tianshou.utils¶
Utils package.
-
class
tianshou.utils.
MovAvg
(size: int = 100)[source]¶ Bases:
object
Class for moving average.
It will automatically exclude the infinity and NaN. Usage:
>>> stat = MovAvg(size=66) >>> stat.add(torch.tensor(5)) 5.0 >>> stat.add(float('inf')) # which will not add to stat 5.0 >>> stat.add([6, 7, 8]) 6.5 >>> stat.get() 6.5 >>> print(f'{stat.mean():.2f}±{stat.std():.2f}') 6.50±1.12
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class
tianshou.utils.
RunningMeanStd
(mean: Union[float, numpy.ndarray] = 0.0, std: Union[float, numpy.ndarray] = 1.0, clip_max: Optional[float] = 10.0, epsilon: float = 1.1920928955078125e-07)[source]¶ Bases:
object
Calculates the running mean and std of a data stream.
https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
- Parameters
mean – the initial mean estimation for data array. Default to 0.
std – the initial standard error estimation for data array. Default to 1.
clip_max (float) – the maximum absolute value for data array. Default to 10.0.
epsilon (float) – To avoid division by zero.
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class
tianshou.utils.
DummyTqdm
(total: int, **kwargs: Any)[source]¶ Bases:
object
A dummy tqdm class that keeps stats but without progress bar.
It supports
__enter__
and__exit__
, update and a dummyset_postfix
, which is the interface that trainers use.Note
Using
disable=True
in tqdm config results in infinite loop, thus this class is created. See the discussion at #641 for details.
-
class
tianshou.utils.
BaseLogger
(train_interval: int = 1000, test_interval: int = 1, update_interval: int = 1000)[source]¶ Bases:
abc.ABC
The base class for any logger which is compatible with trainer.
Try to overwrite write() method to use your own writer.
- Parameters
train_interval (int) – the log interval in log_train_data(). Default to 1000.
test_interval (int) – the log interval in log_test_data(). Default to 1.
update_interval (int) – the log interval in log_update_data(). Default to 1000.
-
abstract
write
(step_type: str, step: int, data: Dict[str, Union[int, numbers.Number, numpy.number, numpy.ndarray]]) → None[source]¶ Specify how the writer is used to log data.
- Parameters
step_type (str) – namespace which the data dict belongs to.
step (int) – stands for the ordinate of the data dict.
data (dict) – the data to write with format
{key: value}
.
-
log_train_data
(collect_result: dict, step: int) → None[source]¶ Use writer to log statistics generated during training.
- Parameters
collect_result – a dict containing information of data collected in training stage, i.e., returns of collector.collect().
step (int) – stands for the timestep the collect_result being logged.
-
log_test_data
(collect_result: dict, step: int) → None[source]¶ Use writer to log statistics generated during evaluating.
- Parameters
collect_result – a dict containing information of data collected in evaluating stage, i.e., returns of collector.collect().
step (int) – stands for the timestep the collect_result being logged.
-
log_update_data
(update_result: dict, step: int) → None[source]¶ Use writer to log statistics generated during updating.
- Parameters
update_result – a dict containing information of data collected in updating stage, i.e., returns of policy.update().
step (int) – stands for the timestep the collect_result being logged.
-
abstract
save_data
(epoch: int, env_step: int, gradient_step: int, save_checkpoint_fn: Optional[Callable[[int, int, int], str]] = None) → None[source]¶ Use writer to log metadata when calling
save_checkpoint_fn
in trainer.- Parameters
epoch (int) – the epoch in trainer.
env_step (int) – the env_step in trainer.
gradient_step (int) – the gradient_step in trainer.
save_checkpoint_fn (function) – a hook defined by user, see trainer documentation for detail.
-
class
tianshou.utils.
TensorboardLogger
(writer: torch.utils.tensorboard.writer.SummaryWriter, train_interval: int = 1000, test_interval: int = 1, update_interval: int = 1000, save_interval: int = 1, write_flush: bool = True)[source]¶ Bases:
tianshou.utils.logger.base.BaseLogger
A logger that relies on tensorboard SummaryWriter by default to visualize and log statistics.
- Parameters
writer (SummaryWriter) – the writer to log data.
train_interval (int) – the log interval in log_train_data(). Default to 1000.
test_interval (int) – the log interval in log_test_data(). Default to 1.
update_interval (int) – the log interval in log_update_data(). Default to 1000.
save_interval (int) – the save interval in save_data(). Default to 1 (save at the end of each epoch).
write_flush (bool) – whether to flush tensorboard result after each add_scalar operation. Default to True.
