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Source code for ding.policy.r2d3

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_error_with_rescale, get_nstep_return_data, \
    get_train_sample, dqfd_nstep_td_error, dqfd_nstep_td_error_with_rescale, dqfd_nstep_td_data
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('r2d3') class R2D3Policy(Policy): r""" Overview: Policy class of r2d3, from paper `Making Efficient Use of Demonstrations to Solve Hard Exploration Problems` . 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 0.997, | 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='r2d3', # (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 use priority(priority sample, IS weight, update priority) priority=True, # (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True. priority_IS_weight=True, # ============================================================== # The following configs are algorithm-specific # ============================================================== # (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=2, # (int) the trajectory length to unroll the RNN network minus # the timestep of burnin operation learn_unroll_len=80, learn=dict( update_per_collect=1, batch_size=64, learning_rate=0.0001, # ============================================================== # The following configs are algorithm-specific # ============================================================== # (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, ignore_done=False, ), collect=dict( # NOTE it is important that don't include key n_sample here, to make sure self._traj_len=INF # each_iter_n_sample=32, # `env_num` is used in hidden state, should equal to that one in env config. # User should specify this value in user config. env_num=None, ), eval=dict( # `env_num` is used in hidden state, should equal to that one in env config. # User should specify this value in user config. env_num=None, ), other=dict( eps=dict( type='exp', start=0.95, end=0.05, decay=10000, ), replay_buffer=dict(replay_buffer_size=10000, ), ), ) def default_model(self) -> Tuple[str, List[str]]: return 'drqn', ['ding.model.template.q_learning'] def _init_learn(self) -> None: r""" Overview: Init the learner model of r2d3Policy Arguments: .. note:: The _init_learn method takes the argument from the self._cfg.learn in the config file - learning_rate (:obj:`float`): The learning rate fo the optimizer - gamma (:obj:`float`): The discount factor - nstep (:obj:`int`): The num of n step return - value_rescale (:obj:`bool`): Whether to use value rescaled loss in algorithm - burnin_step (:obj:`int`): The num of step of burnin """ self.lambda1 = self._cfg.learn.lambda1 # n-step return self.lambda2 = self._cfg.learn.lambda2 # supervised loss self.lambda3 = self._cfg.learn.lambda3 # L2 self.lambda_one_step_td = self._cfg.learn.lambda_one_step_td # 1-step return # margin function in JE, here we implement this as a constant self.margin_function = self._cfg.learn.margin_function 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, weight_decay=self.lambda3, optim_type='adamw' ) 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: r""" 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, Any]`): the processed data, including at least \ ['main_obs', 'target_obs', 'burnin_obs', 'action', 'reward', 'done', 'weight'] - data_info (:obj:`dict`): the data info, such as replay_buffer_idx, replay_unique_id """ # 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) bs = 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-self._nstep] ignore_done = self._cfg.learn.ignore_done if ignore_done: data['done'] = [None for _ in range(self._sequence_len - bs)] else: data['done'] = data['done'][bs:].float() # 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-self._nstep], # below is two different implementation ways if 'value_gamma' not in data: data['value_gamma'] = [None for _ in range(self._sequence_len - bs)] else: data['value_gamma'] = data['value_gamma'][bs:] if 'weight' not in data: data['weight'] = [None for _ in range(self._sequence_len - bs)] 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 data['action'] = data['action'][bs:-self._nstep] data['reward'] = data['reward'][bs:-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 data['burnin_nstep_obs'] = data['obs'][:bs + 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'][bs:-self._nstep] # the target_obs is used to calculate the target_q_value data['target_obs'] = data['obs'][bs + self._nstep:] # TODO(pu) data['target_obs_one_step'] = data['obs'][bs + 1:] if ignore_done: data['done_one_step'] = [None for _ in range(self._sequence_len - bs)] else: data['done_one_step'] = data['done_one_step'][bs:].float() return data
[docs] def _forward_learn(self, data: dict) -> Dict[str, Any]: r""" Overview: Forward and backward function of learn mode. Acquire the data, calculate the loss and optimize learner model. Arguments: - data (:obj:`dict`): Dict type data, including at least \ ['main_obs', 'target_obs', 'burnin_obs', 'action', 'reward', 'done', 'weight'] Returns: - info_dict (:obj:`Dict[str, Any]`): Including cur_lr and total_loss - cur_lr (:obj:`float`): Current learning rate - total_loss (:obj:`float`): The calculated loss """ # forward data = self._data_preprocess_learn(data) self._learn_model.train() self._target_model.train() # take out the hidden state in timestep=0 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, self._burnin_step + 1] ) burnin_output_target = self._target_model.forward( inputs, saved_state_timesteps=[self._burnin_step, self._burnin_step + self._nstep, self._burnin_step + 1] ) 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'] # n-step 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'] # one-step