""" Overview: BTW, users can refer to the unittest of these model templates to learn how to use them. """ from typing import Optional, Tuple import math import torch import torch.nn as nn from ding.torch_utils import MLP, ResBlock from ding.utils import MODEL_REGISTRY, SequenceType from numpy import ndarray from .common import EZNetworkOutput, RepresentationNetwork, PredictionNetwork from .utils import renormalize, get_params_mean, get_dynamic_mean, get_reward_mean # use ModelRegistry to register the model, for more details about ModelRegistry, please refer to DI-engine's document. @MODEL_REGISTRY.register('EfficientZeroModel') class EfficientZeroModel(nn.Module): def __init__( self, observation_shape: SequenceType = (12, 96, 96), action_space_size: int = 6, lstm_hidden_size: int = 512, num_res_blocks: int = 1, num_channels: int = 64, reward_head_channels: int = 16, value_head_channels: int = 16, policy_head_channels: int = 16, fc_reward_layers: SequenceType = [32], fc_value_layers: SequenceType = [32], fc_policy_layers: SequenceType = [32], reward_support_size: int = 601, value_support_size: int = 601, proj_hid: int = 1024, proj_out: int = 1024, pred_hid: int = 512, pred_out: int = 1024, self_supervised_learning_loss: bool = True, categorical_distribution: bool = True, last_linear_layer_init_zero: bool = True, state_norm: bool = False, downsample: bool = False, activation: Optional[nn.Module] = nn.ReLU(inplace=True), norm_type: Optional[str] = 'BN', discrete_action_encoding_type: str = 'one_hot', *args, **kwargs ) -> None: """ Overview: The definition of the network model of EfficientZero, which is a generalization version for 2D image obs. The networks are built on convolution residual blocks and fully connected layers. EfficientZero model which consists of a representation network, a dynamics network and a prediction network. Arguments: - observation_shape (:obj:`SequenceType`): Observation space shape, e.g. [C, W, H]=[12, 96, 96] for Atari. - action_space_size: (:obj:`int`): Action space size, usually an integer number for discrete action space. - lstm_hidden_size (:obj:`int`): The hidden size of LSTM in dynamics network to predict value_prefix. - num_res_blocks (:obj:`int`): The number of res blocks in EfficientZero model. - num_channels (:obj:`int`): The channels of hidden states. - reward_head_channels (:obj:`int`): The channels of reward head. - value_head_channels (:obj:`int`): The channels of value head. - policy_head_channels (:obj:`int`): The channels of policy head. - fc_reward_layers (:obj:`SequenceType`): The number of hidden layers of the reward head (MLP head). - fc_value_layers (:obj:`SequenceType`): The number of hidden layers used in value head (MLP head). - fc_policy_layers (:obj:`SequenceType`): The number of hidden layers used in policy head (MLP head). - reward_support_size (:obj:`int`): The size of categorical reward output - value_support_size (:obj:`int`): The size of categorical value output. - proj_hid (:obj:`int`): The size of projection hidden layer. - proj_out (:obj:`int`): The size of projection output layer. - pred_hid (:obj:`int`): The size of prediction hidden layer. - pred_out (:obj:`int`): The size of prediction output layer. - categorical_distribution (:obj:`bool`): Whether to use discrete support to represent categorical \ distribution for value and reward/value_prefix. - last_linear_layer_init_zero (:obj:`bool`): Whether to use zero initializationss for the last layer of \ dynamics/prediction mlp, default sets it to True. - state_norm (:obj:`bool`): Whether to use normalization for hidden states, default set it to False. - downsample (:obj:`bool`): Whether to do downsampling for observations in ``representation_network``, \ defaults to True. This option is often used in video games like Atari. In board games like go, \ we don't need this module. - activation (:obj:`Optional[nn.Module]`): Activation function used in network, which often use in-place \ operation to speedup, e.g. ReLU(inplace=True). - norm_type (:obj:`str`): The type of normalization in networks. defaults to 'BN'. - discrete_action_encoding_type (:obj:`str`): The type of encoding for discrete action. Default sets it to 'one_hot'. options = {'one_hot', 'not_one_hot'} """ super(EfficientZeroModel, self).