-
write
(step_type: str, step: int, data: Dict[str, Union[int, numbers.Number, numpy.number, numpy.ndarray]]) → None[source]¶ Specify how the writer is used to log data.
- Parameters
step_type (str) – namespace which the data dict belongs to.
step (int) – stands for the ordinate of the data dict.
data (dict) – the data to write with format
{key: value}
.
-
save_data
(epoch: int, env_step: int, gradient_step: int, save_checkpoint_fn: Optional[Callable[[int, int, int], str]] = None) → None[source]¶ Use writer to log metadata when calling
save_checkpoint_fn
in trainer.- Parameters
epoch (int) – the epoch in trainer.
env_step (int) – the env_step in trainer.
gradient_step (int) – the gradient_step in trainer.
save_checkpoint_fn (function) – a hook defined by user, see trainer documentation for detail.
-
class
tianshou.utils.
BasicLogger
(*args: Any, **kwargs: Any)[source]¶ Bases:
tianshou.utils.logger.tensorboard.TensorboardLogger
BasicLogger has changed its name to TensorboardLogger in #427.
This class is for compatibility.
-
class
tianshou.utils.
LazyLogger
[source]¶ Bases:
tianshou.utils.logger.base.BaseLogger
A logger that does nothing. Used as the placeholder in trainer.
-
write
(step_type: str, step: int, data: Dict[str, Union[int, numbers.Number, numpy.number, numpy.ndarray]]) → None[source]¶ The LazyLogger writes nothing.
-
save_data
(epoch: int, env_step: int, gradient_step: int, save_checkpoint_fn: Optional[Callable[[int, int, int], str]] = None) → None[source]¶ Use writer to log metadata when calling
save_checkpoint_fn
in trainer.- Parameters
epoch (int) – the epoch in trainer.
env_step (int) – the env_step in trainer.
gradient_step (int) – the gradient_step in trainer.
save_checkpoint_fn (function) – a hook defined by user, see trainer documentation for detail.
-
-
class
tianshou.utils.
WandbLogger
(train_interval: int = 1000, test_interval: int = 1, update_interval: int = 1000, save_interval: int = 1000, write_flush: bool = True, project: Optional[str] = None, name: Optional[str] = None, entity: Optional[str] = None, run_id: Optional[str] = None, config: Optional[argparse.Namespace] = None)[source]¶ Bases:
tianshou.utils.logger.base.BaseLogger
Weights and Biases logger that sends data to https://wandb.ai/.
This logger creates three panels with plots: train, test, and update. Make sure to select the correct access for each panel in weights and biases:
Example of usage:
logger = WandbLogger() logger.load(SummaryWriter(log_path)) result = onpolicy_trainer(policy, train_collector, test_collector, logger=logger)
- Parameters
train_interval (int) – the log interval in log_train_data(). Default to 1000.
test_interval (int) – the log interval in log_test_data(). Default to 1.
update_interval (int) – the log interval in log_update_data(). Default to 1000.
save_interval (int) – the save interval in save_data(). Default to 1 (save at the end of each epoch).
write_flush (bool) – whether to flush tensorboard result after each add_scalar operation. Default to True.
project (str) – W&B project name. Default to “tianshou”.
name (str) – W&B run name. Default to None. If None, random name is assigned.
entity (str) – W&B team/organization name. Default to None.
run_id (str) – run id of W&B run to be resumed. Default to None.
config (argparse.Namespace) – experiment configurations. Default to None.
-
write
(step_type: str, step: int, data: Dict[str, Union[int, numbers.Number, numpy.number, numpy.ndarray]]) → None[source]¶ Specify how the writer is used to log data.
- Parameters
step_type (str) – namespace which the data dict belongs to.
step (int) – stands for the ordinate of the data dict.
data (dict) – the data to write with format
{key: value}
.
-
save_data
(epoch: int, env_step: int, gradient_step: int, save_checkpoint_fn: Optional[Callable[[int, int, int], str]] = None) → None[source]¶ Use writer to log metadata when calling
save_checkpoint_fn
in trainer.- Parameters
epoch (int) – the epoch in trainer.
env_step (int) – the env_step in trainer.
gradient_step (int) – the gradient_step in trainer.
save_checkpoint_fn (function) – a hook defined by user, see trainer documentation for detail.
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class
tianshou.utils.
MultipleLRSchedulers
(*args: torch.optim.lr_scheduler.LambdaLR)[source]¶ Bases:
object
A wrapper for multiple learning rate schedulers.
