tianshou.utils

class tianshou.utils.MovAvg(size: int = 100)

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

add(x: Union[numbers.Number, numpy.number, list, numpy.ndarray, torch.Tensor]) → numpy.number

Add a scalar into MovAvg.

You can add torch.Tensor with only one element, a python scalar, or a list of python scalar.

get() → numpy.number

Get the average.

mean() → numpy.number

Get the average. Same as get().

std() → numpy.number

Get the standard deviation.

class tianshou.utils.BaseLogger(writer: Any)

Bases: abc.ABC

The base class for any logger which is compatible with trainer.

abstract write(key: str, x: Union[numbers.Number, numpy.number, numpy.ndarray], y: Union[numbers.Number, numpy.number, numpy.ndarray], **kwargs: Any) → None

Specify how the writer is used to log data.

Parameters

• key – namespace which the input data tuple belongs to.

• x – stands for the ordinate of the input data tuple.

• y – stands for the abscissa of the input data tuple.

log_train_data(collect_result: dict, step: int) → None

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_update_data(update_result: dict, step: int) → None

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.

log_test_data(collect_result: dict, step: int) → None

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.

class tianshou.utils.BasicLogger(writer: torch.utils.tensorboard.writer.SummaryWriter, train_interval: int = 1, test_interval: int = 1, update_interval: int = 1000)

Bases: tianshou.utils.log_tools.BaseLogger

A loggger that relies on tensorboard SummaryWriter by default to visualize and log statistics.

You can also rewrite write() func to use your own writer.

Parameters

• writer (SummaryWriter) – the writer to log data.

• train_interval (int) – the log interval in log_train_data(). Default to 1.

• 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.

write(key: str, x: Union[numbers.Number, numpy.number, numpy.ndarray], y: Union[numbers.Number, numpy.number, numpy.ndarray], **kwargs: Any) → None

Specify how the writer is used to log data.

Parameters

• key – namespace which the input data tuple belongs to.

• x – stands for the ordinate of the input data tuple.

• y – stands for the abscissa of the input data tuple.

log_train_data(collect_result: dict, step: int) → None

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.

Note

collect_result will be modified in-place with “rew” and “len” keys.

log_test_data(collect_result: dict, step: int) → None

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.

Note

collect_result will be modified in-place with “rew”, “rew_std”, “len”, and “len_std” keys.

log_update_data(update_result: dict, step: int) → None

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.

class tianshou.utils.LazyLogger

Bases: tianshou.utils.log_tools.BasicLogger

A loggger that does nothing. Used as the placeholder in trainer.

write(key: str, x: Union[numbers.Number, numpy.number, numpy.ndarray], y: Union[numbers.Number, numpy.number, numpy.ndarray], **kwargs: Any) → None

The LazyLogger writes nothing.

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) → List[torch.nn.modules.module.Module]

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)

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 incluing input_dim and output_dim.

• norm_layer – use which normalization before activation, e.g., nn.LayerNorm and nn.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 actvition for all layers if passed in nn.Module, or different activation for different Modules if passed in a list. Default to nn.ReLU.

forward(x: Union[numpy.ndarray, torch.Tensor]) → torch.Tensor

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: Optional[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)

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 and nn.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 actvition 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.

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(s: Union[numpy.ndarray, torch.Tensor], state: Optional[Any] = None, info: Dict[str, Any] = {}) → Tuple[torch.Tensor, Any]

Mapping: s -> 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)

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(s: Union[numpy.ndarray, torch.Tensor], state: Optional[Dict[str, torch.Tensor]] = None, info: Dict[str, Any] = {}) → Tuple[torch.Tensor, Dict[str, torch.Tensor]]

Mapping: s -> flatten -> logits.

In the evaluation mode, s should be with shape [bsz, dim]; in the training mode, s should be with shape [bsz, len, dim]. See the code and comment for more detail.

training: bool

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')

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(s: Union[numpy.ndarray, torch.Tensor], state: Optional[Any] = None, info: Dict[str, Any] = {}) → Tuple[torch.Tensor, Any]

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')

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(s: Union[numpy.ndarray, torch.Tensor], **kwargs: Any) → torch.Tensor

Mapping: s -> V(s).

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)

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(s: Union[numpy.ndarray, torch.Tensor], state: Optional[Any] = None, info: Dict[str, Any] = {}) → Tuple[torch.Tensor, Any]

Mapping: s -> 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)

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.

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(s: Union[numpy.ndarray, torch.Tensor], a: Optional[Union[numpy.ndarray, torch.Tensor]] = None, info: Dict[str, Any] = {}) → torch.Tensor

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)

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(s: Union[numpy.ndarray, torch.Tensor], state: Optional[Any] = None, info: Dict[str, Any] = {}) → Tuple[Tuple[torch.Tensor, torch.Tensor], Any]

Mapping: s -> 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)

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(s: 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]]

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)

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(s: Union[numpy.ndarray, torch.Tensor], a: Optional[Union[numpy.ndarray, torch.Tensor]] = None, info: Dict[str, Any] = {}) → torch.Tensor

Almost the same as Recurrent.

training: bool