__init__() if isinstance(observation_shape, int) or len(observation_shape) == 1: # for vector obs input, e.g. classical control and box2d environments # to be compatible with LightZero model/policy, transform to shape: [C, W, H] observation_shape = [1, observation_shape, 1] if not categorical_distribution: self.reward_support_size = 1 self.value_support_size = 1 else: self.reward_support_size = reward_support_size self.value_support_size = value_support_size self.action_space_size = action_space_size assert discrete_action_encoding_type in ['one_hot', 'not_one_hot'], discrete_action_encoding_type self.discrete_action_encoding_type = discrete_action_encoding_type if self.discrete_action_encoding_type == 'one_hot': self.action_encoding_dim = action_space_size elif self.discrete_action_encoding_type == 'not_one_hot': self.action_encoding_dim = 1 self.lstm_hidden_size = lstm_hidden_size self.proj_hid = proj_hid self.proj_out = proj_out self.pred_hid = pred_hid self.pred_out = pred_out self.last_linear_layer_init_zero = last_linear_layer_init_zero self.state_norm = state_norm self.downsample = downsample self.self_supervised_learning_loss = self_supervised_learning_loss self.norm_type = norm_type self.activation = activation flatten_output_size_for_reward_head = ( (reward_head_channels * math.ceil(observation_shape[1] / 16) * math.ceil(observation_shape[2] / 16)) if downsample else (reward_head_channels * observation_shape[1] * observation_shape[2]) ) flatten_output_size_for_value_head = ( (value_head_channels * math.ceil(observation_shape[1] / 16) * math.ceil(observation_shape[2] / 16)) if downsample else (value_head_channels * observation_shape[1] * observation_shape[2]) ) flatten_output_size_for_policy_head = ( (policy_head_channels * math.ceil(observation_shape[1] / 16) * math.ceil(observation_shape[2] / 16)) if downsample else (policy_head_channels * observation_shape[1] * observation_shape[2]) ) self.representation_network = RepresentationNetwork( observation_shape, num_res_blocks, num_channels, downsample, activation=self.activation, norm_type=self.norm_type, ) self.dynamics_network = DynamicsNetwork( observation_shape, self.action_encoding_dim, num_res_blocks, num_channels + self.action_encoding_dim, reward_head_channels, fc_reward_layers, self.reward_support_size, flatten_output_size_for_reward_head, downsample, lstm_hidden_size=lstm_hidden_size, last_linear_layer_init_zero=self.last_linear_layer_init_zero, activation=self.activation, norm_type=self.norm_type, ) self.prediction_network = PredictionNetwork( observation_shape, action_space_size, num_res_blocks, num_channels, value_head_channels, policy_head_channels, fc_value_layers, fc_policy_layers, self.value_support_size, flatten_output_size_for_value_head, flatten_output_size_for_policy_head, downsample, last_linear_layer_init_zero=self.last_linear_layer_init_zero, activation=self.activation, norm_type=self.norm_type, ) # projection used in EfficientZero if self.downsample: # In Atari, if the observation_shape is set to (12, 96, 96), which indicates the original shape of # (3,96,96), and frame_stack_num is 4. Due to downsample, the encoding of observation (latent_state) is # (64, 96/16, 96/16), where 64 is the number of channels, 96/16 is the size of the latent state. Thus, # self.projection_input_dim = 64 * 96/16 * 96/16 = 64*6*6 = 2304 ceil_size = math.ceil(observation_shape[1] / 16) * math.ceil(observation_shape[2] / 16) self.projection_input_dim = num_channels * ceil_size else: self.projection_input_dim = num_channels * observation_shape[1] * observation_shape[2] if self.self_supervised_learning_loss: self.projection = nn.Sequential( nn.Linear(self.projection_input_dim, self.proj_hid), nn.BatchNorm1d(self.proj_hid), activation, nn.Linear(self.proj_hid, self.proj_hid), nn.BatchNorm1d(self.proj_hid), activation, nn.Linear(self.proj_hid, self.proj_out), nn.BatchNorm1d(self.proj_out) ) self.prediction_head = nn.Sequential( nn.Linear(self.proj_out, self.pred_hid), nn.BatchNorm1d(self.pred_hid), activation, nn.Linear(self.pred_hid, self.pred_out), ) def initial_inference(self, obs: torch.Tensor) -> EZNetworkOutput: """ Overview: Initial inference of EfficientZero model, which is the first step of the EfficientZero model. To perform the initial inference, we first use the representation network to obtain the ``latent_state``. Then we use the prediction network to predict ``value`` and ``policy_logits`` of the ``latent_state``, and also prepare the zeros-like ``reward_hidden_state`` for the next step of the EfficientZero model. Arguments: - obs (:obj:`torch.Tensor`): The 2D image observation data. Returns (EZNetworkOutput): - value (:obj:`torch.Tensor`): The output value of input state to help policy improvement and evaluation. - value_prefix (:obj:`torch.Tensor`): The predicted prefix sum of value for input state. \ In initial inference, we set it to zero vector. - policy_logits (:obj:`torch.Tensor`): The output logit to select discrete action. - latent_state (:obj:`torch.Tensor`): The encoding latent state of input state. - reward_hidden_state (:obj:`Tuple[torch.Tensor]`): The hidden state of LSTM about reward. In initial inference, \ we set it to the zeros-like hidden state (H and C). Shapes: - obs (:obj:`torch.Tensor`): :math:`(B, num_channel, obs_shape[1], obs_shape[2])`, where B is batch_size. - value (:obj:`torch.Tensor`): :math:`(B, value_support_size)`, where B is batch_size. - value_prefix (:obj:`torch.Tensor`): :math:`(B, reward_support_size)`, where B is batch_size. - policy_logits (:obj:`torch.Tensor`): :math:`(B, action_dim)`, where B is batch_size. - latent_state (:obj:`torch.Tensor`): :math:`(B, H_, W_)`, where B is batch_size, H_ is the height of \ latent state, W_ is the width of latent state. - reward_hidden_state (:obj:`Tuple[torch.Tensor]`): The shape of each element is :math:`(1, B, lstm_hidden_size)`, where B is batch_size. """ batch_size = obs.size(0) latent_state = self._representation(obs) policy_logits, value = self._prediction(latent_state) # zero initialization for reward hidden states # (hn, cn), each element shape is (layer_num=1, batch_size, lstm_hidden_size) reward_hidden_state = ( torch.zeros(1, batch_size, self.lstm_hidden_size).to(obs.device), torch.zeros(1, batch_size, self.lstm_hidden_size).to(obs.device) ) return EZNetworkOutput(value, [0. for _ in range(batch_size)], policy_logits, latent_state, reward_hidden_state) def recurrent_inference( self, latent_state: torch.Tensor, reward_hidden_state: Tuple[torch.Tensor], action: torch.Tensor ) -> EZNetworkOutput: """ Overview: Recurrent inference of EfficientZero model, which is the rollout step of the EfficientZero model. To perform the recurrent inference, we first use the dynamics network to predict ``next_latent_state``, ``reward_hidden_state``, ``value_prefix`` by the given current ``latent_state`` and ``action``. We then use the prediction network to predict the ``value`` and ``policy_logits``. Arguments: - latent_state (:obj:`torch.Tensor`): The encoding latent state of input state. - reward_hidden_state (:obj:`Tuple[torch.Tensor]`): The