Every time
step()
is called, it calls the step() method of each of the schedulers that it contains. Example usage:scheduler1 = ConstantLR(opt1, factor=0.1, total_iters=2) scheduler2 = ExponentialLR(opt2, gamma=0.9) scheduler = MultipleLRSchedulers(scheduler1, scheduler2) policy = PPOPolicy(..., lr_scheduler=scheduler)
Pre-defined Networks¶
Common¶
-
tianshou.utils.net.common.
miniblock
(input_size: int, output_size: int = 0, norm_layer: Optional[Type[torch.nn.modules.module.Module]] = None, activation: Optional[Type[torch.nn.modules.module.Module]] = None, linear_layer: Type[torch.nn.modules.linear.Linear] = <class 'torch.nn.modules.linear.Linear'>) → List[torch.nn.modules.module.Module][source]¶ Construct a miniblock with given input/output-size, norm layer and activation.
-
class
tianshou.utils.net.common.
MLP
(input_dim: int, output_dim: int = 0, hidden_sizes: Sequence[int] = (), norm_layer: Optional[Union[Type[torch.nn.modules.module.Module], Sequence[Type[torch.nn.modules.module.Module]]]] = None, activation: Optional[Union[Type[torch.nn.modules.module.Module], Sequence[Type[torch.nn.modules.module.Module]]]] = <class 'torch.nn.modules.activation.ReLU'>, device: Optional[Union[str, int, torch.device]] = None, linear_layer: Type[torch.nn.modules.linear.Linear] = <class 'torch.nn.modules.linear.Linear'>, flatten_input: bool = True)[source]¶ Bases:
torch.nn.modules.module.Module
Simple MLP backbone.
Create a MLP of size input_dim * hidden_sizes[0] * hidden_sizes[1] * … * hidden_sizes[-1] * output_dim
- Parameters
input_dim (int) – dimension of the input vector.
output_dim (int) – dimension of the output vector. If set to 0, there is no final linear layer.
hidden_sizes – shape of MLP passed in as a list, not including input_dim and output_dim.
norm_layer – use which normalization before activation, e.g.,
nn.LayerNorm
andnn.BatchNorm1d
. Default to no normalization. You can also pass a list of normalization modules with the same length of hidden_sizes, to use different normalization module in different layers. Default to no normalization.activation – which activation to use after each layer, can be both the same activation for all layers if passed in nn.Module, or different activation for different Modules if passed in a list. Default to nn.ReLU.
device – which device to create this model on. Default to None.
linear_layer – use this module as linear layer. Default to nn.Linear.
flatten_input (bool) – whether to flatten input data. Default to True.
-
forward
(obs: Union[numpy.ndarray, torch.Tensor]) → torch.Tensor[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
training
: bool¶
-
class
tianshou.utils.net.common.
Net
(state_shape: Union[int, Sequence[int]], action_shape: Union[int, Sequence[int]] = 0, hidden_sizes: Sequence[int] = (), norm_layer: Optional[Type[torch.nn.modules.module.Module]] = None, activation: Optional[Type[torch.nn.modules.module.Module]] = <class 'torch.nn.modules.activation.ReLU'>, device: Union[str, int, torch.device] = 'cpu', softmax: bool = False, concat: bool = False, num_atoms: int = 1, dueling_param: Optional[Tuple[Dict[str, Any], Dict[str, Any]]] = None, linear_layer: Type[torch.nn.modules.linear.Linear] = <class 'torch.nn.modules.linear.Linear'>)[source]¶ Bases:
torch.nn.modules.module.Module
Wrapper of MLP to support more specific DRL usage.
For advanced usage (how to customize the network), please refer to Build the Network.
- Parameters
state_shape – int or a sequence of int of the shape of state.
action_shape – int or a sequence of int of the shape of action.
hidden_sizes – shape of MLP passed in as a list.
norm_layer – use which normalization before activation, e.g.,
nn.LayerNorm
andnn.BatchNorm1d
. Default to no normalization. You can also pass a list of normalization modules with the same length of hidden_sizes, to use different normalization module in different layers. Default to no normalization.activation – which activation to use after each layer, can be both the same activation for all layers if passed in nn.Module, or different activation for different Modules if passed in a list. Default to nn.ReLU.
device – specify the device when the network actually runs. Default to “cpu”.
softmax (bool) – whether to apply a softmax layer over the last layer’s output.
concat (bool) – whether the input shape is concatenated by state_shape and action_shape. If it is True,
action_shape
is not the output shape, but affects the input shape only.num_atoms (int) – in order to expand to the net of distributional RL. Default to 1 (not use).
dueling_param (bool) – whether to use dueling network to calculate Q values (for Dueling DQN). If you want to use dueling option, you should pass a tuple of two dict (first for Q and second for V) stating self-defined arguments as stated in class:~tianshou.utils.net.common.MLP. Default to None.
linear_layer – use this module as linear layer. Default to nn.Linear.
