`ding.policy.dt`¶

`ding.policy.dt` ¶

`DTPolicy` ¶

Bases: Policy
Overview
Policy class of Decision Transformer algorithm in discrete environments. Paper link: https://arxiv.org/abs/2106.01345.
`default_model()` ¶

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.
Full Source Code

../ding/policy/dt.py
from typing import List, Dict, Any, Tuple, Optionalfrom collections import namedtupleimport torch.nn.functional as Fimport torchimport numpy as npfrom ding.torch_utils import to_devicefrom ding.utils import POLICY_REGISTRYfrom ding.utils.data import default_decollatefrom .base_policy import Policy@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']    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    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(),        }    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        )    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)}    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)    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
ding.policy.dt¶

ding.policy.dt ¶

DTPolicy ¶

default_model() ¶

Full Source Code

`ding.policy.dt`¶

`ding.policy.dt` ¶

`DTPolicy` ¶

`default_model()` ¶