input hidden state of LSTM about reward. - action (:obj:`torch.Tensor`): The predicted action to rollout. Returns (EZNetworkOutput): - value (:obj:`torch.Tensor`): The output value of input state to help policy improvement and evaluation. - value_prefix (:obj:`torch.Tensor`): The predicted prefix sum of value for input state. - policy_logits (:obj:`torch.Tensor`): The output logit to select discrete action. - latent_state (:obj:`torch.Tensor`): The encoding latent state of input state. - next_latent_state (:obj:`torch.Tensor`): The predicted next latent state. - reward_hidden_state (:obj:`Tuple[torch.Tensor]`): The output hidden state of LSTM about reward. Shapes: - action (:obj:`torch.Tensor`): :math:`(B, )`, where B is batch_size. - value (:obj:`torch.Tensor`): :math:`(B, value_support_size)`, where B is batch_size. - value_prefix (:obj:`torch.Tensor`): :math:`(B, reward_support_size)`, where B is batch_size. - policy_logits (:obj:`torch.Tensor`): :math:`(B, action_dim)`, where B is batch_size. - latent_state (:obj:`torch.Tensor`): :math:`(B, H_, W_)`, where B is batch_size, H_ is the height of \ latent state, W_ is the width of latent state. - next_latent_state (:obj:`torch.Tensor`): :math:`(B, H_, W_)`, where B is batch_size, H_ is the height of \ latent state, W_ is the width of latent state. - reward_hidden_state (:obj:`Tuple[torch.Tensor]`): :math:`(1, B, lstm_hidden_size)`, where B is batch_size. """ next_latent_state, reward_hidden_state, value_prefix = self._dynamics(latent_state, reward_hidden_state, action) policy_logits, value = self._prediction(next_latent_state) return EZNetworkOutput(value, value_prefix, policy_logits, next_latent_state, reward_hidden_state) def _representation(self, observation: torch.Tensor) -> torch.Tensor: """ Overview: Use the representation network to encode the observations into latent state. Arguments: - obs (:obj:`torch.Tensor`): The 2D image observation data. Returns: - latent_state (:obj:`torch.Tensor`): The encoding latent state of input state. Shapes: - obs (:obj:`torch.Tensor`): :math:`(B, num_channel, obs_shape[1], obs_shape[2])`, where B is batch_size. - latent_state (:obj:`torch.Tensor`): :math:`(B, H_, W_)`, where B is batch_size, H_ is the height of \ latent state, W_ is the width of latent state. """ latent_state = self.representation_network(observation) if self.state_norm: latent_state = renormalize(latent_state) return latent_state def _prediction(self, latent_state: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """ Overview: use the prediction network to predict the "value" and "policy_logits" of the "latent_state". Arguments: - latent_state (:obj:`torch.Tensor`): The encoding latent state of input state. Returns: - policy_logits (:obj:`torch.Tensor`): The output logit to select discrete action. - value (:obj:`torch.Tensor`): The output value of input state to help policy improvement and evaluation. Shapes: - latent_state (:obj:`torch.Tensor`): :math:`(B, H_, W_)`, where B is batch_size, H_ is the height of \ latent state, W_ is the width of latent state. - policy_logits (:obj:`torch.Tensor`): :math:`(B, action_dim)`, where B is batch_size. - value (:obj:`torch.Tensor`): :math:`(B, value_support_size)`, where B is batch_size. """ return self.prediction_network(latent_state) def _dynamics(self, latent_state: torch.Tensor, reward_hidden_state: Tuple[torch.Tensor], action: torch.Tensor) -> Tuple[torch.Tensor, Tuple[torch.Tensor], torch.Tensor]: """ Overview: Concatenate ``latent_state`` and ``action`` and use the dynamics network to predict ``next_latent_state`` ``value_prefix`` and ``next_reward_hidden_state``. Arguments: - latent_state (:obj:`torch.Tensor`): The encoding latent state of input state. - reward_hidden_state (:obj:`Tuple[torch.Tensor]`): The