See also
Please refer to
MLP
for more detailed explanation on the usage of activation, norm_layer, etc.You can also refer to
Actor
,Critic
, etc, to see how it’s suggested be used.-
forward
(obs: Union[numpy.ndarray, torch.Tensor], state: Optional[Any] = None, info: Dict[str, Any] = {}) → Tuple[torch.Tensor, Any][source]¶ Mapping: obs -> flatten (inside MLP)-> logits.
-
training
: bool¶
-
class
tianshou.utils.net.common.
Recurrent
(layer_num: int, state_shape: Union[int, Sequence[int]], action_shape: Union[int, Sequence[int]], device: Union[str, int, torch.device] = 'cpu', hidden_layer_size: int = 128)[source]¶ Bases:
torch.nn.modules.module.Module
Simple Recurrent network based on LSTM.
For advanced usage (how to customize the network), please refer to Build the Network.
-
forward
(obs: Union[numpy.ndarray, torch.Tensor], state: Optional[Dict[str, torch.Tensor]] = None, info: Dict[str, Any] = {}) → Tuple[torch.Tensor, Dict[str, torch.Tensor]][source]¶ Mapping: obs -> flatten -> logits.
In the evaluation mode, obs should be with shape
[bsz, dim]
; in the training mode, obs should be with shape[bsz, len, dim]
. See the code and comment for more detail.
-
training
: bool¶
-
-
class
tianshou.utils.net.common.
ActorCritic
(actor: torch.nn.modules.module.Module, critic: torch.nn.modules.module.Module)[source]¶ Bases:
torch.nn.modules.module.Module
An actor-critic network for parsing parameters.
Using
actor_critic.parameters()
instead of set.union or list+list to avoid issue #449.- Parameters
actor (nn.Module) – the actor network.
critic (nn.Module) – the critic network.
-
training
: bool¶
-
class
tianshou.utils.net.common.
DataParallelNet
(net: torch.nn.modules.module.Module)[source]¶ Bases:
torch.nn.modules.module.Module
DataParallel wrapper for training agent with multi-GPU.
This class does only the conversion of input data type, from numpy array to torch’s Tensor. If the input is a nested dictionary, the user should create a similar class to do the same thing.
- Parameters
net (nn.Module) – the network to be distributed in different GPUs.
-
forward
(obs: Union[numpy.ndarray, torch.Tensor], *args: Any, **kwargs: Any) → Tuple[Any, Any][source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
training
: bool¶
-
class
tianshou.utils.net.common.
EnsembleLinear
(ensemble_size: int, in_feature: int, out_feature: int, bias: bool = True)[source]¶ Bases:
torch.nn.modules.module.Module
Linear Layer of Ensemble network.
- Parameters
ensemble_size (int) – Number of subnets in the ensemble.
inp_feature (int) – dimension of the input vector.
out_feature (int) – dimension of the output vector.
bias (bool) – whether to include an additive bias, default to be True.
-
forward
(x: torch.Tensor) → torch.Tensor[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
training
: bool¶
-
class
tianshou.utils.net.common.
BranchingNet
(state_shape: Union[int, Sequence[int]], num_branches: int = 0, action_per_branch: int = 2, common_hidden_sizes: List[int] = [], value_hidden_sizes: List[int] = [], action_hidden_sizes: List[int] = [], norm_layer: Optional[Type[torch.nn.modules.module.Module]] = None, activation: Optional[Type[torch.nn.modules.module.Module]] = <class 'torch.nn.modules.activation.ReLU'>, device: Union[str, int, torch.device] = 'cpu')[source]¶ Bases:
torch.nn.modules.module.Module
Branching dual Q network.
