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Source code for ding.policy.dt

from typing import List, Dict, Any, Tuple, Optional
from collections import namedtuple
import torch.nn.functional as F
import torch
import numpy as np
from ding.torch_utils import to_device
from ding.utils import POLICY_REGISTRY
from ding.utils.data import default_decollate
from .base_policy import Policy


[docs]@POLICY_REGISTRY.register('dt') class DTPolicy(Policy): """ Overview: Policy class of Decision Transformer algorithm in discrete environments. Paper link: https://arxiv.org/abs/2106.01345. """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='dt', # (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=False, # (int) N-step reward for target q_value estimation obs_shape=4, action_shape=2, rtg_scale=1000, # normalize returns to go max_eval_ep_len=1000, # max len of one episode batch_size=64, # training batch size wt_decay=1e-4, # decay weight in optimizer warmup_steps=10000, # steps for learning rate warmup context_len=20, # length of transformer input learning_rate=1e-4, ) 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 'dt', ['ding.model.template.dt']
[docs] def _init_learn(self) -> None: """ Overview: Initialize the learn mode of policy, including related attributes and modules. For Decision Transformer, \ it mainly contains the optimizer, algorithm-specific arguments such as rtg_scale and lr scheduler. 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``. """ # rtg_scale: scale of `return to go` # rtg_target: max target of `return to go` # Our goal is normalize `return to go` to (0, 1), which will favour the covergence. # As a result, we usually set rtg_scale == rtg_target. self.rtg_scale = self._cfg.rtg_scale # normalize returns to go self.rtg_target = self._cfg.rtg_target # max target reward_to_go self.max_eval_ep_len = self._cfg.max_eval_ep_len # max len of one episode lr = self._cfg.learning_rate # learning rate wt_decay = self._cfg.wt_decay # weight decay warmup_steps = self._cfg.warmup_steps # warmup steps for lr scheduler self.clip_grad_norm_p = self._cfg.clip_grad_norm_p self.context_len = self._cfg.model.context_len # K in decision transformer self.state_dim = self._cfg.model.state_dim self.act_dim = self._cfg.model.act_dim self._learn_model = self._model self._atari_env = 'state_mean' not in self._cfg self._basic_discrete_env = not self._cfg.model.continuous and 'state_mean' in self._cfg if self._atari_env: self._optimizer = self._learn_model.configure_optimizers(wt_decay, lr) else: self._optimizer = torch.optim.AdamW(self._learn_model.parameters(), lr=lr, weight_decay=wt_decay) self._scheduler = torch.optim.lr_scheduler.LambdaLR( self._optimizer, lambda steps: min((steps + 1) / warmup_steps, 1) ) self.max_env_score = -1.0
[docs] def _forward_learn(self, data: List[torch.Tensor]) -> 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 offline dataset and then returns the output \ result, including various training information such as loss, current learning rate. Arguments: - data (:obj:`List[torch.Tensor]`): The input data used for policy forward, including a series of \ processed torch.Tensor data, i.e., timesteps, states, actions, returns_to_go, traj_mask. 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. """ self._learn_model.train() timesteps, states, actions, returns_to_go, traj_mask = data # The shape of `returns_to_go` may differ with different dataset (B x T or B x T x 1), # and we need a 3-dim tensor if len(returns_to_go.shape) == 2: returns_to_go = returns_to_go.unsqueeze(-1) if self._basic_discrete_env: actions = actions.to(torch.long) actions = actions.squeeze(-1) action_target = torch.clone(actions).detach().to(self._device) if self._atari_env: state_preds, action_preds, return_preds = self._learn_model.forward( timesteps=timesteps, states=states, actions=actions, returns_to_go=returns_to_go, tar=1 ) else: state_preds, action_preds, return_preds = self._learn_model.forward( timesteps=timesteps, states=states, actions=actions, returns_to_go=returns_to_go ) if self._atari_env: action_loss = F.cross_entropy(action_preds.reshape(-1, action_preds.size(-1)), action_target.reshape(-1)) else: traj_mask = traj_mask.view(-1, ) # only consider non padded elements action_preds = action_preds.view(-1, self.act_dim)[traj_mask > 0] if self._cfg.model.continuous: action_target = action_target.view(-1, self.act_dim)[traj_mask > 0] action_loss = F.mse_loss(action_preds, action_target) else: action_target = action_target.view(-1)[traj_mask > 0] action_loss = F.cross_entropy(action_preds, action_target) self._optimizer.zero_grad() action_loss.backward() if self._cfg.multi_gpu: self.sync_gradients(self._learn_model) torch.nn.utils.clip_grad_norm_(self._learn_model.parameters(), self.clip_grad_norm_p) self._optimizer.step() self._scheduler.step() return { 'cur_lr': self._optimizer.state_dict()['param_groups'][0]['lr'], 'action_loss': action_loss.detach().cpu().item(), 'total_loss': action_loss.detach().cpu().item(), }
