Source code for lzero.model.stochastic_muzero_model_mlp

from typing import Optional, Tuple

import torch
import torch.nn as nn
from ding.utils import MODEL_REGISTRY, SequenceType

from .common import RepresentationNetworkMLP, PredictionNetworkMLP
from .muzero_model_mlp import DynamicsNetwork
from .stochastic_muzero_model import StochasticMuZeroModel, ChanceEncoder
from .utils import renormalize


[docs]@MODEL_REGISTRY.register('StochasticMuZeroModelMLP') class StochasticMuZeroModelMLP(StochasticMuZeroModel):
[docs] def __init__( self, observation_shape: int = 2, action_space_size: int = 6, chance_space_size: int = 2, latent_state_dim: int = 256, 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 = False, categorical_distribution: bool = True, activation: Optional[nn.Module] = nn.ReLU(inplace=True), last_linear_layer_init_zero: bool = True, state_norm: bool = False, discrete_action_encoding_type: str = 'one_hot', norm_type: Optional[str] = 'BN', res_connection_in_dynamics: bool = False, *args, **kwargs ): """ Overview: The definition of the network model of Stochastic, which is a generalization version for 1D vector obs. \ The networks are mainly built on fully connected layers. \ The representation network is an MLP network which maps the raw observation to a latent state. \ The dynamics network is an MLP network which predicts the next latent state, and reward given the current latent state and action. \ The prediction network is an MLP network which predicts the value and policy given the current latent state. Arguments: - observation_shape (:obj:`int`): Observation space shape, e.g. 8 for Lunarlander. - action_space_size: (:obj:`int`): Action space size, usually an integer number for discrete action space. - action_space_size: (:obj:`int`): Action space size, e.g. 4 for Lunarlander. - latent_state_dim (:obj:`int`): The dimension of latent state, such as 256. - 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. - self_supervised_learning_loss (:obj:`bool`): Whether to use self_supervised_learning related networks in Stochastic model, default set it to False. - categorical_distribution (:obj:`bool`): Whether to use discrete support to represent categorical distribution for value, reward/value_prefix. - activation (:obj:`Optional[nn.Module]`): Activation function used in network, which often use in-place \ operation to speedup, e.g. ReLU(inplace=True). - last_linear_layer_init_zero (:obj:`bool`): Whether to use zero initializations for the last layer of value/policy mlp, default sets it to True. - state_norm (:obj:`bool`): Whether to use normalization for latent states, default sets it to True. - discrete_action_encoding_type (:obj:`str`): The encoding type of discrete action, which can be 'one_hot' or 'not_one_hot'. - norm_type (:obj:`str`): The type of normalization in networks. defaults to 'BN'. - res_connection_in_dynamics (:obj:`bool`): Whether to use residual connection for dynamics network, default set it to False. """ super(StochasticMuZeroModelMLP, self).__init__() self.categorical_distribution = categorical_distribution if not self.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 self.chance_space_size = chance_space_size self.continuous_action_space = False # The dim of action space. For discrete action space, it is 1. # For continuous action space, it is the dimension of continuous action. self.action_space_dim = action_space_size if self.continuous_action_space else 1 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.continuous_action_space: self.action_encoding_dim = action_space_size else: 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.latent_state_dim = latent_state_dim self.proj_hid = proj_hid self.proj_out = proj_out self.pred_hid = pred_hid self.pred_out = pred_out self.self_supervised_learning_loss = self_supervised_learning_loss self.last_linear_layer_init_zero = last_linear_layer_init_zero self.state_norm = state_norm self.res_connection_in_dynamics = res_connection_in_dynamics self.representation_network = RepresentationNetworkMLP( observation_shape=observation_shape, hidden_channels=self.latent_state_dim, norm_type=norm_type ) # TODO(pu): different input data type for chance_encoder # here, the input is two concatenated frames self.chance_encoder = ChanceEncoder(observation_shape * 2, chance_space_size, encoder_backbone_type='mlp') self.dynamics_network = DynamicsNetwork( action_encoding_dim=self.chance_space_size, num_channels=self.latent_state_dim + self.chance_space_size, common_layer_num=2, fc_reward_layers=fc_reward_layers, output_support_size=self.reward_support_size, last_linear_layer_init_zero=self.last_linear_layer_init_zero, norm_type=norm_type, res_connection_in_dynamics=self.res_connection_in_dynamics, ) self.prediction_network = PredictionNetworkMLP( action_space_size=action_space_size, num_channels=latent_state_dim, fc_value_layers=fc_value_layers, fc_policy_layers=fc_policy_layers, output_support_size=self.value_support_size, last_linear_layer_init_zero=self.last_linear_layer_init_zero, norm_type=norm_type ) self.afterstate_dynamics_network = AfterstateDynamicsNetwork( action_encoding_dim=self.action_encoding_dim, num_channels=self.latent_state_dim + self.action_encoding_dim, common_layer_num=2, fc_reward_layers=fc_reward_layers, output_support_size=self.reward_support_size, last_linear_layer_init_zero=self.last_linear_layer_init_zero, norm_type=norm_type, res_connection_in_dynamics=self.res_connection_in_dynamics, ) self.afterstate_prediction_network = AfterstatePredictionNetworkMLP( chance_space_size=chance_space_size, num_channels=latent_state_dim, fc_value_layers=fc_value_layers, fc_policy_layers=fc_policy_layers, output_support_size=self.value_support_size, last_linear_layer_init_zero=self.last_linear_layer_init_zero, norm_type=norm_type ) if self.self_supervised_learning_loss: # self_supervised_learning_loss related network proposed in EfficientZero self.projection_input_dim = latent_state_dim 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), )