Network for the BranchingDQNPolicy, it uses a common network module, a value module and action “branches” one for each dimension.It allows for a linear scaling of Q-value the output w.r.t. the number of dimensions in the action space. For more info please refer to: arXiv:1711.08946. :param state_shape: int or a sequence of int of the shape of state. :param action_shape: int or a sequence of int of the shape of action. :param action_peer_branch: int or a sequence of int of the number of actions in each dimension. :param common_hidden_sizes: shape of the common MLP network passed in as a list. :param value_hidden_sizes: shape of the value MLP network passed in as a list. :param action_hidden_sizes: shape of the action MLP network passed in as a list. :param norm_layer: use which normalization before activation, e.g.,
nn.LayerNorm
andnn.BatchNorm1d
. Default to no normalization. You can also pass a list of normalization modules with the same length of hidden_sizes, to use different normalization module in different layers. Default to no normalization. :param activation: which activation to use after each layer, can be both the same activation for all layers if passed in nn.Module, or different activation for different Modules if passed in a list. Default to nn.ReLU. :param device: specify the device when the network actually runs. Default to “cpu”. :param bool softmax: whether to apply a softmax layer over the last layer’s output.-
training
: bool¶
-
-
tianshou.utils.net.common.
get_dict_state_decorator
(state_shape: Dict[str, Union[int, Sequence[int]]], keys: Sequence[str]) → Tuple[Callable, int][source]¶ A helper function to make Net or equivalent classes (e.g. Actor, Critic) applicable to dict state.
The first return item,
decorator_fn
, will alter the implementation of forward function of the given class by preprocessing the observation. The preprocessing is basically flatten the observation and concatenate them based on thekeys
order. The batch dimension is preserved if presented. The result observation shape will be equal tonew_state_shape
, the second return item.- Parameters
state_shape – A dictionary indicating each state’s shape
keys – A list of state’s keys. The flatten observation will be according to this list order.
- Returns
a 2-items tuple
decorator_fn
andnew_state_shape
Discrete¶
-
class
tianshou.utils.net.discrete.
Actor
(preprocess_net: torch.nn.modules.module.Module, action_shape: Sequence[int], hidden_sizes: Sequence[int] = (), softmax_output: bool = True, preprocess_net_output_dim: Optional[int] = None, device: Union[str, int, torch.device] = 'cpu')[source]¶ Bases:
torch.nn.modules.module.Module
Simple actor network.
Will create an actor operated in discrete action space with structure of preprocess_net —> action_shape.
- Parameters
preprocess_net – a self-defined preprocess_net which output a flattened hidden state.
action_shape – a sequence of int for the shape of action.
hidden_sizes – a sequence of int for constructing the MLP after preprocess_net. Default to empty sequence (where the MLP now contains only a single linear layer).
softmax_output (bool) – whether to apply a softmax layer over the last layer’s output.
preprocess_net_output_dim (int) – the output dimension of preprocess_net.
For advanced usage (how to customize the network), please refer to Build the Network.
See also
Please refer to
Net
as an instance of how preprocess_net is suggested to be defined.-
forward
(obs: Union[numpy.ndarray, torch.Tensor], state: Optional[Any] = None, info: Dict[str, Any] = {}) → Tuple[torch.Tensor, Any][source]¶ Mapping: s -> Q(s, *).
-
training
: bool¶
-
class
tianshou.utils.net.discrete.
Critic
(preprocess_net: torch.nn.modules.module.Module, hidden_sizes: Sequence[int] = (), last_size: int = 1, preprocess_net_output_dim: Optional[int] = None, device: Union[str, int, torch.device] = 'cpu')[source]¶ Bases:
torch.nn.modules.module.Module
Simple critic network. Will create an actor operated in discrete action space with structure of preprocess_net —> 1(q value).
- Parameters
preprocess_net – a self-defined preprocess_net which output a flattened hidden state.
hidden_sizes – a sequence of int for constructing the MLP after preprocess_net. Default to empty sequence (where the MLP now contains only a single linear layer).
last_size (int) – the output dimension of Critic network. Default to 1.
preprocess_net_output_dim (int) – the output dimension of preprocess_net.
For advanced usage (how to customize the network), please refer to Build the Network.
See also
Please refer to
Net
as an instance of how preprocess_net is suggested to be defined.-
forward
(obs: Union[numpy.ndarray, torch.Tensor], **kwargs: Any) → torch.Tensor[source]¶ Mapping: s -> V(s).
-
training
: bool¶
-
class
tianshou.utils.net.discrete.
CosineEmbeddingNetwork
(num_cosines: int, embedding_dim: int)[source]¶ Bases:
torch.nn.modules.module.Module
Cosine embedding network for IQN. Convert a scalar in [0, 1] to a list of n-dim vectors.
- Parameters
num_cosines – the number of cosines used for the embedding.
embedding_dim – the dimension of the embedding/output.
Note
From https://github.com/ku2482/fqf-iqn-qrdqn.pytorch/blob/master /fqf_iqn_qrdqn/network.py .