input hidden state of LSTM about reward. - action (:obj:`torch.Tensor`): The predicted action to rollout. Returns: - next_latent_state (:obj:`torch.Tensor`): The predicted latent state of the next timestep. - next_reward_hidden_state (:obj:`Tuple[torch.Tensor]`): The output hidden state of LSTM about reward. - value_prefix (:obj:`torch.Tensor`): The predicted prefix sum of value for input state. Shapes: - latent_state (:obj:`torch.Tensor`): :math:`(B, H_, W_)`, where B is batch_size, H_ is the height of \ latent state, W_ is the width of latent state. - action (:obj:`torch.Tensor`): :math:`(B, )`, where B is batch_size. - next_latent_state (:obj:`torch.Tensor`): :math:`(B, H_, W_)`, where B is batch_size, H_ is the height of \ latent state, W_ is the width of latent state. - value_prefix (:obj:`torch.Tensor`): :math:`(B, reward_support_size)`, where B is batch_size. """ # NOTE: the discrete action encoding type is important for some environments # discrete action space if self.discrete_action_encoding_type == 'one_hot': # Stack latent_state with the one hot encoded action. # The final action_encoding shape is (batch_size, action_space_size, latent_state[2], latent_state[3]), e.g. (8, 2, 4, 1). if len(action.shape) == 1: # (batch_size, ) -> (batch_size, 1) # e.g., torch.Size([8]) -> torch.Size([8, 1]) action = action.unsqueeze(-1) # transform action to one-hot encoding. # action_one_hot shape: (batch_size, action_space_size), e.g., (8, 4) action_one_hot = torch.zeros(action.shape[0], self.action_space_size, device=action.device) # transform action to torch.int64 action = action.long() action_one_hot.scatter_(1, action, 1) action_encoding_tmp = action_one_hot.unsqueeze(-1).unsqueeze(-1) action_encoding = action_encoding_tmp.expand( latent_state.shape[0], self.action_space_size, latent_state.shape[2], latent_state.shape[3] ) elif self.discrete_action_encoding_type == 'not_one_hot': # Stack latent_state with the normalized encoded action. # The final action_encoding shape is (batch_size, 1, latent_state[2], latent_state[3]), e.g. (8, 1, 4, 1). if len(action.shape) == 2: # (batch_size, action_dim=1) -> (batch_size, 1, 1, 1) # e.g., torch.Size([8, 1]) -> torch.Size([8, 1, 1, 1]) action = action.unsqueeze(-1).unsqueeze(-1) elif len(action.shape) == 1: # (batch_size,) -> (batch_size, 1, 1, 1) # e.g., -> torch.Size([8, 1, 1, 1]) action = action.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1) action_encoding = action.expand( latent_state.shape[0], 1, latent_state.shape[2], latent_state.shape[3] ) / self.action_space_size # state_action_encoding shape: (batch_size, latent_state[1] + action_dim, latent_state[2], latent_state[3]) or # (batch_size, latent_state[1] + action_space_size, latent_state[2], latent_state[3]) depending on the discrete_action_encoding_type. state_action_encoding = torch.cat((latent_state, action_encoding), dim=1) # NOTE: the key difference between EfficientZero and MuZero next_latent_state, next_reward_hidden_state, value_prefix = self.dynamics_network( state_action_encoding, reward_hidden_state ) if self.state_norm: next_latent_state = renormalize(next_latent_state) return next_latent_state, next_reward_hidden_state, value_prefix def project(self, latent_state: torch.Tensor, with_grad: bool = True) -> torch.Tensor: """ Overview: Project the latent state to a lower dimension to calculate the self-supervised loss, which is proposed in EfficientZero. For more details, please refer to the paper ``Exploring Simple Siamese Representation Learning``. Arguments: - latent_state (:obj:`torch.Tensor`): The encoding latent state of input state. - with_grad (:obj:`bool`): Whether to calculate gradient for the projection result. Returns: - proj (:obj:`torch.Tensor`): The result embedding vector of projection