[docs] def _dynamics(self, latent_state: torch.Tensor, action: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """ Overview: Concatenate ``latent_state`` and ``action`` and use the dynamics network to predict ``next_latent_state`` \ ``reward`` 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. - reward (:obj:`torch.Tensor`): The predicted reward for input state. Shapes: - latent_state (:obj:`torch.Tensor`): :math:`(B, H)`, where B is batch_size, H is the dimension of latent state. - action (:obj:`torch.Tensor`): :math:`(B, )`, where B is batch_size. - next_latent_state (:obj:`torch.Tensor`): :math:`(B, H)`, where B is batch_size, H is the dimension of latent state. - reward (:obj:`torch.Tensor`): :math:`(B, reward_support_size)`, where B is batch_size. """ # NOTE: the discrete action encoding type is important for some environments # Stack latent_state with the one hot encoded action 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.chance_space_size, device=action.device) # transform action to torch.int64 action = action.long() action_one_hot.scatter_(1, action, 1) action_encoding = action_one_hot action_encoding = action_encoding.to(latent_state.device).float() # state_action_encoding shape: (batch_size, latent_state[1] + action_dim]) or # (batch_size, latent_state[1] + action_space_size]) depending on the discrete_action_encoding_type. state_action_encoding = torch.cat((latent_state, action_encoding), dim=1) next_latent_state, reward = self.dynamics_network(state_action_encoding) if not self.state_norm: return next_latent_state, reward else: next_latent_state_normalized = renormalize(next_latent_state) return next_latent_state_normalized, reward
[docs] def _afterstate_dynamics(self, latent_state: torch.Tensor, action: torch.Tensor) -> Tuple[ torch.Tensor, torch.Tensor]: """ Overview: Concatenate ``latent_state`` and ``action`` and use the dynamics network to predict ``next_latent_state`` \ ``reward`` 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. - reward (:obj:`torch.Tensor`): The predicted reward for input state. Shapes: - latent_state (:obj:`torch.Tensor`): :math:`(B, H)`, where B is batch_size, H is the dimension of latent state. - action (:obj:`torch.Tensor`): :math:`(B, )`, where B is batch_size. - next_latent_state (:obj:`torch.Tensor`): :math:`(B, H)`, where B is batch_size, H is the dimension of latent state. - reward (: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 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 = action_one_hot elif self.discrete_action_encoding_type == 'not_one_hot': action_encoding = action / self.action_space_size if len(action_encoding.shape) == 1: # (batch_size, ) -> (batch_size, 1) # e.g., torch.Size([8]) -> torch.Size([8, 1]) action_encoding = action_encoding.unsqueeze(-1) action_encoding = action_encoding.to(latent_state.device).float() # state_action_encoding shape: (batch_size, latent_state[1] + action_dim]) or # (batch_size, latent_state[1] + action_space_size]) depending on the discrete_action_encoding_type. state_action_encoding = torch.cat((latent_state, action_encoding), dim=1) next_latent_state, reward = self.afterstate_dynamics_network(state_action_encoding) if not self.state_norm: return next_latent_state, reward else: next_latent_state_normalized = renormalize(next_latent_state) return next_latent_state_normalized, reward
[docs] def project(self, latent_state: torch.Tensor, with_grad=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)`, where B is batch_size, H is the dimension of latent state. - proj (:obj:`torch.Tensor`): :math:`(B, projection_output_dim)`, where B is batch_size. Examples: >>> latent_state = torch.randn(256, 64) >>> output = self.project(latent_state) >>> output.shape # (256, 1024) """ proj = self.projection(latent_state) if with_grad: # with grad, use prediction_head return self.prediction_head(proj) else: return proj.detach()
AfterstateDynamicsNetwork = DynamicsNetwork class AfterstatePredictionNetworkMLP(PredictionNetworkMLP): def __init__( self, chance_space_size, num_channels, common_layer_num: int = 2, fc_value_layers: SequenceType = [32], fc_policy_layers: SequenceType = [32], output_support_size: int = 601, last_linear_layer_init_zero: bool = True, activation: Optional[nn.Module] = nn.ReLU(inplace=True), norm_type: Optional[str] = 'BN', ): """ Overview: The definition of policy and value prediction network with Multi-Layer Perceptron (MLP), \ which is used to predict value and policy by the given latent state. Arguments: - chance_space_size: (:obj:`int`): Chance space size, usually an integer number. For discrete action \ space, it is the number of discrete chance outcomes. - num_channels (:obj:`int`): The channels of latent states. - 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). - output_support_size (:obj:`int`): The size of categorical value output. - last_linear_layer_init_zero (:obj:`bool`): Whether to use zero initializations for the last layer of \ dynamics/prediction 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. defaults to 'BN'. """ super(AfterstatePredictionNetworkMLP, self).__init__(chance_space_size, num_channels, common_layer_num, fc_value_layers, fc_policy_layers, output_support_size, last_linear_layer_init_zero , activation, norm_type)