-
forward
(taus: torch.Tensor) → torch.Tensor[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
training
: bool¶
-
class
tianshou.utils.net.discrete.
ImplicitQuantileNetwork
(preprocess_net: torch.nn.modules.module.Module, action_shape: Sequence[int], hidden_sizes: Sequence[int] = (), num_cosines: int = 64, preprocess_net_output_dim: Optional[int] = None, device: Union[str, int, torch.device] = 'cpu')[source]¶ Bases:
tianshou.utils.net.discrete.Critic
Implicit Quantile Network.
- Parameters
preprocess_net – a self-defined preprocess_net which output a flattened hidden state.
action_shape (int) – a sequence of int for the shape of action.
hidden_sizes – a sequence of int for constructing the MLP after preprocess_net. Default to empty sequence (where the MLP now contains only a single linear layer).
num_cosines (int) – the number of cosines to use for cosine embedding. Default to 64.
preprocess_net_output_dim (int) – the output dimension of preprocess_net.
Note
Although this class inherits Critic, it is actually a quantile Q-Network with output shape (batch_size, action_dim, sample_size).
The second item of the first return value is tau vector.
-
forward
(obs: Union[numpy.ndarray, torch.Tensor], sample_size: int, **kwargs: Any) → Tuple[Any, torch.Tensor][source]¶ Mapping: s -> Q(s, *).
-
training
: bool¶
-
class
tianshou.utils.net.discrete.
FractionProposalNetwork
(num_fractions: int, embedding_dim: int)[source]¶ Bases:
torch.nn.modules.module.Module
Fraction proposal network for FQF.
- Parameters
num_fractions – the number of factions to propose.
embedding_dim – the dimension of the embedding/input.
Note
Adapted from https://github.com/ku2482/fqf-iqn-qrdqn.pytorch/blob/master /fqf_iqn_qrdqn/network.py .
-
forward
(obs_embeddings: torch.Tensor) → Tuple[torch.Tensor, torch.Tensor, torch.Tensor][source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
training
: bool¶
-
class
tianshou.utils.net.discrete.
FullQuantileFunction
(preprocess_net: torch.nn.modules.module.Module, action_shape: Sequence[int], hidden_sizes: Sequence[int] = (), num_cosines: int = 64, preprocess_net_output_dim: Optional[int] = None, device: Union[str, int, torch.device] = 'cpu')[source]¶ Bases:
tianshou.utils.net.discrete.ImplicitQuantileNetwork
Full(y parameterized) Quantile Function.
- Parameters
preprocess_net – a self-defined preprocess_net which output a flattened hidden state.
action_shape (int) – a sequence of int for the shape of action.
hidden_sizes – a sequence of int for constructing the MLP after preprocess_net. Default to empty sequence (where the MLP now contains only a single linear layer).
num_cosines (int) – the number of cosines to use for cosine embedding. Default to 64.
preprocess_net_output_dim (int) – the output dimension of preprocess_net.
Note
The first return value is a tuple of (quantiles, fractions, quantiles_tau), where fractions is a Batch(taus, tau_hats, entropies).
-
forward
(obs: Union[numpy.ndarray, torch.Tensor], propose_model: tianshou.utils.net.discrete.FractionProposalNetwork, fractions: Optional[tianshou.data.batch.Batch] = None, **kwargs: Any) → Tuple[Any, torch.Tensor][source]¶ Mapping: s -> Q(s, *).
-
training
: bool¶
-
class
tianshou.utils.net.discrete.
NoisyLinear
(in_features: int, out_features: int, noisy_std: float = 0.5)[source]¶ Bases:
torch.nn.modules.module.Module
Implementation of Noisy Networks. arXiv:1706.10295.
- Parameters
in_features (int) – the number of input features.
out_features (int) – the number of output features.
noisy_std (float) – initial standard deviation of noisy linear layers.
Note
Adapted from https://github.com/ku2482/fqf-iqn-qrdqn.pytorch/blob/master /fqf_iqn_qrdqn/network.py .
-
forward
(x: torch.Tensor) → torch.Tensor[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
training
: bool¶
-
tianshou.utils.net.discrete.
sample_noise
(model: torch.nn.modules.module.Module) → bool[source]¶ Sample the random noises of NoisyLinear modules in the model.
- Parameters
model – a PyTorch module which may have NoisyLinear submodules.
- Returns
True if model has at least one NoisyLinear submodule; otherwise, False.
-
class
tianshou.utils.net.discrete.