[docs] def _init_eval(self) -> None: """ Overview: Initialize the eval mode of policy, including related attributes and modules. For DQN, it contains the \ eval model, some algorithm-specific parameters such as context_len, max_eval_ep_len, etc. This method will be called in ``__init__`` method if ``eval`` field is in ``enable_field``. .. tip:: For the evaluation of complete episodes, we need to maintain some historical information for transformer \ inference. These variables need to be initialized in ``_init_eval`` and reset in ``_reset_eval`` when \ necessary. .. 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 = self._model # init data self._device = torch.device(self._device) self.rtg_scale = self._cfg.rtg_scale # normalize returns to go self.rtg_target = self._cfg.rtg_target # max target reward_to_go self.state_dim = self._cfg.model.state_dim self.act_dim = self._cfg.model.act_dim self.eval_batch_size = self._cfg.evaluator_env_num self.max_eval_ep_len = self._cfg.max_eval_ep_len self.context_len = self._cfg.model.context_len # K in decision transformer self.t = [0 for _ in range(self.eval_batch_size)] if self._cfg.model.continuous: self.actions = torch.zeros( (self.eval_batch_size, self.max_eval_ep_len, self.act_dim), dtype=torch.float32, device=self._device ) else: self.actions = torch.zeros( (self.eval_batch_size, self.max_eval_ep_len, 1), dtype=torch.long, device=self._device ) self._atari_env = 'state_mean' not in self._cfg self._basic_discrete_env = not self._cfg.model.continuous and 'state_mean' in self._cfg if self._atari_env: self.states = torch.zeros( ( self.eval_batch_size, self.max_eval_ep_len, ) + tuple(self.state_dim), dtype=torch.float32, device=self._device ) self.running_rtg = [self.rtg_target for _ in range(self.eval_batch_size)] else: self.running_rtg = [self.rtg_target / self.rtg_scale for _ in range(self.eval_batch_size)] self.states = torch.zeros( (self.eval_batch_size, self.max_eval_ep_len, self.state_dim), dtype=torch.float32, device=self._device ) self.state_mean = torch.from_numpy(np.array(self._cfg.state_mean)).to(self._device) self.state_std = torch.from_numpy(np.array(self._cfg.state_std)).to(self._device) self.timesteps = torch.arange( start=0, end=self.max_eval_ep_len, step=1 ).repeat(self.eval_batch_size, 1).to(self._device) self.rewards_to_go = torch.zeros( (self.eval_batch_size, self.max_eval_ep_len, 1), dtype=torch.float32, device=self._device )
[docs] def _forward_eval(self, data: Dict[int, Any]) -> Dict[int, Any]: """ Overview: Policy forward function of eval mode (evaluation policy performance, such as interacting with envs. \ Forward means that the policy gets some input data (current obs/return-to-go and historical information) \ from the envs and then returns the output data, such as the action to interact with the envs. \ Arguments: - data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs and \ reward to calculate running return-to-go. 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:: Decision Transformer will do different operations for different types of envs in evaluation. """ # save and forward data_id = list(data.keys()) self._eval_model.eval() with torch.no_grad(): if self._atari_env: states = torch.zeros( ( self.eval_batch_size, self.context_len, ) + tuple(self.state_dim), dtype=torch.float32, device=self._device ) timesteps = torch.zeros((self.eval_batch_size, 1, 1), dtype=torch.long, device=self._device) else: states = torch.zeros( (self.eval_batch_size, self.context_len, self.state_dim), dtype=torch.float32, device=self._device ) timesteps = torch.zeros((self.eval_batch_size, self.context_len), dtype=torch.long, device=self._device) if not self._cfg.model.continuous: actions = torch.zeros( (self.eval_batch_size, self.context_len, 1), dtype=torch.long, device=self._device ) else: actions = torch.zeros( (self.eval_batch_size, self.context_len, self.act_dim), dtype=torch.float32, device=self._device ) rewards_to_go = torch.zeros( (self.eval_batch_size, self.context_len, 1), dtype=torch.float32, device=self._device ) for i in