operation. Shapes: - latent_state (:obj:`torch.Tensor`): :math:`(B, H_, W_)`, where B is batch_size, H_ is the height of \ latent state, W_ is the width of latent state. - proj (:obj:`torch.Tensor`): :math:`(B, projection_output_dim)`, where B is batch_size. Examples: >>> latent_state = torch.randn(256, 64, 6, 6) >>> output = self.project(latent_state) >>> output.shape # (256, 1024) .. note:: for Atari: observation_shape = (12, 96, 96), # original shape is (3,96,96), frame_stack_num=4 if downsample is True, latent_state.shape: (batch_size, num_channel, obs_shape[1] / 16, obs_shape[2] / 16) i.e., (256, 64, 96 / 16, 96 / 16) = (256, 64, 6, 6) latent_state reshape: (256, 64, 6, 6) -> (256,64*6*6) = (256, 2304) # self.projection_input_dim = 64*6*6 = 2304 # self.projection_output_dim = 1024 """ latent_state = latent_state.reshape(latent_state.shape[0], -1) proj = self.projection(latent_state) if with_grad: # with grad, use prediction_head return self.prediction_head(proj) else: return proj.detach() def get_params_mean(self) -> float: return get_params_mean(self) class DynamicsNetwork(nn.Module): def __init__( self, observation_shape: SequenceType, action_encoding_dim: int = 2, num_res_blocks: int = 1, num_channels: int = 64, reward_head_channels: int = 64, fc_reward_layers: SequenceType = [32], output_support_size: int = 601, flatten_output_size_for_reward_head: int = 64, downsample: bool = False, lstm_hidden_size: int = 512, last_linear_layer_init_zero: bool = True, activation: Optional[nn.Module] = nn.ReLU(inplace=True), norm_type: Optional[str] = 'BN', ): """ Overview: The definition of dynamics network in EfficientZero algorithm, which is used to predict the next latent state value_prefix and reward_hidden_state by the given current latent state and action. Arguments: - observation_shape (:obj:`SequenceType`): The shape of input observation, e.g., (12, 96, 96). - action_encoding_dim (:obj:`int`): The dimension of action encoding. - num_res_blocks (:obj:`int`): The number of res blocks in EfficientZero model. - num_channels (:obj:`int`): The channels of latent states. - reward_head_channels (:obj:`int`): The channels of reward head. - fc_reward_layers (:obj:`SequenceType`): The number of hidden layers of the reward head (MLP head). - output_support_size (:obj:`int`): The size of categorical reward output. - flatten_output_size_for_reward_head (:obj:`int`): The flatten size of output for reward head, i.e., \ the input size of reward head. - downsample (:obj:`bool`): Whether to downsample the input observation, default set it to False. - lstm_hidden_size (:obj:`int`): The hidden size of lstm in dynamics network. - last_linear_layer_init_zero (:obj:`bool`): Whether to use zero initializationss for the last layer of \ reward mlp, Default sets it to True. - activation (:obj:`Optional[nn.Module]`): Activation function used in network, which often use in-place \ operation to speedup, e.g. ReLU(inplace=True). - norm_type (:obj:`str`): The type of normalization in networks. Default sets it to 'BN'. """ super().__init__() assert norm_type in ['BN', 'LN'], "norm_type must in ['BN', 'LN']" assert num_channels > action_encoding_dim, f'num_channels:{num_channels} <= action_encoding_dim:{action_encoding_dim}' self.action_encoding_dim = action_encoding_dim self.num_channels = num_channels self.flatten_output_size_for_reward_head = flatten_output_size_for_reward_head self.lstm_hidden_size = lstm_hidden_size self.activation = activation self.conv = nn.Conv2d(num_channels, num_channels - self.action_encoding_dim, kernel_size=3, stride=1, padding=1, bias=False) if norm_type == 'BN': self.norm_common = nn.BatchNorm2d(num_channels - self.action_encoding_dim) elif norm_type == 'LN': if downsample: self.norm_common = nn.LayerNorm( [num_channels - self.action_encoding_dim, math.ceil(observation_shape[-2] / 16), math.ceil(observation_shape[-1] / 16)]) else: self.norm_common = nn.LayerNorm([num_channels - self.action_encoding_dim, observation_shape[-2], observation_shape[-1]]) self.resblocks = nn.ModuleList( [ ResBlock( in_channels=num_channels - self.action_encoding_dim, activation=self.activation, norm_type='BN', res_type='basic', bias=False ) for _ in range(num_res_blocks) ] ) self.reward_resblocks = nn.ModuleList( [ ResBlock( in_channels=num_channels - self.action_encoding_dim, activation=self.activation, norm_type='BN', res_type='basic', bias=False ) for _ in range(num_res_blocks) ] ) self.conv1x1_reward = nn.Conv2d(num_channels - self.action_encoding_dim, reward_head_channels, 1) if norm_type == 'BN': self.norm_reward = nn.BatchNorm2d(reward_head_channels) elif norm_type == 'LN': if downsample: self.norm_reward = nn.LayerNorm([reward_head_channels, math.ceil(observation_shape[-2] / 16), math.ceil(observation_shape[-1] / 16)]) else: self.norm_reward = nn.LayerNorm([reward_head_channels, observation_shape[-2], observation_shape[-1]]) # input_shape: （sequence_length，batch_size，input_size) # output_shape: (sequence_length, batch_size, hidden_size) self.lstm = nn.LSTM(input_size=self.flatten_output_size_for_reward_head, hidden_size=self.lstm_hidden_size) self.norm_value_prefix = nn.BatchNorm1d(self.lstm_hidden_size) self.fc_reward_head = MLP( in_channels=self.lstm_hidden_size, hidden_channels=fc_reward_layers[0], out_channels=output_support_size, layer_num=len(fc_reward_layers) + 1, activation=self.activation, norm_type=norm_type, output_activation=False, output_norm=False, last_linear_layer_init_zero=last_linear_layer_init_zero ) def forward(self, state_action_encoding: torch.Tensor, reward_hidden_state: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, Tuple, torch.Tensor]: """ Overview: Forward computation of the dynamics network. Predict next latent state, next reward hidden state and value prefix given current state_action_encoding and reward hidden state. Arguments: - state_action_encoding (:obj:`torch.Tensor`): The state-action encoding, which is the concatenation of \ latent state and action encoding, with shape (batch_size, num_channels, height, width). - reward_hidden_state (:obj:`Tuple[torch.Tensor, torch.Tensor]`): The input hidden state of LSTM about reward. Returns: - next_latent_state (:obj:`torch.Tensor`): The next latent state, with shape (batch_size, num_channels, \ height, width). - next_reward_hidden_state (:obj:`torch.Tensor`): The input hidden state of LSTM about reward. - value_prefix (:obj:`torch.Tensor`): The predicted prefix sum of value for input state. """ # take the state encoding, state_action_encoding[:, -self.action_encoding_dim:, :, :] is action encoding state_encoding = state_action_encoding[:, :-self.action_encoding_dim:, :, :] x = self.conv(state_action_encoding) x = self.norm_common(x) # the residual link: add state encoding to the state_action encoding x += state_encoding x = self.activation(x) for block in self.resblocks: x = block(x) next_latent_state = x x = self.conv1x1_reward(next_latent_state) x = self.norm_reward(x) x = self.activation(x) x = x.reshape(-1, self.flatten_output_size_for_reward_head).unsqueeze(0) # use lstm to predict value_prefix and reward_hidden_state value_prefix, next_reward_hidden_state = self.lstm(x, reward_hidden_state) value_prefix = value_prefix.squeeze(0) value_prefix = self.norm_value_prefix(value_prefix) value_prefix = self.activation(value_prefix) value_prefix = self.fc_reward_head(value_prefix) return next_latent_state, next_reward_hidden_state, value_prefix def get_dynamic_mean(self) -> float: return get_dynamic_mean(self) def get_reward_mean(self) -> Tuple[ndarray, float]: return get_reward_mean(self)