IntrinsicCuriosityModule
(feature_net: torch.nn.modules.module.Module, feature_dim: int, action_dim: int, hidden_sizes: Sequence[int] = (), device: Union[str, torch.device] = 'cpu')[source]¶ Bases:
torch.nn.modules.module.Module
Implementation of Intrinsic Curiosity Module. arXiv:1705.05363.
- Parameters
feature_net (torch.nn.Module) – a self-defined feature_net which output a flattened hidden state.
feature_dim (int) – input dimension of the feature net.
action_dim (int) – dimension of the action space.
hidden_sizes – hidden layer sizes for forward and inverse models.
device – device for the module.
-
forward
(s1: Union[numpy.ndarray, torch.Tensor], act: Union[numpy.ndarray, torch.Tensor], s2: Union[numpy.ndarray, torch.Tensor], **kwargs: Any) → Tuple[torch.Tensor, torch.Tensor][source]¶ Mapping: s1, act, s2 -> mse_loss, act_hat.
-
training
: bool¶
Continuous¶
-
class
tianshou.utils.net.continuous.
Actor
(preprocess_net: torch.nn.modules.module.Module, action_shape: Sequence[int], hidden_sizes: Sequence[int] = (), max_action: float = 1.0, device: Union[str, int, torch.device] = 'cpu', preprocess_net_output_dim: Optional[int] = None)[source]¶ Bases:
torch.nn.modules.module.Module
Simple actor network. Will create an actor operated in continuous action space with structure of preprocess_net —> action_shape.
- Parameters
preprocess_net – a self-defined preprocess_net which output a flattened hidden state.
action_shape – a sequence of int for the shape of action.
hidden_sizes – a sequence of int for constructing the MLP after preprocess_net. Default to empty sequence (where the MLP now contains only a single linear layer).
max_action (float) – the scale for the final action logits. Default to 1.
preprocess_net_output_dim (int) – the output dimension of preprocess_net.
For advanced usage (how to customize the network), please refer to Build the Network.
See also
Please refer to
Net
as an instance of how preprocess_net is suggested to be defined.-
forward
(obs: Union[numpy.ndarray, torch.Tensor], state: Optional[Any] = None, info: Dict[str, Any] = {}) → Tuple[torch.Tensor, Any][source]¶ Mapping: obs -> logits -> action.
-
training
: bool¶
-
class
tianshou.utils.net.continuous.
Critic
(preprocess_net: torch.nn.modules.module.Module, hidden_sizes: Sequence[int] = (), device: Union[str, int, torch.device] = 'cpu', preprocess_net_output_dim: Optional[int] = None, linear_layer: Type[torch.nn.modules.linear.Linear] = <class 'torch.nn.modules.linear.Linear'>, flatten_input: bool = True)[source]¶ Bases:
torch.nn.modules.module.Module
Simple critic network. Will create an actor operated in continuous action space with structure of preprocess_net —> 1(q value).
- Parameters
preprocess_net – a self-defined preprocess_net which output a flattened hidden state.
hidden_sizes – a sequence of int for constructing the MLP after preprocess_net. Default to empty sequence (where the MLP now contains only a single linear layer).
preprocess_net_output_dim (int) – the output dimension of preprocess_net.
linear_layer – use this module as linear layer. Default to nn.Linear.
flatten_input (bool) – whether to flatten input data for the last layer. Default to True.
For advanced usage (how to customize the network), please refer to Build the Network.
See also
Please refer to
Net
as an instance of how preprocess_net is suggested to be defined.-
forward
(obs: Union[numpy.ndarray, torch.Tensor], act: Optional[Union[numpy.ndarray, torch.Tensor]] = None, info: Dict[str, Any] = {}) → torch.Tensor[source]¶ Mapping: (s, a) -> logits -> Q(s, a).
-
training
: bool¶
-
class
tianshou.utils.net.continuous.
ActorProb
(preprocess_net: torch.nn.modules.module.Module, action_shape: Sequence[int], hidden_sizes: Sequence[int] = (), max_action: float = 1.0, device: Union[str, int, torch.device] = 'cpu', unbounded: bool = False, conditioned_sigma: bool = False, preprocess_net_output_dim: Optional[int] = None)[source]¶ Bases:
torch.nn.modules.module.Module
Simple actor network (output with a Gauss distribution).
- Parameters
preprocess_net – a self-defined preprocess_net which output a flattened hidden state.
action_shape – a sequence of int for the shape of action.
hidden_sizes – a sequence of int for constructing the MLP after preprocess_net. Default to empty sequence (where the MLP now contains only a single linear layer).
max_action (float) – the scale for the final action logits. Default to 1.
unbounded (bool) – whether to apply tanh activation on final logits. Default to False.
conditioned_sigma (bool) – True when sigma is calculated from the input, False when sigma is an independent parameter. Default to False.
preprocess_net_output_dim (int) – the output dimension of preprocess_net.