self._learn_model.reset(data_id=None, state=burnin_output['saved_state'][2]) self._target_model.reset(data_id=None, state=burnin_output_target['saved_state'][2]) next_inputs_one_step = {'obs': data['target_obs_one_step'], 'enable_fast_timestep': True} with torch.no_grad(): target_q_value_one_step = self._target_model.forward(next_inputs_one_step)['logit'] # argmax_action double_dqn target_q_action_one_step = self._learn_model.forward(next_inputs_one_step)['action'] action, reward, done, weight = data['action'], data['reward'], data['done'], data['weight'] value_gamma = data['value_gamma'] done_one_step = data['done_one_step'] # T, B, nstep -> T, nstep, B reward = reward.permute(0, 2, 1).contiguous() loss = [] loss_nstep = [] loss_1step = [] loss_sl = [] 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 = dqfd_nstep_td_data( q_value[t], target_q_value[t], action[t], target_q_action[t], reward[t], done[t], done_one_step[t], weight[t], target_q_value_one_step[t], target_q_action_one_step[t], data['is_expert'][t], # is_expert flag(expert 1, agent 0) ) if self._value_rescale: l, e, loss_statistics = dqfd_nstep_td_error_with_rescale( td_data, self._gamma, self.lambda1, self.lambda2, self.margin_function, self.lambda_one_step_td, self._nstep, False, value_gamma=value_gamma[t], ) loss.append(l) # td_error.append(e.abs()) # first sum then abs td_error.append(e) # first abs then sum # loss statistics for debugging loss_nstep.append(loss_statistics[0]) loss_1step.append(loss_statistics[1]) loss_sl.append(loss_statistics[2]) else: l, e, loss_statistics = dqfd_nstep_td_error( td_data, self._gamma, self.lambda1, self.lambda2, self.margin_function, self.lambda_one_step_td, self._nstep, False, value_gamma=value_gamma[t], ) loss.append(l) # td_error.append(e.abs()) # first sum then abs td_error.append(e) # first abs then sum # loss statistics for debugging loss_nstep.append(loss_statistics[0]) loss_1step.append(loss_statistics[1]) loss_sl.append(loss_statistics[2]) loss = sum(loss) / (len(loss) + 1e-8) # loss statistics for debugging loss_nstep = sum(loss_nstep) / (len(loss_nstep) + 1e-8) loss_1step = sum(loss_1step) / (len(loss_1step) + 1e-8) loss_sl = sum(loss_sl) / (len(loss_sl) + 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)) # 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(), # loss statistics for debugging 'nstep_loss': loss_nstep.item(), '1step_loss': loss_1step.item(), 'sl_loss': loss_sl.item(), 'priority': td_error_per_sample.abs().tolist(), # 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(), }
def _reset_learn(self, data_id: Optional[List[int]] = None) -> None: self._learn_model.reset(data_id=data_id) def _state_dict_learn(self) -> Dict[str, Any]: 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: 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: r""" Overview: Collect mode init method. Called by ``self.__init__``. Init traj and unroll length, collect model. """ assert 'unroll_len' not in self._cfg.collect, "r2d3 use default unroll_len" 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 compatibility 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() def _forward_collect(self, data: dict, eps: float) -> dict: r""" Overview: Collect output according to eps_greedy plugin Arguments: - data (:obj:`dict`): Dict type data, including at least ['obs']. Returns: - data (:obj:`dict`): The collected data """ 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)} def _reset_collect(self, data_id: Optional[List[int]] = None) -> None: self._collect_model.reset(data_id=data_id) def _process_transition(self, obs: Any, model_output: dict, timestep: namedtuple) -> dict: r""" Overview: Generate dict type transition data from inputs. Arguments: - obs (:obj:`Any`): Env observation - model_output (:obj:`dict`): Output of collect model, including at least ['action', 'prev_state'] - timestep (:obj:`namedtuple`): Output after env step, including at least ['reward', 'done'] \ (here 'obs' indicates obs after env step). Returns: - transition (:obj:`dict`): Dict type transition data. """ transition = { 'obs': obs, 'action': model_output['action'], 'prev_state': model_output['prev_state'], 'reward': timestep.reward, 'done': timestep.done, } return transition def _get_train_sample(self, data: list) -> Union[None, List[Any]]: r""" Overview: Get the trajectory and the n step return data, then sample from the n_step return data Arguments: - data (:obj:`list`): The trajectory's cache Returns: - samples (:obj:`dict`): The training samples generated """ from copy import deepcopy data_one_step = deepcopy(get_nstep_return_data(data, 1, gamma=self._gamma)) data = get_nstep_return_data(data, self._nstep, gamma=self._gamma) for i in range(len(data)): # here we record the one-step done, we don't need record one-step reward, # because the n-step reward in data already include one-step reward data[i]['done_one_step'] = data_one_step[i]['done'] return get_train_sample(data, self._sequence_len) def _init_eval(self) -> None: r""" Overview: Evaluate mode init method. Called by ``self.__init__``. Init eval model with argmax strategy. """ 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) -> dict: r""" Overview: Forward function of collect mode, similar to ``self._forward_collect``. Arguments: - data (:obj:`dict`): Dict type data, including at least ['obs']. Returns: - output (:obj:`dict`): Dict type data, including at least inferred action according to input obs. """ 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)} def _reset_eval(self, data_id: Optional[List[int]] = None) -> None: self._eval_model.reset(data_id=data_id) def _monitor_vars_learn(self) -> List[str]: return super()._monitor_vars_learn() + [ 'total_loss', 'nstep_loss', '1step_loss', 'sl_loss', 'priority', 'q_s_taken-a_t0', 'target_q_s_max-a_t0', 'q_s_a-mean_t0' ]