Shortcuts

Source code for ding.policy.rainbow

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

from ding.torch_utils import Adam, to_device
from ding.rl_utils import dist_nstep_td_data, dist_nstep_td_error, get_train_sample, get_nstep_return_data
from ding.model import model_wrap
from ding.utils import POLICY_REGISTRY
from ding.utils.data import default_collate, default_decollate
from .dqn import DQNPolicy
from .common_utils import default_preprocess_learn


[docs]@POLICY_REGISTRY.register('rainbow') class RainbowDQNPolicy(DQNPolicy): r""" Overview: Rainbow DQN contain several improvements upon DQN, including: - target network - dueling architecture - prioritized experience replay - n_step return - noise net - distribution net Therefore, the RainbowDQNPolicy class inherit upon DQNPolicy class Config: == ==================== ======== ============== ======================================== ======================= ID Symbol Type Default Value Description Other(Shape) == ==================== ======== ============== ======================================== ======================= 1 ``type`` str rainbow | 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 True | Whether use priority(PER) | priority sample, | update priority 5 ``model.v_min`` float -10 | Value of the smallest atom | in the support set. 6 ``model.v_max`` float 10 | Value of the largest atom | in the support set. 7 ``model.n_atom`` int 51 | Number of atoms in the support set | of the value distribution. 8 | ``other.eps`` float 0.05 | Start value for epsilon decay. It's | ``.start`` | small because rainbow use noisy net. 9 | ``other.eps`` float 0.05 | End value for epsilon decay. | ``.end`` 10 | ``discount_`` float 0.97, | Reward's future discount factor, aka. | may be 1 when sparse | ``factor`` [0.95, 0.999] | gamma | reward env 11 ``nstep`` int 3, | N-step reward discount sum for target [3, 5] | q_value estimation 12 | ``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 == ==================== ======== ============== ======================================== ======================= """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='rainbow', # (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, # (int) Number of training samples(randomly collected) in replay buffer when training starts. # random_collect_size=2000, model=dict( # (float) Value of the smallest atom in the support set. # Default to -10.0. v_min=-10, # (float) Value of the smallest atom in the support set. # Default to 10.0. v_max=10, # (int) Number of atoms in the support set of the # value distribution. Default to 51. n_atom=51, ), # (float) Reward's future discount factor, aka. gamma. discount_factor=0.99, # (int) N-step reward for target q_value estimation nstep=3, 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=1, batch_size=32, learning_rate=0.001, # ============================================================== # The following configs are algorithm-specific # ============================================================== # (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) Only one of [n_sample, n_episode] shoule be set # n_sample=32, # (int) Cut trajectories into pieces with length "unroll_len". unroll_len=1, ), eval=dict(), # other config other=dict( # Epsilon greedy with decay. eps=dict( # (str) Decay type. Support ['exp', 'linear']. type='exp', # (float) End value for epsilon decay, in [0, 1]. It's equals to `end` because rainbow uses noisy net. start=0.05, # (float) End value for epsilon decay, in [0, 1]. end=0.05, # (int) Env steps of epsilon decay. decay=100000, ), replay_buffer=dict( # (int) Max size of replay buffer. replay_buffer_size=100000, # (float) Prioritization exponent. alpha=0.6, # (float) Importance sample soft coefficient. # 0 means no correction, while 1 means full correction beta=0.4, # (int) Anneal step for beta: 0 means no annealing. Defaults to 0 anneal_step=100000, ) ), ) def default_model(self) -> Tuple[str, List[str]]: return 'rainbowdqn', ['ding.model.template.q_learning'] def _init_learn(self) -> None: r""" Overview: Init the learner model of RainbowDQNPolicy Arguments: - 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 - v_min (:obj:`float`): value distribution minimum value - v_max (:obj:`float`): value distribution maximum value - n_atom (:obj:`int`): the number of atom sample point """ 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._v_max = self._cfg.model.v_max self._v_min = self._cfg.model.v_min self._n_atom = self._cfg.model.n_atom self._target_model = copy.deepcopy(self._model) self._target_model = model_wrap( self._target_model, wrapper_name='target', update_type='assign', update_kwargs={'freq': self._cfg.learn.target_update_freq} ) self._learn_model = model_wrap(self._model, wrapper_name='argmax_sample') self._learn_model.reset() self._target_model.reset() def _forward_learn(self, data: dict) -> Dict[str, Any]: """ Overview: Forward and backward function of learn mode, acquire the data and calculate the loss and\ optimize learner model Arguments: - data (:obj:`dict`): Dict type data, including at least ['obs', 'next_obs', 'reward', 'action'] 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 """ 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) # ==================== # Rainbow forward # ==================== self._learn_model.train() self._target_model.train() # reset noise of noisenet for both main model and target model self._reset_noise(self._learn_model) self._reset_noise(self._target_model) q_dist = self._learn_model.forward(data['obs'])['distribution'] with torch.no_grad(): target_q_dist = self._target_model.forward(data['next_obs'])['distribution'] self._reset_noise(self._learn_model) target_q_action = self._learn_model.forward(data['next_obs'])['action'] value_gamma = data.get('value_gamma', None) data = dist_nstep_td_data( q_dist, target_q_dist, data['action'], target_q_action, data['reward'], data['done'], data['weight'] ) loss, td_error_per_sample = dist_nstep_td_error( data, self._gamma, self._v_min, self._v_max, self._n_atom, nstep=self._nstep, value_gamma=value_gamma ) # ==================== # Rainbow update # ==================== self._optimizer.zero_grad() loss.backward() self._optimizer.step() # ============= # after update # ============= self._target_model.update(self._learn_model.state_dict()) return { 'cur_lr': self._optimizer.defaults['lr'], 'total_loss': loss.item(), 'priority': td_error_per_sample.abs().tolist(), } def _init_collect(self) -> None: r""" Overview: Collect mode init moethod. Called by ``self.__init__``. Init traj and unroll length, collect model. .. note:: the rainbow dqn enable the eps_greedy_sample, but might not need to use it, \ as the noise_net contain noise that can help exploration """ self._unroll_len = self._cfg.collect.unroll_len self._nstep = self._cfg.nstep self._gamma = self._cfg.discount_factor self._collect_model = model_wrap(self._model, wrapper_name='eps_greedy_sample') self._collect_model.reset() def _forward_collect(self, data: dict, eps: float) -> dict: r""" Overview: Reset the noise from noise net and collect output according to eps_greedy plugin Arguments: - data (:obj:`Dict[str, Any]`): Dict type data, stacked env data for predicting policy_output(action), \ values are torch.Tensor or np.ndarray or dict/list combinations, keys are env_id indicated by integer. - eps (:obj:`float`): epsilon value for exploration, which is decayed by collected env step. Returns: - output (:obj:`Dict[int, Any]`): Dict type data, including at least inferred action according to input obs. ReturnsKeys - necessary: ``action`` """ data_id = list(data.keys()) data = default_collate(list(data.values())) if self._cuda: data = to_device(data, self._device) self._collect_model.eval() self._reset_noise(self._collect_model) 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, traj: 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: - traj (:obj:`list`): The trajactory's buffer list Returns: - samples (:obj:`dict`): The training samples generated """ data = get_nstep_return_data(traj, self._nstep, gamma=self._gamma) return get_train_sample(data, self._unroll_len) def _reset_noise(self, model: torch.nn.Module): r""" Overview: Reset the noise of model Arguments: - model (:obj:`torch.nn.Module`): the model to reset, must contain reset_noise method """ for m in model.modules(): if hasattr(m, 'reset_noise'): m.reset_noise()