For advanced usage (how to customize the network), please refer to Build the Network.
See also
Please refer to
Net
as an instance of how preprocess_net is suggested to be defined.-
forward
(obs: Union[numpy.ndarray, torch.Tensor], state: Optional[Any] = None, info: Dict[str, Any] = {}) → Tuple[Tuple[torch.Tensor, torch.Tensor], Any][source]¶ Mapping: obs -> logits -> (mu, sigma).
-
training
: bool¶
-
class
tianshou.utils.net.continuous.
RecurrentActorProb
(layer_num: int, state_shape: Sequence[int], action_shape: Sequence[int], hidden_layer_size: int = 128, max_action: float = 1.0, device: Union[str, int, torch.device] = 'cpu', unbounded: bool = False, conditioned_sigma: bool = False)[source]¶ Bases:
torch.nn.modules.module.Module
Recurrent version of ActorProb.
For advanced usage (how to customize the network), please refer to Build the Network.
-
forward
(obs: Union[numpy.ndarray, torch.Tensor], state: Optional[Dict[str, torch.Tensor]] = None, info: Dict[str, Any] = {}) → Tuple[Tuple[torch.Tensor, torch.Tensor], Dict[str, torch.Tensor]][source]¶ Almost the same as
Recurrent
.
-
training
: bool¶
-
-
class
tianshou.utils.net.continuous.
RecurrentCritic
(layer_num: int, state_shape: Sequence[int], action_shape: Sequence[int] = [0], device: Union[str, int, torch.device] = 'cpu', hidden_layer_size: int = 128)[source]¶ Bases:
torch.nn.modules.module.Module
Recurrent version of Critic.
For advanced usage (how to customize the network), please refer to Build the Network.
-
forward
(obs: Union[numpy.ndarray, torch.Tensor], act: Optional[Union[numpy.ndarray, torch.Tensor]] = None, info: Dict[str, Any] = {}) → torch.Tensor[source]¶ Almost the same as
Recurrent
.
-
training
: bool¶
-
-
class
tianshou.utils.net.continuous.
Perturbation
(preprocess_net: torch.nn.modules.module.Module, max_action: float, device: Union[str, int, torch.device] = 'cpu', phi: float = 0.05)[source]¶ Bases:
torch.nn.modules.module.Module
Implementation of perturbation network in BCQ algorithm. Given a state and action, it can generate perturbed action.
- Parameters
preprocess_net (torch.nn.Module) – a self-defined preprocess_net which output a flattened hidden state.
max_action (float) – the maximum value of each dimension of action.
int, torch.device] device (Union[str,) – which device to create this model on. Default to cpu.
phi (float) – max perturbation parameter for BCQ. Default to 0.05.
For advanced usage (how to customize the network), please refer to Build the Network.
See also
You can refer to examples/offline/offline_bcq.py to see how to use it.
-
forward
(state: torch.Tensor, action: torch.Tensor) → torch.Tensor[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
training
: bool¶
-
class
tianshou.utils.net.continuous.
VAE
(encoder: torch.nn.modules.module.Module, decoder: torch.nn.modules.module.Module, hidden_dim: int, latent_dim: int, max_action: float, device: Union[str, torch.device] = 'cpu')[source]¶ Bases:
torch.nn.modules.module.Module
Implementation of VAE. It models the distribution of action. Given a state, it can generate actions similar to those in batch. It is used in BCQ algorithm.
- Parameters
encoder (torch.nn.Module) – the encoder in VAE. Its input_dim must be state_dim + action_dim, and output_dim must be hidden_dim.
decoder (torch.nn.Module) – the decoder in VAE. Its input_dim must be state_dim + latent_dim, and output_dim must be action_dim.
hidden_dim (int) – the size of the last linear-layer in encoder.
latent_dim (int) – the size of latent layer.
max_action (float) – the maximum value of each dimension of action.
torch.device] device (Union[str,) – which device to create this model on. Default to “cpu”.
For advanced usage (how to customize the network), please refer to Build the Network.
See also
You can refer to examples/offline/offline_bcq.py to see how to use it.
-
forward
(state: torch.Tensor, action: torch.Tensor) → Tuple[torch.Tensor, torch.Tensor, torch.Tensor][source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
training
: bool¶