. """ return 'drqn', ['ding.model.template.q_learning']
[docs] def _init_learn(self) -> None: """ Overview: Initialize the learn mode of policy, including some attributes and modules. For R2D2, it mainly contains \ optimizer, algorithm-specific arguments such as burnin_step, value_rescale and gamma, main and target \ model. Because of the use of RNN, all the models should be wrappered with ``hidden_state`` which needs to \ be initialized with proper size. 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 self._optimizer = Adam(self._model.parameters(), lr=self._cfg.learn.learning_rate) self._gamma = self._cfg.discount_factor self._nstep = self._cfg.nstep self._burnin_step = self._cfg.burnin_step self._value_rescale = self._cfg.learn.value_rescale self._target_model = copy.deepcopy(self._model) self._target_model = model_wrap( self._target_model, wrapper_name='target', update_type='momentum', update_kwargs={'theta': self._cfg.learn.target_update_theta} ) self._target_model = model_wrap( self._target_model, wrapper_name='hidden_state', state_num=self._cfg.learn.batch_size, ) self._learn_model = model_wrap( self._model, wrapper_name='hidden_state', state_num=self._cfg.learn.batch_size, ) self._learn_model = model_wrap(self._learn_model, wrapper_name='argmax_sample') self._learn_model.reset() self._target_model.reset()
def _data_preprocess_learn(self, data: List[Dict[str, Any]]) -> Dict[str, torch.Tensor]: """ Overview: Preprocess the data to fit the required data format for learning Arguments: - data (:obj:`List[Dict[str, Any]]`): The data collected from collect function Returns: - data (:obj:`Dict[str, torch.Tensor]`): The processed data, including at least \ ['main_obs', 'target_obs', 'burnin_obs', 'action', 'reward', 'done', 'weight'] """ # data preprocess data = timestep_collate(data) if self._cuda: data = to_device(data, self._device) if self._priority_IS_weight: assert self._priority, "Use IS Weight correction, but Priority is not used." if self._priority and self._priority_IS_weight: data['weight'] = data['IS'] else: data['weight'] = data.get('weight', None) burnin_step = self._burnin_step # data['done'], data['weight'], data['value_gamma'] is used in def _forward_learn() to calculate # the q_nstep_td_error, should be length of [self._sequence_len-self._burnin_step] ignore_done = self._cfg.learn.ignore_done if ignore_done: data['done'] = [None for _ in range(self._sequence_len - burnin_step)] else: data['done'] = data['done'][burnin_step:].float() # for computation of online model self._learn_model # NOTE that after the proprocessing of get_nstep_return_data() in _get_train_sample # the data['done'] [t] is already the n-step done # if the data don't include 'weight' or 'value_gamma' then fill in None in a list # with length of [self._sequence_len-self._burnin_step], # below is two different implementation ways if 'value_gamma' not in data: data['value_gamma'] = [None for _ in range(self._sequence_len - burnin_step)] else: data['value_gamma'] = data['value_gamma'][burnin_step:] if 'weight' not in data or data['weight'] is None: data['weight'] = [None for _ in range(self._sequence_len - burnin_step)] else: data['weight'] = data['weight'] * torch.ones_like(data['done']) # every timestep in sequence has same weight, which is the _priority_IS_weight in PER # cut the seq_len from burn_in step to (seq_len - nstep) step data['action'] = data['action'][burnin_step:-self._nstep] # cut the seq_len from burn_in step to (seq_len - nstep) step data['reward'] = data['reward'][burnin_step:-self._nstep] # the burnin_nstep_obs is used to calculate the init hidden state of rnn for the calculation of the q_value, # target_q_value, and target_q_action # these slicing are all done in the outermost layer, which is the seq_len dim data['burnin_nstep_obs'] = data['obs'][:burnin_step + self._nstep] # the main_obs is used to calculate the q_value, the [bs:-self._nstep] means using the data from # [bs] timestep to [self._sequence_len-self._nstep] timestep data['main_obs'] = data['obs'][burnin_step:-self._nstep] # the target_obs is used to calculate the target_q_value data['target_obs'] = data['obs'][burnin_step + self._nstep:] return data
[docs] def _forward_learn(self, data: List[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 (trajectory for R2D2) from the replay buffer and then \ returns the output result, including various training information such as loss, q value, priority. Arguments: - data (:obj:`List[List[Dict[int, Any]]]`): The input data used for policy forward, including a batch of \ training samples. For each dict element, 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 time and \ batch dimension by the utility functions ``self._data_preprocess_learn``. \ For R2D2, each element in list is a trajectory with the length of ``unroll_len``, and the element in \ trajectory list is a dict containing at least the following keys: ``obs``, ``action``, ``prev_state``, \ ``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 R2D2Policy: ``ding.policy.tests.test_r2d2``. """ # forward data = self._data_preprocess_learn(data) # output datatype: Dict self._learn_model.train() self._target_model.train() # use the hidden state in timestep=0 # note the reset method is performed at the hidden state wrapper, to reset self._state. self._learn_model.reset(data_id=None, state=data['prev_state'][0]) self._target_model.reset(data_id=None, state=data['prev_state'][0]) if len(data['burnin_nstep_obs']) != 0: with torch.no_grad(): inputs = {'obs': data['burnin_nstep_obs'], 'enable_fast_timestep': True} burnin_output = self._learn_model.forward( inputs, saved_state_timesteps=[self._burnin_step, self._burnin_step + self._nstep] ) # keys include 'logit', 'hidden_state' 'saved_state', \ # 'action', for their specific dim, please refer to DRQN model burnin_output_target = self._target_model.forward( inputs, saved_state_timesteps=[self._burnin_step, self._burnin_step + self._nstep] ) self._learn_model.reset(data_id=None, state=burnin_output['saved_state'][0]) inputs = {'obs': data['main_obs'], 'enable_fast_timestep': True} q_value = self._learn_model.forward(inputs)['logit'] self._learn_model.reset(data_id=None, state=burnin_output['saved_state'][1]) self._target_model.reset(data_id=None, state=burnin_output_target['saved_state'][1]) next_inputs = {'obs': data['target_obs'], 'enable_fast_timestep': True} with torch.no_grad(): target_q_value = self._target_model.forward(next_inputs)['logit'] # argmax_action double_dqn target_q_action = self._learn_model.forward(next_inputs)['action'] action, reward, done, weight = data['action'], data['reward'], data['done'], data['weight'] value_gamma = data['value_gamma'] # T, B, nstep -> T, nstep, B reward = reward.permute(0, 2, 1).contiguous() loss = [] td_error = [] for t in range(self._sequence_len - self._burnin_step - self._nstep): # here t=0 means timestep <self._burnin_step> in the original sample sequence, we minus self._nstep # because for the last <self._nstep> timestep in the sequence, we don't have their target obs td_data = q_nstep_td_data( q_value[t], target_q_value[t], action[t], target_q_action[t], reward[t], done[t], weight[t] ) if self._value_rescale: l, e = q_nstep_td_error_with_rescale(td_data, self._gamma, self._nstep, value_gamma=value_gamma[t]) loss.append(l) td_error.append(e.abs()) else: l, e = q_nstep_td_error(td_data, self._gamma, self._nstep, value_gamma=value_gamma[t]) loss.append(l) # td will be a list of the length # <self._sequence_len - self._burnin_step - self._nstep> # and each value is a tensor of the size batch_size td_error.append(e.abs()) loss = sum(loss) / (len(loss) + 1e-8) # using the mixture of max and mean absolute n-step TD-errors as the priority of the sequence td_error_per_sample = 0.9 * torch.max( torch.stack(td_error), dim=0 )[0] + (1 - 0.9) * (torch.sum(torch.stack(td_error), dim=0) / (len(td_error) + 1e-8)) # torch.max(torch.stack(td_error), dim=0) will return tuple like thing, please refer to torch.max # td_error shape list(<self._sequence_len-self._burnin_step-self._nstep>, B), # for example, (75,64) # torch.sum(torch.stack(td_error), dim=0) can also be replaced with sum(td_error) # update self._optimizer.zero_grad() loss.backward() self._optimizer.step() # after update self._target_model.update(self._learn_model.state_dict()) # the information for debug batch_range = torch.arange(action[0].shape[0]) q_s_a_t0 = q_value[0][batch_range, action[0]] target_q_s_a_t0 = target_q_value[0][batch_range, target_q_action[0]] return { 'cur_lr': self._optimizer.defaults['lr'], 'total_loss': loss.item(), 'priority': td_error_per_sample.tolist(), # note abs operation has been performed above # the first timestep in the sequence, may not be the start of episode 'q_s_taken-a_t0': q_s_a_t0.mean().item(), 'target_q_s_max-a_t0': target_q_s_a_t0.mean().item(), 'q_s_a-mean_t0': q_value[0].mean().item(), }
[docs] def _reset_learn(self, data_id: Optional[List[int]] = None) -> None: """ Overview: Reset some stateful variables for learn mode when necessary, such as the hidden state of RNN or the \ memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \ varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \ different trajectories in ``data_id`` will have different hidden state in RNN. Arguments: - data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \ (i.e. RNN hidden_state in R2D2) specified by ``data_id``. """ self._learn_model.reset(data_id=data_id)
[docs] 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(), }
[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.load_state_dict(state_dict['optimizer'])
