DdpgMlpQNet

class torchrl.modules.DdpgMlpQNet(mlp_net_kwargs_net1: Optional[dict] = None, mlp_net_kwargs_net2: Optional[dict] = None, device: Optional[Union[device, str, int]] = None)

DDPG Q-value MLP class.

Presented in “CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING”, https://arxiv.org/pdf/1509.02971.pdf

The DDPG Q-value network takes as input an observation and an action, and returns a scalar from it. Because actions are integrated later than observations, two networks are created.

Parameters:

• mlp_net_kwargs_net1 (dict, optional) –

  kwargs for MLP. Defaults to

  >>> {
  ...     'in_features': None,
  ...     'out_features': 400,
  ...     'depth': 0,
  ...     'num_cells': [],
  ...     'activation_class': nn.ELU,
  ...     'bias_last_layer': True,
  ...     'activate_last_layer': True,
  ...     }
  

• mlp_net_kwargs_net2 –

  Defaults to

  >>> {
  ...     'in_features': None,
  ...     'out_features': 1,
  ...     'depth': 1,
  ...     'num_cells': [300, ],
  ...     'activation_class': nn.ELU,
  ...     'bias_last_layer': True,
  ... }
  

• device (torch.device, optional) – device to create the module on.

Examples

>>> import torch
>>> from torchrl.modules import DdpgMlpQNet
>>> net = DdpgMlpQNet()
>>> print(net)
DdpgMlpQNet(
  (mlp1): MLP(
    (0): LazyLinear(in_features=0, out_features=400, bias=True)
    (1): ELU(alpha=1.0)
  )
  (mlp2): MLP(
    (0): LazyLinear(in_features=0, out_features=300, bias=True)
    (1): ELU(alpha=1.0)
    (2): Linear(in_features=300, out_features=1, bias=True)
  )
)
>>> obs = torch.zeros(1, 32)
>>> action = torch.zeros(1, 4)
>>> value = net(obs, action)
>>> print(value.shape)
torch.Size([1, 1])

forward(observation: Tensor, action: Tensor) → Tensor

Define 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.