data_id: if self._atari_env: self.states[i, self.t[i]] = data[i]['obs'].to(self._device) else: self.states[i, self.t[i]] = (data[i]['obs'].to(self._device) - self.state_mean) / self.state_std self.running_rtg[i] = self.running_rtg[i] - (data[i]['reward'] / self.rtg_scale).to(self._device) self.rewards_to_go[i, self.t[i]] = self.running_rtg[i] if self.t[i] <= self.context_len: if self._atari_env: timesteps[i] = min(self.t[i], self._cfg.model.max_timestep) * torch.ones( (1, 1), dtype=torch.int64 ).to(self._device) else: timesteps[i] = self.timesteps[i, :self.context_len] states[i] = self.states[i, :self.context_len] actions[i] = self.actions[i, :self.context_len] rewards_to_go[i] = self.rewards_to_go[i, :self.context_len] else: if self._atari_env: timesteps[i] = min(self.t[i], self._cfg.model.max_timestep) * torch.ones( (1, 1), dtype=torch.int64 ).to(self._device) else: timesteps[i] = self.timesteps[i, self.t[i] - self.context_len + 1:self.t[i] + 1] states[i] = self.states[i, self.t[i] - self.context_len + 1:self.t[i] + 1] actions[i] = self.actions[i, self.t[i] - self.context_len + 1:self.t[i] + 1] rewards_to_go[i] = self.rewards_to_go[i, self.t[i] - self.context_len + 1:self.t[i] + 1] if self._basic_discrete_env: actions = actions.squeeze(-1) _, act_preds, _ = self._eval_model.forward(timesteps, states, actions, rewards_to_go) del timesteps, states, actions, rewards_to_go logits = act_preds[:, -1, :] if not self._cfg.model.continuous: if self._atari_env: probs = F.softmax(logits, dim=-1) act = torch.zeros((self.eval_batch_size, 1), dtype=torch.long, device=self._device) for i in data_id: act[i] = torch.multinomial(probs[i], num_samples=1) else: act = torch.argmax(logits, axis=1).unsqueeze(1) else: act = logits for i in data_id: self.actions[i, self.t[i]] = act[i] # TODO: self.actions[i] should be a queue when exceed max_t self.t[i] += 1 if self._cuda: act = to_device(act, 'cpu') output = {'action': act} 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 historical info of transformer \ for decision transformer. 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 history. Arguments: - data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \ specified by ``data_id``. """ # clean data if data_id is None: self.t = [0 for _ in range(self.eval_batch_size)] self.timesteps = torch.arange( start=0, end=self.max_eval_ep_len, step=1 ).repeat(self.eval_batch_size, 1).to(self._device) if not self._cfg.model.continuous: self.actions = torch.zeros( (self.eval_batch_size, self.max_eval_ep_len, 1), dtype=torch.long, device=self._device ) else: self.actions = torch.zeros( (self.eval_batch_size, self.max_eval_ep_len, self.act_dim), dtype=torch.float32, device=self._device ) if self._atari_env: self.states = torch.zeros( ( self.eval_batch_size, self.max_eval_ep_len, ) + tuple(self.state_dim), dtype=torch.float32, device=self._device ) self.running_rtg = [self.rtg_target for _ in range(self.eval_batch_size)] else: self.states = torch.zeros( (self.eval_batch_size, self.max_eval_ep_len, self.state_dim), dtype=torch.float32, device=self._device ) self.running_rtg = [self.rtg_target / self.rtg_scale for _ in range(self.eval_batch_size)] self.rewards_to_go = torch.zeros( (self.eval_batch_size, self.max_eval_ep_len, 1), dtype=torch.float32, device=self._device ) else: for i in data_id: self.t[i] = 0 if not self._cfg.model.continuous: self.actions[i] = torch.zeros((self.max_eval_ep_len, 1), dtype=torch.long, device=self._device) else: self.actions[i] = torch.zeros( (self.max_eval_ep_len, self.act_dim), dtype=torch.float32, device=self._device ) if self._atari_env: self.states[i] = torch.zeros( (self.max_eval_ep_len, ) + tuple(self.state_dim), dtype=torch.float32, device=self._device ) self.running_rtg[i] = self.rtg_target else: self.states[i] = torch.zeros( (self.max_eval_ep_len, self.state_dim), dtype=torch.float32, device=self._device ) self.running_rtg[i] = self.rtg_target / self.rtg_scale self.timesteps[i] = torch.arange(start=0, end=self.max_eval_ep_len, step=1).to(self._device) self.rewards_to_go[i] = torch.zeros((self.max_eval_ep_len, 1), dtype=torch.float32, device=self._device)
[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 ['cur_lr', 'action_loss']
def _init_collect(self) -> None: pass def _forward_collect(self, data: Dict[int, Any], eps: float) -> Dict[int, Any]: pass def _get_train_sample(self, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]: pass def _process_transition(self, obs: Any, policy_output: Dict[str, Any], timestep: namedtuple) -> Dict[str, Any]: pass