[docs] def _init_collect(self) -> None: """ Overview: Initialize the collect mode of policy, including related attributes and modules. For R2D2, it contains the \ collect_model to balance the exploration and exploitation with epsilon-greedy sample mechanism and \ maintain the hidden state of rnn. Besides, there are some initialization operations about other \ algorithm-specific arguments such as burnin_step, 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 R2D2. This \ design is for the convenience of parallel execution of different policy modes. """ self._nstep = self._cfg.nstep self._burnin_step = self._cfg.burnin_step self._gamma = self._cfg.discount_factor self._sequence_len = self._cfg.learn_unroll_len + self._cfg.burnin_step self._unroll_len = self._sequence_len # for r2d2, this hidden_state wrapper is to add the 'prev hidden state' for each transition. # Note that collect env forms a batch and the key is added for the batch simultaneously. self._collect_model = model_wrap( self._model, wrapper_name='hidden_state', state_num=self._cfg.collect.env_num, save_prev_state=True ) self._collect_model = model_wrap(self._collect_model, wrapper_name='eps_greedy_sample') self._collect_model.reset()
[docs] 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 (prev_state) 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:: RNN's hidden states are maintained in the policy, so we don't need pass them into data but to reset the \ hidden states with ``_reset_collect`` method when episode ends. Besides, the previous hidden states are \ necessary for training, so we need to return them in ``_process_transition`` 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 R2D2Policy: ``ding.policy.tests.test_r2d2``. """ data_id = list(data.keys()) data = default_collate(list(data.values())) if self._cuda: data = to_device(data, self._device) data = {'obs': data} self._collect_model.eval() with torch.no_grad(): # in collect phase, inference=True means that each time we only pass one timestep data, # so the we can get the hidden state of rnn: <prev_state> at each timestep. output = self._collect_model.forward(data, data_id=data_id, eps=eps, inference=True) 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 _reset_collect(self, data_id: Optional[List[int]] = None) -> None: """ Overview: Reset some stateful variables for eval mode when necessary, such as the hidden state of RNN or the \ memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \ varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \ different environments/episodes in evaluation in ``data_id`` will have different hidden state in RNN. Arguments: - data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \ (i.e., RNN hidden_state in R2D2) specified by ``data_id``. """ self._collect_model.reset(data_id=data_id)
[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 R2D2, it contains obs, action, prev_state, reward, and 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 given the observation \ as input. For R2D2, it contains the action and the prev_state of RNN. - 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'], 'prev_state': policy_output['prev_state'], '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 R2D2, a train sample is processed transitions with unroll_len \ length. 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 sample is a fixed-length \ trajectory, and each element in a sample is the similar format as input transitions, but may contain \ more data for training, such as nstep reward and value_gamma factor. """ transitions = get_nstep_return_data(transitions, self._nstep, gamma=self._gamma) return get_train_sample(transitions, self._unroll_len)
def _init_eval(self) -> None: """ Overview: Initialize the eval mode of policy, including related attributes and modules. For R2D2, it contains the \ eval model to greedily select action with argmax q_value mechanism and main the hidden state. 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='hidden_state', state_num=self._cfg.eval.env_num) self._eval_model = model_wrap(self._eval_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. ``_forward_eval`` often use argmax sample method to get actions that \ q_value is the highest. 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:: RNN's hidden states are maintained in the policy, so we don't need pass them into data but to reset the \ hidden states with ``_reset_eval`` method when the episode ends. .. 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 R2D2Policy: ``ding.policy.tests.test_r2d2``. """ data_id = list(data.keys()) data = default_collate(list(data.values())) if self._cuda: data = to_device(data, self._device) data = {'obs': data} self._eval_model.eval() with torch.no_grad(): output = self._eval_model.forward(data, data_id=data_id, inference=True) 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 _reset_eval(self, data_id: Optional[List[int]] = None) -> None: """ Overview: Reset some stateful variables for eval mode when necessary, such as the hidden state of RNN or the \ memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \ varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \ different environments/episodes in evaluation in ``data_id`` will have different hidden state in RNN. Arguments: - data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \ (i.e., RNN hidden_state in R2D2) specified by ``data_id``. """ self._eval_model.reset(data_id=data_id)
[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() + [ 'total_loss', 'priority', 'q_s_taken-a_t0', 'target_q_s_max-a_t0', 'q_s_a-mean_t0' ]